The Internet of Things Empowering the Internet of Pets—An Outlook from the Academic and Scientific Experience

Abstract

This paper presents a systematic review to explore how the Internet of Things (IoT) is empowering the Internet of Pets (IoP) to enhance the quality of life for companion animals. Thirty-six relevant papers published between 2010 and 2024 were retrieved and analyzed following both the PRISMA and the Kitchenham and Charters guidelines for conducting literature reviews. The findings demonstrate that the IoP is transforming pet care by offering innovative solutions for monitoring, feeding, and animal welfare. Asian countries are leading the development of these technologies, with a surge in research activity in recent years (2020–2024). While remote feeding prototypes currently dominate the field (79%), the IoP is anticipated to expand into other areas. Monitoring health (25%), surveillance and monitoring activities (49%), and providing comfort (17%) for pets are the primary research interests. The IoT holds immense potential to improve pet care. Research in this area is expected to continue growing, driving innovation and the creation of new IoP solutions utilizing artificial intelligence to achieve smart and predictive devices. In the future, the development of multifunctional devices that combine various capabilities in a single unit will become commonplace in a society where it is trending for young people to adopt pets instead of having children.

1. Introduction

The Internet of Things (IoT) is an emerging paradigm that proposes the creation of dynamic networks of devices that can collect and exchange data with each other, anywhere, anytime, and from any network that is accessible [1]. This communication is aimed at carrying out actions that allow for monitoring and controlling the real world remotely, where people and other living beings cohabit [2].

The applications of the IoT in real contexts are varied, and this has led to an exponential increase in the number of interconnected devices in recent years [3]. The heterogeneity of sensors, actuators, and other types of IoT-compatible devices [4] has been decisive in providing innovative solutions that have helped to solve problems in today’s hyper-connected scenarios, such as industry, agriculture, cities, homes, hospitals, among others [5].

In the case of smart homes, the IoT has focused mainly on the continuous monitoring of indoor and outdoor environmental conditions, security monitoring, automatic access control, comfort management, the control of household appliances, and the optimization of energy resources, leading to the reduced consumption of basic services such as water and electricity [6]. However, the IoT can also be applied to manage the care and entertainment of people living in a smart home. Thus, the IoT seeks to provide the welfare and quality of life of those who inhabit such scenarios.

Complementary to the care of people living in a smart home through IoT technologies, this paradigm can also be beneficial to provide welfare to other living beings that inhabit modern homes. For plants, prototypes of smart pots have been proposed that can water the plants to maintain an optimal soil moisture level [7]. Similarly, for domestic animals or pets, various prototypes have been proposed, many of which aim to automatically provide food to pets left alone at home [8,9].

The scope of the term “pet” varies depending on the perspective from which it is analyzed. Generally, people consider traditional companion animals (e.g., dogs, cats, and birds) as pets, while others may adopt fewer common animals (e.g., pigs and goats) or even wild and potentially dangerous animals (e.g., scorpions, snakes, spiders, primates, big cats, and caimans). Therefore, an analysis of the concept is proposed to clarify its definition in this research. First, from a psychological perspective, pets are described as “the animals that live in our homes and share our lives” [10,11]. On the other hand, in anthropology, pets are seen as animals that have an owner [12]. Anthropologically, a pet is considered a “domesticated animal that maintains a symbolic and social relationship with humans, acting as a mediator between the individual and their cultural and natural environment” [13]. Thus, pets play an important role in the social and emotional structure of modern life [14]. Finally, zoology—which studies the anatomy, physiology, behavior, ecology, evolution, and classification of animals—shares a similar view with other fields regarding pets, but it examines pets’ interactions with humans as well as their relationships with their environment and other species.

Currently, there is no universal classification for categorizing animals as pets. Each region or country may establish its own regulations to define and group these animals. For example, Canada [15], Georgia [16], Byelorussia [17], the United Stated of America [18], and the European Union [11] define which animals can be considered pets in households and, in some cases, regulate the number and types of pets that can be kept, such as in the case of Canada. Such regulations aim to protect people from animals that may pose potential dangers and to shield pets from cruel treatment by humans [17]. Generally, pets are animals that live with and are cared for by an owner in a residential area, and they are widely recognized as companion animals. Consequently, animals that live in natural freedom cannot be classified as domestic pets. Similarly, animals kept in semi-wild conditions or artificially created habitats are not considered pets, nor are wild or exotic animals kept in captivity [17,18].

According to the European Convention for the Protection of Pet Animals [11], a classification of animals within the pet category includes the following four main groups:

• Domestic ungulates and livestock animals (cattle, sheep, goats, and pigs);
• Poultry and small domestic animals (chickens, ducks, geese, pigeons, rabbits, and hamsters);
• Domestic dogs and cats;
• Other mammals and birds (such as small birds and parrots).

Within the spectrum of pets that live in households, some species in these categories fall outside the scope of traditional pets considered in this study. The category of “domestic dogs and cats” is fully recognized as household pets due to their global popularity and widespread adoption by families.

However, in the case of the “domestic ungulates and livestock animals” category, certain birds included in this group are considered pets that usually live outside the house. As a result, their care differs significantly from that of “domestic dogs and cats”, and the care of these birds is more commonly associated with the poultry industry. Within this same category, rabbits and hamsters are small pets that can be kept indoors. They typically live in cages and interact with their owners during specific periods. These animals as well as those categorized as “other mammals and birds” are included as pets for the purposes of this study because they are more commonly adopted in households in various countries.

Similarly, in the “domestic ungulates and livestock animals” category, livestock species require care that differs from traditional pets. Although they can occasionally be kept at home, they are generally housed outdoors. Baby animals may be temporarily kept indoors if they require special care or if the owners become attached to them. However, as these animals mature, it is more common for them to live outdoors, preferably on farms, due to their need for large spaces, their potential to transmit diseases, and their instinct-driven behaviors, which may include aggression. Livestock care studies have already been proposed, as discussed in [19].

Consequently, these animals have not been a primary focus of this study, as their care falls under livestock management. While some individuals may adopt them as pets, it is ideal for them to have appropriate spaces to live, similar to wild animals, which are also sometimes kept as pets despite pet ownership regulations. For wild animals that cannot live in a natural ecosystem, it is preferable for them to be cared for in zoos, where conditions can replicate their natural habitat as closely as possible.

In today’s modern society, pets have become an integral part of many households. Numerous families now have one or more pets, and, increasingly, young people are opting for pets as companions rather than having biological children [20]. Several factors contribute to this social shift, including the high cost of living in certain countries, changing priorities among younger generations, the rise of single-person households, the normalization of same-sex couples in some countries, where adoption may not be possible, or simply a conscious decision by young people to forgo the traditional family structure—father, mother, and children—imposed by society. Regardless of the reason, pets are increasingly viewed as essential members of both “traditional” and non-traditional families, often being cared for and nurtured as though they were children [21].

Due to the importance that pets currently have in the context of the home and family, it is key to provide them with care, monitoring, and protection actions so that they feel comfortable, healthy, and happy. To this end, technologies associated with the IoT can make a significant contribution. In this sense, a new approach has emerged, and it has been called the Internet of Pets or Pets 2.0. This paradigm, related to the IoT, proposes the use of sensors and smart objects to support pet care management. Some cases of the use of these devices are cameras used for monitoring the activities carried out by pets [22]. Another common example is the use of Global Positioning System (GPS) integrated in collars that aim to geolocate pets in case of loss in the street [23]. Finally, another relevant case of the use of the Internet of Pets proposes the creation of water and food dispensers to keep pets hydrated and well fed when their owners are at home or away [24,25].

For the above reasons, this paper proposes to carry out a systematic review of the literature to provide an updated view of the state of the art regarding the influence of IoT technologies in the care of pets in smart homes. The study seeks to answer the following research question: what is the contribution of the IoT in practical terms in the care, protection, and welfare of pets in the context of the home? This will determine the current trends and prospects of the IoT to support families in the care of their pets, whether they live indoors or in the outdoor yards of their homes, or in similar scenarios.

The systematic literature review followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [26] as well as the methodology proposed by Kitchenham and Charters [27]. A scientific search protocol was developed based on this methodology and applied to databases, including Scopus, Web of Science, Scielo, Dialnet, Directory of Open Access Journals (DOAJ), ProQuest, Google Scholar, and general Google searches. The application of the research protocol made it possible to select a total of 81 scientific studies of the journal article and conference article types. From the exhaustive analysis of these selected studies, the five research questions formulated in this study were answered. These five questions are related to the research question posed above and to be answered in this study.

The results underscore the impact of the Internet of Things for Pets (IoP) on animal welfare, highlighting its role in developing technological solutions for monitoring and caring for pets, particularly in Asian countries. These solutions focus on key aspects such as feeding, health, and safety, enhancing the bond between owners and pets. While most IoP devices are designed for home use, some cater to other settings like veterinary clinics or pet transportation. With the growing number of pet-owning households, the IoP has become a relevant tool, offering not only automatic care and remote monitoring but also emotional support for pets left alone. Through IoT devices, the IoP helps to alleviate loneliness and maintain the human–animal connection, ensuring that pets feel accompanied.

This paper is organized in four sections. Section 2 describes the methodology used to conduct the systematic literature review. Section 3 presents the key findings of the review, including current trends, prospects, and practical contributions of the IoT in pet care. In Section 4 the implications of these findings are discussed. Finally, Section 5 presents the main conclusions and discusses future research lines.

2. Systematic Review

A systematic literature review is the cornerstone of sound academic research, which is the critical examination of existing knowledge. This process acts as a roadmap guiding researchers through the established landscape of a field and identifying uncharted territory. Through a deep analysis of relevant studies, researchers gain an in-depth understanding of current knowledge and pinpoint areas that require further investigation and attention. This analysis allows us not only to develop new theories, but also to critically evaluate the quality of existing research, uncovering the weaknesses and inconsistencies of specific topics [28]

Systematic reviews employ rigorous methodologies to minimize bias during the selection, publication, and data extraction stages [29]. In the case of this study, PRISMA [26] and the methodology proposed by Kitchenham, B and Charters [27] were applied.

PRISMA, designed by Page et al., was created to plan and conduct systematic reviews and studies evaluating the effects of health-related interventions [26]. However, its checklist items are also applicable to systematic reviews in other fields. Planning a systematic review using the PRISMA methodology ensures that all relevant information is captured. It is important to note that PRISMA is not intended to guide systematic reviews but to support their planning. In this regard, the proposed study is complemented by the methodology of Kitchenham and Charters [27], which provides guidance for both the review process and its documentation.

The methodology by Kitchenham and Charters serves as a guideline for conducting rigorous reviews of current empirical evidence within the software engineering community [27]; however, it can also be extrapolated to other engineering domains. According to these researchers, a successful literature review is composed of three main stages. In the first stage, the planning stage, researchers identify the need to conduct the review, formulate the research questions, and develop a protocol that will guide the process. The second stage, the execution stage, focuses on finding and selecting relevant primary studies, extracting their data, analyzing them, and synthesizing the information obtained. Finally, in the report stage, the researchers write a document to communicate the findings of the review [27]. These stages and the steps to be followed have been synthesized by Xiao and Watson [28], and are illustrated in Figure 1.

2.1. Stage 1—Planning the Review

2.1.1. Step 1. Formulate the Problem

This literature review proposes to answer five research questions (RQs). These questions contemplate studying how the IoT has been used to create prototypes that support the care of pets in homes. In addition, the questions were asked so to allow us to determine how technologies have changed the pet–owner relationship. The following is a description of the research questions formulated:

• RQ1. What are the types of IoT devices/prototypes most used for pet care and what functionalities do they offer?
• RQ2. How has Internet of Things (IoT) technology evolved in the field of pet care from its inception to the present day?
• RQ3. How has the IoT impacted pet wellness?
• RQ4. How has the relationship between owners and their pets changed with the implementation of the IoT?
• RQ5. What are the emerging trends in the IoT for pets and what impact might they have in the future?

2.1.2. Step 2. Develop and Validate the Review Protocol

The research protocol relied on the following three key elements: a search strategy, inclusion criteria, and exclusion criteria. The search strategy outlined the defined search string and the databases used to identify relevant information for the study. The inclusion criteria specified the characteristic studies needed for consideration, while the exclusion criteria defined the studies that would not be included. These elements together ensured the replicability of the study, and guaranteed that the retrieved studies directly addressed the research question (RQ) and meaningfully contributed to its answer.

The search string formulated in this study consisted of three terms and is described as follows: ((“internet of things” OR “iot”) AND (“pet”)). This search string was applied in seven sources of information, e.g., documentary databases that index impact studies published in quality scientific media, such as Web of Science (WoS), Scopus, Scielo, Dialnet, DOAJ, and ProQuest. In addition, a supplementary search strategy was applied using Google Scholar and general Google search terms to identify additional relevant studies.

Inclusion and exclusion criteria were established to focus the analysis. The inclusion criteria consisted of the following four points: studies published and indexed in scientific platforms, those proposing prototypes or practical IoT systems for pet care, studies that are accessible, and publications from 2010 to 2024. This research focuses on analyzing practical proposals, specifically real-world IoT implementations designed to automate pet care actions. Furthermore, considering the global presence of pets, studies in any language were included, provided they met the inclusion criteria. Studies written in other languages different to English were also analyzed. We analyzed papers written in Korean and Spanish.

As part of the scientific search protocol, the following three exclusion criteria were applied: studies not published in indexed journals or conference proceedings, studies outside the defined time frame, and studies that did not propose practical applications. These criteria excluded documents such as tutorials and theses. As a result, the analyzed information ensured that the innovations reviewed applied the IoT and were validated by the scientific community, indicating a certain level of academic impact. Therefore, studies that did not present prototypes or devices designed for the care of non-pet animals fell outside the scope of this research.

2.2. Stage 2—Conducting the Review

2.2.1. Steps 3–5. Search the Literature, Screen for Inclusion, and Assess Quality

The application of the search string in the information sources, in the time under study, allowed for the retrieval of 206 studies (Figure 2). The selection of studies was made by analyzing the titles of the studies retrieved. Thus, out of 206 studies, 39 duplicate studies were eliminated. Of these 167 studies, 45 more studies that met the exclusion criteria were discarded. Finally, the studies selected based on the title of the studies included 122 articles. Of the 122 studies previously discussed, after reading the abstract and the full text of the studies, 41 more studies were discarded. Thus, the literature review focused on the analysis of 81 studies.

2.2.2. Step 6. Extract Data

The systematic literature review process captured the following metadata for each selected study: identifier, publication year, country, study type, source, and full bibliographic reference.

Table 1. Studies analyzed in the literature review.

ID	Year	Country	Type of Paper	Source	Reference
S1	2024	Mexico	Conference	Scopus	[30]
S2	2023	India	Conference	Scopus	[31]
S3	2022	India	Conference	Scopus	[32]
S4	2022	India	Conference	Scopus	[33]
S5	2022	South Korea	Conference	Scopus	[34]
S6	2022	China	Conference	Scopus	[35]
S7	2022	India	Book chapter	Scopus	[36]
S8	2021	India	Conference	Scopus	[37]
S9	2021	India	Conference	Scopus	[24]
S10	2021	Mexico	Journal	Scopus	[38]
S11	2021	India	Report	Scholar	[39]
S12	2020	Canada	Conference	Scopus	[9]
S13	2020	Thailand	Conference	Scopus	[25]
S14	2020	China	Conference	Scopus	[40]
S15	2021	Canada	Journal	Scholar	[41]
S16	2019	Philippines	Conference	Scopus	[22]
S17	2018	Egypt	Conference	Scopus	[23]
S18	2016	South Korea	Conference	Scopus	[42]
S19	2016	Taiwan	Conference	Scopus	[43]
S20	2016	South Korea	Journal	Scopus	[44]
S21	2011	Taiwan	Conference	Scopus	[45]
S22	2018	China	Conference	Web of Science	[21]
S23	2019	China	Conference	Google Scholar	[46]
S24	2023	India	Journal	Google Scholar	[47]
S25	2023	Indonesia	Journal	Google Scholar	[48]
S26	2024	Japan	Conference	Google Scholar	[49]
S27	2022	Sri Lanka	Conference	Google Scholar	[50]
S28	2022	India	Journal	Google Scholar	[8]
S29	2023	Mexico	Journal	Scielo	[51]
S30	2021	Ecuador	Journal	Dialnet	[52]
S31	2018	Turkey	Journal	DOAJ	[53]
S32	2022	Malaysia	Journal	DOAJ	[54]
S33	2018	Korea	Journal	DOAJ	[55]
S34	2023	Sri Lanka	Journal	ProQuest	[56]
S35	2024	Mexico	Journal	ProQuest	[57]
S36	2021	Malaysia	Journal	ProQuest	[58]
S37	2023	India	Conference	Google	[59]
S38	2022	India	Conference	Google	[60]
S39	2022	Malaysia	Journal	Google	[61]
S40	2022	Malaysia	Journal	Google	[62]
S41	2024	Malaysia	Journal	Google	[63]
S42	2023	India	Conference	Google	[64]
S43	2021	India	Conference	Google	[65]
S44	2024	India	Conference	Google	[66]
S45	2021	South Korea	Journal	Google	[67]
S46	2020	United Kingdom	Conference	Google	[68]
S47	2022	Ghana	Conference	Google	[69]
S48	2018	Taiwan	Conference	Google	[70]
S49	2023	Indonesia	Conference	Google	[71]
S50	2021	Indonesia	Conference	Google	[72]
S51	2024	Indonesia	Report	Google	[73]
S52	2021	India	Conference	Google	[74]
S53	2021	Australia	Conference	Google	[75]
S54	2020	South Korea	Conference	Google	[76]
S55	2023	India	Conference	Google	[77]
S56	2019	Indonesia	Conference	Google	[78]
S57	2022	Indonesia	Conference	Google	[79]
S58	2023	Malaysia	Conference	Google	[80]
S59	2021	Taiwan	Journal	Google	[81]
S60	2024	India	Conference	Google	[82]
S61	2024	Thailand	Conference	Google	[83]
S62	2024	Malaysia	Conference	Google	[84]
S63	2024	Malaysia	Conference	Google	[85]
S64	2022	India	Journal	Google	[86]
S65	2021	India	Journal	Google	[87]
S66	2023	India	Journal	Google	[88]
S67	2022	India	Journal	Google	[89]
S68	2016	Indonesia	Conference	Google	[90]
S69	2022	India	Journal	Google	[91]
S70	2022	Turkey	Journal	Google	[92]
S71	2022	Nigeria	Journal	Google	[93]
S72	2023	Indonesia	Journal	Google	[94]
S73	2022	India	Journal	Google	[95]
S74	2024	Indonesia	Journal	Google	[96]
S75	2020	Colombia	Journal	Google	[97]
S76	2018	India	Conference	Google	[98]
S77	2024	Mexico	Journal	Google	[99]
S78	2022	Malaysia	Journal	Google	[100]
S79	2020	India	Conference	Google	[101]
S80	2024	Malaysia	Conference	Google	[102]
S81	2021	Indonesia	Conference	Google	[103]

lists the 81 selected studies. Peer-reviewed articles from scientific journals and conference proceedings were included. Additionally, book chapters and technical reports were also considered.

2.2.3. Step 7. Analyze and Synthesize Data

According to the organization Health for Animals [104], the global pet population is steadily increasing. Following the COVID-19 pandemic, many people decided to adopt pets into their households. This trend is further supported by a cultural shift in pet ownership in regions such as South Asia, Central Asia, Sub-Saharan Africa, and Eastern Europe. By 2022, statistics from the same organization revealed that the European Union had 205 million pets, primarily dogs and cats. Similarly, China reported 141 million pets, and the United States had at least 150 million. Other notable countries include Brazil (78.1 million), Thailand (12.2 million), Mexico (46 million), Canada (15.8 million), Japan (18.1 million), and India (20 million).

The aforementioned data illustrate a global trend in pet ownership, as corroborated by a 2016 consultancy study conducted by Global HQ, which reported that more than half of the world’s population owned a pet [105]. A more specific 2016 study by market research firm GDK identified Argentina as the Latin American country with the highest percentage of pet ownership, with 66% of households owning a dog, followed by Mexico (64%) and Brazil (58%) [106]. In the United States, 50% of households own a dog, while, in Canada, the figure stands at 33%. The same report states that, in Asia, the proportion of households with pet dogs is 25% in China, 20% in South Korea, and 17% in Japan. In Europe, data from the 2022 European Pet Food Industry Federation (FEDIAF) survey reveal that the countries with the highest dog ownership rates are Hungary (50%), Poland (49%), Romania (45%), Slovenia and Portugal (39%), Lithuania (34%), the United Kingdom (33%), Belgium (30%), Croatia and Slovakia (29%), and Italy (28%), while the remaining European countries report dog ownership rates below 28%, with Turkey having the lowest at 5% [107]. The data described refer specifically to dog ownership in households, as dogs are the most common pet worldwide, yet the surveys also collected information on cats, which rank as the second most popular pet globally.

Millennial couples represent a growing demographic and frequently adopt pets as a means of preparing for parenthood before making the decision to have children [104]. Three out of four Millennials own either a dog or a cat, and consider their pets as the first step in forming a family, thereby facilitating their transition into parenthood [108]. As a technology-driven generation, Millennials are driving significant growth in pet technology (Pet Tech). According to a 2020 publication on the All Pet Food portal, 56% of Millennials reported purchasing a technological device to monitor and observe their pets when they are not at home, with IoT devices likely included within this percentage [109]. To date, there are no precise data on the investment made by pet owners in IoT devices for pet care support; however, in terms of technological devices, Millennials in the United States have spent money on their pets in the last month, as indicated in the report “Gen Z and Millennials as Pet Market Consumers: Dogs, Cats” [110]. According to Networking King, the global pet technology market was valued at USD 5 billion in 2022 and is projected to grow at a 15% annual rate until 2032, surpassing USD 35 billion [111].

The previously reported pet ownership figures, especially in globally representative countries, also correlate with aging populations. Many older individuals seek companionship through pets. According to the World Bank Group [112] in 2023, China, India, and the United States ranked as the top three countries with the largest aging populations, followed by Japan. Europe, as a continent, also exhibits high levels of aging populations, with countries like Russia, Germany, France, Italy, the United Kingdom, and Spain standing out.

The growing interest in pet ownership and the aging population highlights the motivation of people in these countries to prioritize their pets’ well-being. This trend has inspired researchers in several nations to explore ways to improve pet care through technology, particularly IoT applications. The findings of this study indicate that Asian countries demonstrate the most interest in developing IoT-based solutions for pet care. India, Indonesia, South Korea, Malaysia, Taiwan, and China stand out, collectively contributing 50 research articles and proposals. Additionally, other countries in the region, such as Egypt, the Philippines, Thailand, Japan, Sri Lanka, and Turkey, have also shown interest, adding nine more studies. This brings the total to 59 published studies from Asia (see Figure 3). The remaining studies have been conducted in the Americas and, to a lesser extent, in Africa and Oceania.

The opportunity for research on IoT-mediated pet care is closely tied to the growing pet care market, which was valued at USD 304.4 billion in 2023 and is projected to grow at a rate of 6.8% between 2024 and 2032 [113]. Furthermore, pet owners are allocating larger budgets for their pets’ needs and demanding higher-quality products to keep their pets healthy, happy, and well cared for [114]. This has driven the development of automated solutions to support pet care, particularly for working owners who are away from home for long periods. Pets, especially dogs, are not self-sufficient and rely on consistent access to food and water. This growing demand has also created opportunities for companies specializing in home automation solutions for pet care, such as Petsafe, Race, and Dynotag. These companies market their products primarily through online platforms, addressing the needs of pet owners worldwide.

The metadata analysis reveals a notable increase in research on IoT-based pet care solutions in recent years, with 67 studies published between 2020 and 2024 compared to only 14 studies before 2020 (see Figure 4). This trend not only reflects technological advancements but also responds to evolving social and economic factors. The Internet of Things (IoT) is becoming increasingly well-known, and the global reduction in Internet access gaps facilitates the connection of IoT devices in homes. The maturity of the IoT has expanded the range of available devices and applications, including those focused on pet care, with pets being considered an integral part of the family. In many cases, pet owners are humanizing their pets, leading to a greater need for providing them with comfort and well-being. In this context, IoT devices contribute to the care of their diet, health, safety, and emotional well-being. This explains the rise in academic proposals in recent years to address the issue of pet care in situations where there are no people at home due to work or travel. Thus, science is contributing to providing a technological solution.

2.3. Stage 3—Reporting the Review

Step 8. Report Findings

The findings of the review are reported in detail in Section 3. The systematic review findings are focused on answering each of the research questions that were previously posed. To answer these questions, tables and image collages have been used to summarize the relevant information, which allowed us to conceptualize and describe the particular details of the most relevant prototypes that have been proposed in the context of the IoT for pet care, that is, the context in which it is used, the technologies employed for the creation of the devices, the main functions, and the type of pet for which each proposal has been raised.

3. Results

A comprehensive literature review was conducted to examine the major technological trends and developments that are reshaping pet care in smart scenarios using the IoT. The findings provide definitive answers to the five research questions (RQs).

3.1. RQ1. What Are the Types of IoT Devices/Prototypes Most Used for Pet Care and What Functionalities Do They Offer?

3.1.1. IoT Devices Proposed at Academic and Research Level

The literature review revealed the existence of various prototypes focused on the care of different types of pets. Prototypes focused primarily on the care of dogs (e.g., S9, S10, S18, S19, S20, S30, S33, S34, S35, S47, S57, S68, S69, S71, and S75), cats (e.g., S12, S15, S17, S21, S25, S29, S38, S41, S49, S51, S56, S72, S78, and S81), and even both (e.g., S3, S8, S13, S16, S24, and S29). However, it was also evident that IoT prototypes for the care of fish (e.g., S1, S58, S76, and S79), chickens (e.g., S44), turtles (e.g., S26 and S53), and rodents such as hamsters, rats, and hedgehogs (e.g., S11) have been proposed. Additionally, there were prototypes focused on the care of pets in general, without specifying a target segment (e.g., S2, S4, S5, S6, S7, S11, S13, S14, S22, S23, S24, S27, S28, S29, S31, S32, S36, S37, S39, S40, S42, S43, S45, S46, S48, S50, S52, S54, S55, S59, S60, S61, S62, S63, S64, S65, S66, S67, S70, S73, S74, S77, and S80).

The prototypes developed by the authors of the analyzed studies were presented at different levels of detail. Some presented their preliminary ideas through sketches (e.g., S22), while others were limited to presenting the circuit diagram of the prototype (e.g., S3, S8, S14, S24, and S31). However, most illustrated the prototype from a real-world perspective (e.g., S1, S2, S4, S5, S7, S9, S10, S12, S13, S16, S18–S23, S25-S30, and S32–S81), showing them implemented on the actual pets used in the experiments. It is also important to note that some researchers did not present the prototype itself, but rather limited themselves to describing the software application or directly presenting the results (e.g., S6, S11, S15, and S17).

Figure 5 presents 49 IoT device prototypes designed for pet care. The majority are automated food and water dispensers for dogs (18.5%), cats (17.3%), or both (7.4%), and for general pets (53.1%). Notably, prototypes S1 and S29 are specialized for fish feeding. This prevalence of feeding devices indicates a focus on home pets, particularly cats and dogs, which are the most popular pets today. The prototypes previously illustrated in Figure 5 have the common goal of allowing owners to provide food and water to their pets. The IoT allows them to perform this action automatically when they detect the presence of the animal or, alternatively, to do it manually but remotely, i.e., from their work or wherever they are physically located. This ensures that the animal has food and does not die from malnutrition, or that the food is portioned to avoid diseases such as obesity or diabetes (e.g., S8, S9, S13, S30, S34, S49, S55, S59, S61, S72, and S78), a very common clinical condition in pets today, especially in those in which there is sedentary lifestyle due to being locked up in apartments or houses.

Proper portioning must be based on factors such as the pet’s species, breed, size, physical activity level, and veterinary-recommended diet. To ensure a healthy and adequate diet for pets, various technological proposals have been developed. However, some of these proposals do not conduct a rigorous evaluation of the precision of the portions they dispense. For instance, studies S8, S13, S34, and S78 lack comprehensive assessments of the portioning accuracy. Study S55 introduces a device designed to regulate pets’ eating habits and train them for scheduled meals, but its effectiveness remains unevaluated. Similarly, S9 only claims to provide optimal feeding without presenting detailed performance metrics.

Conversely, other studies have evaluated the effectiveness of their devices to varying degrees of complexity. Some employ basic validation methods, while others conduct more comprehensive analyses. For instance, S30 specifies that its prototype operates with an acceptable error margin of less than 5% when dispensing food portions of 200 g or more, making it suitable for medium and large pets. In scheduled mode, the water supply and replacement were successfully executed in 100% of the conducted trials. Similarly, in S59, researchers achieved a 98% accuracy in measuring food portions supplied to the pets. Study S12 reported an accuracy of 99.3%, while S5 achieved an 88.38% accuracy in food weight measurement.

Regarding aquatic pets, S58 attained a 92% accuracy in its fish feeding system, though the researchers attributed the remaining error to the small size of the fish food granules, which affected the number of pellets dispensed into the tank. Additionally, S72 employed fuzzy logic to regulate the ideal portion size for a cat, achieving a 100% precision in the operation of a servo motor used to control the food dispensing outlet.

Further research on nutritional control in pets includes S61, which was specifically evaluated with dogs. This study employed feeding data to implement precise nutritional control. The experimental results assessed the amount of leftover food compared to the supplied portions over different hours of the day. The findings revealed distinct feeding patterns based on the dog’s age. Puppies tend to overeat, necessitating portion adjustments. Young adult dogs maintain a balanced diet. Senior dogs often consume less than required, indicating the need for age-appropriate dietary supplements.

Finally, S49 conducted the most rigorous tests to evaluate the effectiveness of its pet feeding prototype, particularly concerning portion accuracy. The device demonstrated a precision rate of 96.94% when dispensing pre-determined food portions for pets.

Additionally, the prototypes analyzed also focused on aspects beyond providing food. The majority were oriented towards tracking pets for health monitoring (e.g., S6, S7, S23, and S35), emotional support (e.g., S18, S19, S25, S26, S33, S48, and S56), providing thermal comfort (e.g., S14, S32, S40, S41, and S59), cleaning of defecation areas (e.g., S12, S16, and S20), or simply for location and/or activity tracking (e.g., S4, S6, S7, S17, S23, S34, S35, S39, S62, and S65). Some prototypes were also designed for remote pet monitoring, particularly during owner absences or veterinary stays (e.g., S23 and S25). Furthermore, they addressed the management of spaces where pets relieve themselves, such as litter boxes. These kinds of prototypes are illustrated in Figure 6.

More specifically,

Table 2. Main functionalities and contributions of the studies analyzed.

ID	Type of Pet	Type of Device	Functionalities of the System	Ref.
S1	Fish	Tank	IoT-enabled telemetry system for fish tanks, integrating voice commands for monitoring and control, and real-time data collection on the water quality, temperature, and lighting, with a focus on enhancing the user experience for Generation Z.	[30]
S2	General	Feeder	IoT-enabled system for automated pet feeding and plant watering. Continuous monitoring to check if the pet has eaten or not.	[31]
S3	Cat/Dog	Feeder	IoT-enabled automated pet care system integrating a food feeder, water dispenser, and litter box with real-time monitoring and remote access.	[32]
S4	General	Robot	IoT-enabled robotic pet care system for monitoring, feeding automation, remote-controlled interaction, and mapping the position and appearance of the pet.	[33]
S5	General	Feeder	IoT-enabled pet care system for real-time monitoring, automated feeding and watering, and error notification services.	[34]
S6	General	Collar	IoT-enabled pet monitoring system using an intelligent collar for tracking physiological signs, movement, and location, integrating cloud storage.	[35]
S7	General	Collar	IoT-enabled pet monitoring system integrating machine learning for activity recognition, heart rate tracking, and GPS-based location tracking. Using historical data for future medical emergencies.	[36]
S8	Cat/Dog	Feeder	IoT-enabled automatic pet feeder with scheduled feeding and portion control.	[37]
S9	Dog	Feeder	IoT-enabled pet food dispenser integrating real-time monitoring, automated feeding, and portion control via a mobile application. The system prevents obesity by dispensing controlled amounts of food.	[24]
S10	Dog	Feeder	IoT-enabled automatic pet feeder for remote feeding control and nutritional assessment based on the dog breed, size, and weight. Calculation of daily rations based on pets’ energy requirements.	[38]
S11	Hedgehogs, Rats, Hamsters	Exercise Box	IoT-enabled automated small pet physical activity monitoring system, using an exercise wheel and sensor-based tracking for analyzing the movement, distance traveled, and average speed.	[39]
S12	Cat	Feeder	IoT-enabled pet care system integrating a food feeder, water dispenser, and litter box, with real-time monitoring and control via a smartphone application. The system tracks pet feeding, drinking, and defecation habits for health monitoring.	[9]
S13	Dog/Cat	Feeder	IoT-enabled small pet food dispenser with scheduled feeding, portion control, and pet data tracking. The system calculates the optimal food amount based on the pet breed and weight, and provides historical feeding records.	[25]
S14	Mini pets	Home	IoT-enabled intelligent home system for mini pet feeding, integrating the real-time local and remote monitoring of environmental conditions and pet health, as well as the remote control of feeding, heating, and other execution components.	[40]
S15	Cat	Door	IoT-enabled pet door that uses artificial intelligence (AI) and computer vision to detect pets and control door access.	[41]
S16	Cat/Dog	Feeder, Defecation Pad, Door	An IoT-based mobile application for pet care that enables remote feeding, defecation monitoring, room temperature tracking, pet door control, music activation, and webcam surveillance.	[22]
S17	Cat	Tracking	An IoT-based deep learning pet tracking system that uses computer vision and deep neural networks to detect, classify, and track pets.	[23]
S18	Dog	IPTV	A web-of-objects-based pet care system that integrates sensor monitoring, real-time alerts, environmental control, and automated content streaming for pets.	[42]
S19	Dog	Wearable	Real-time pet–owner communication.	[43]
S20	Dog	Feeder, Pooping Pad, CCTV	An automatic system that allows pet owners to manage feeding schedules, monitor their pets’ activities, and maintain hygiene through automated defecation pad replacement.	[44]
S21	Cat	Collar, Feeder, Door	An IoT-based intelligent pet monitoring system that integrates an automatic pet door and an intelligent pet feeder.	[45]
S22	General	Home	An IoT-based smart pet station that provides remote monitoring, automatic feeding, timed pet release, and cleaning functionality.	[21]
S23	General	App Video Interface	A smart pet logistics system that integrates real-time tracking, transportation monitoring, and veterinary support using IP-RFID technology.	[46]
S24	Dog/Cat	Robot	An IoT-based pet care system that integrates remote feeding, automatic water dispensing, live video surveillance, and a speaker system for interaction.	[47]
S25	Cat	Cage	An IoT-enabled pet care system that provides automated feeding, environmental monitoring, and live video surveillance for pet owners.	[48]
S26	Turtle	Aquarium	IoT-enabled system for remote water parameter management in aquariums.	[49]
S27	General	Feeder	Automated food dispensing and real-time monitoring of pet behavior.	[50]
S28	General	Feeder	An IoT-enabled automatic pet feeder that provides remote feeding, portion control, and the monitoring of food levels.	[8]
S29	Cat	Feeder	An IoT-enabled automatic pet feeder that integrates remote control and the real-time monitoring of food levels.	[51]
S30	Dog	Feeder	An IoT-enabled automated dispenser for food and water via smartphone.	[52]
S31	General	Feeder	An IoT-enabled system providing remote monitoring and control of food and water levels for pets.	[53]
S32	General	Cooling Box	IoT-based smart automatic cooling system that provides comfortable conditions for pets.	[54]
S33	Dog	Collar	IoT-based pet dog sound event classifier (e.g., barking, growling, howling, and whining) to assess dogs’ behavior or emotional states.	[55]
S34	Dog	Collar, Cage	A care application for smart cage management, obesity management, behavioral health monitoring, and health assistance using the IoT and machine learning.	[56]
S35	Dog	Wearable	A wearable device designed for the real-time monitoring of vital signs and motion in dogs.	[57]
S36	General	Feeder	Feed dispenser to provide a correct amount of food as well as to predict food shortage.	[58]
S37	General	Feeder	Automated pet feeding system using the IoT, controlled via a mobile app.	[59]
S38	Cat	Feeder	Automated feeding and monitoring system for cats, accessible remotely via a website.	[60]
S39	General	Door	Automated pet cage door using RFID for access control and GPS tracking via LoRa WAN for pet location monitoring.	[61]
S40	General	Cage	IoT-based smart pet cage with automated food and water dispensing, temperature monitoring, and safety features in high temperatures.	[62]
S41	Cat	Home	IoT-based pet shelter with automated food dispensing, waste management, temperature control, and real-time monitoring via a web interface.	[63]
S42	General	Robot	IoT-based robotic pet daycare with automated feeding, water dispensing, video monitoring, and remote control for movement and interaction.	[64]
S43	General	Robot	IoT-based pet feeder system using the WoT (Web of Things) with voice control via Google Assistant, automated food dispensing, and real-time monitoring.	[65]
S44	Chicken	Feeder	IoT-enabled automated feeding system for poultry farms with precision feed dispensing to minimize waste and ensure consistent nutrient delivery.	[66]
S45	General	Feeder	Integrated IoT-based automatic pet feeder with real-time monitoring via a smartphone application for remote control.	[67]
S46	General	Home Barrier	IoT-based safety barrier system for home environments, limiting pet and child access to restricted areas such as kitchens, with weight and motion sensing.	[68]
S47	Dog	Feeder	IoT-based smart dog feeder with automated food, water, and medication dispensing, and real-time monitoring via a mobile app.	[69]
S48	General	Feeder	IoT-based mobile pet feeder with an IP camera, remote control, and mobility for enhanced interaction with pets.	[70]
S49	Cat	Feeder	IoT-enabled automated cat feeder with RFID-based cat recognition, real-time monitoring via a camera, and scheduled feeding through a mobile application.	[71]
S50	General	Feeder	IoT-based automatic pet feeder powered by a solar photovoltaic system, providing scheduled and real-time feeding control through a mobile app.	[72]
S51	Cat	Feeder	IoT-enabled automatic cat feeder with real-time control via bot, ultrasonic sensor for pet detection, and RFID-based cat recognition.	[73]
S52	General	Feeder	IoT-based smart pet feeder, enabling scheduled feeding with weight control and water level monitoring.	[74]
S53	Tortoise	Aquarium	IoT-enabled smart home system for tortoises, integrating temperature control, land–water swap, water filtration, feeding automation, and lighting.	[75]
S54	General	Feeder	IoT-enabled automatic feeder capable of dispensing both pet food and medication, remotely controlled via a web-based platform.	[76]
S55	General	Feeder	IoT-based automated pet food dispenser that monitors feeding habits, dispenses scheduled meals, and prevents overfeeding through tracking mechanisms.	[77]
S56	Cat	Feeder, Playmate, Door, Collar	IoT-enabled cat care system integrating feeding, play, automatic door access, monitoring, and remote control via a mobile app.	[78]
S57	Dog	Feeder	IoT-enabled smart dog feeder that automates feeding and drinking schedules.	[79]
S58	Fish	Aquarium	IoT-enabled smart aquarium system with automated feeding, water quality monitoring, and automatic water change, remotely controlled via a mobile app.	[80]
S59	General	House	IoT-enabled home for pet care, monitors temperature, humidity, and air quality, and adjusts environmental conditions for optimal pet comfort via AI.	[81]
S60	General	Feeder	IoT-enabled smart pet feeding system, allowing remote-controlled feeding schedules, monitoring of food levels, and the prevention of food contamination.	[82]
S61	General	Feeder	IoT-enabled pet system integrating food weight measurement, feeding automation, and real-time monitoring to ensure precise nutritional intake.	[83]
S62	General	House	IoT-enabled smart pet house with automated feeding, air purification, temperature and humidity control, real-time monitoring, and waste cleaning.	[84]
S63	General	Feeder	IoT-enabled pet feeder powered by solar energy, featuring automated feeding schedules, portion control, and real-time monitoring for sustainable pet care.	[85]
S64	General	Feeder	IoT-enabled automatic pet feeder controlled via Google Assistant, allowing voice commands and scheduled feeding through a mobile application.	[86]
S65	General	Feeder	IoT-enabled smart pet feeder with dual dispensers for solid and liquid food, and remote monitoring via a mobile application.	[87]
S66	General	Feeder, Collar	IoT-enabled smart pet feeder utilizing RFID to automate feeding, prevent food wastage, and monitor pet feeding habits remotely.	[88]
S67	General	Feeder	IoT-enabled pet feeder for automated feeding schedules and food level monitoring.	[89]
S68	Dog	Feeder	IoT-enabled smart dog feeder using RFID authentication for remote feeding control, scheduling, and monitoring.	[90]
S69	Dog	Robot	IoT-enabled robotic pet daycare system integrating feeding automation, pet monitoring, and remote control for efficient pet care management.	[91]
S70	General	Feeder	IoT-enabled pet feeder with portion control, feeding schedules, weight monitoring, and real-time pet surveillance.	[92]
S71	Dog	Feeder	IoT-enabled automatic feeder system with real-time weight monitoring, water dispensing, and scheduled feeding control.	[93]
S72	Cat	Feeder	IoT-enabled pet feeder that utilizes Mamdani’s Fuzzy Logic Inference System to determine optimal food portions based on the cat weight and feeding time.	[94]
S73	General	Robot	IoT-enabled robotic pet monitoring system integrating live video streaming, feeding automation, and water dispensing, controlled via a mobile app.	[95]
S74	General	Feeder	IoT-enabled rotating pet feeder that dispenses dry and wet food with programmed feeding control via a mobile app.	[96]
S75	Dog	Feeder	IoT-enabled automatic feeder for dogs with remote scheduling, food portion control, and real-time monitoring.	[97]
S76	Fish	Feeder	IoT-enabled fish feeder with scheduled and manual feeding options, live video monitoring, and remote control via a web interface.	[98]
S77	General	Feeder	IoT-enabled smart pet feeder integrating deep learning for pet recognition, portion control based on pet weight, and remote feeding scheduling.	[99]
S78	Cat	Feeder	IoT-enabled real-time cat auto feeder for scheduled feeding, ensuring portion control and food availability tracking.	[100]
S79	Fish	Feeder	IoT-enabled fish feeder for remote feeding control, real-time monitoring of feed levels, and scheduled feeding automation.	[101]
S80	General	Feeder	IoT-enabled smart pet feeder for remote monitoring, motion detection, and an AI-powered camera for real-time pet activity tracking.	[102]
S81	Cat	Pet Detector	IoT-enabled stray cat monitoring system integrating digital image processing for detection, automated feeding, and water dispensing.	[103]

shows the main functions of IoT device prototypes designed for pet care. The analysis of the 81 reviewed studies reveals a significant trend towards the development of prototypes for general pet care, dogs, cats, fishes, turtles, chickens, and rodents such as hamsters, rats, or hedgehogs. For these types of pets, which typically cohabit in households, various devices have been proposed, including food and/or water dispensers (e.g., S2, S3, S5, S8, S9, S10, S12, S13, S16, S20, S21, S27, S28, S29, S30, S31, S36, S37, S38, S44, S45, S47, S48, S49, S50, S51, S52, S54, S55, S57, S60, S61, S63, S64, S65, S66, S67, S68, S70, S71, S72, S74, S75, S76, S77, S78, and S80), cages (e.g., S25, S34, and S40), small pet houses (e.g., S14, S22, S41, S59, and S62), daily care robots (e.g., S4, S24, S42, S43, S69, and S73), tanks or aquariums (e.g., S1, S26, S53, and S58), collars (e.g., S6, S7, S21, S33, S34, and S66), other wearable devices (e.g., S19 and S35), exercise boxes (e.g., S11), door and/or access mechanisms (e.g., S15, S16, S21, S39, S46, and S56), defecation pads, IPTV systems (e.g., S18 and S20), cooling enclosures (e.g., S32), playmates (e.g., S56), and pet detection systems (e.g., S16, S20, and S81).

3.1.2. IoT Devices Sold Commercially

The Internet of Things (IoT) has brought significant advancements to the field of pet care, gaining momentum not only in academic research but also in the commercial sector. A web search revealed the emergence of companies solely dedicated to creating and marketing pet care products. These companies primarily focus on household pets, especially dogs and cats (Figure 7). Furthermore, large e-commerce platforms now offer a variety of pet-related products, including items specifically designed for the health, safety, and entertainment of pets.

In the commercial market, the types of IoT pet care devices available are like those explored in the academic and scientific research. As is illustrated in Figure 6, these commercially available devices are typically categorized into health and activity trackers, automatic feeders, pet cameras, and pet sitter services or “pet boxes”. While these same types of devices are studied in academia, the focus is often more scientific. Academic research typically examines these solutions in conjunction with other emerging technologies, such as artificial intelligence, to enhance the functionality, integrate predictive capabilities, and improve the overall user experience. In this way, the academic perspective seeks to innovate beyond the existing commercial applications by exploring how the IoT and AI can work together to meet more complex needs in pet care.

The specific brands under which pet care devices are marketed are irrelevant to this study. However, each brand tends to focus on the aesthetic appeal of its devices. High-quality materials are consistently used in the devices presented in Figure 5, creating a noticeable contrast with the prototypes shown in Figure 3 and Figure 4. In these academic prototypes, lower-cost or recycled materials are often used, resulting in less visually sophisticated products.

In academic studies, authors prioritize the conceptual aspects of the devices, focusing on functionality and testing affordable IoT technologies rather than investing in premium finishes that would appeal to a final user. From our perspective, only a few academic prototypes (e.g., S30, S51, S60, S61, S63, S64, and S74 in Figure 5, and S7, S21, and S35 in Figure 6) feature high-quality finishes, as the main objective in academia is to prove the feasibility and effectiveness of the technology rather than its market-ready appearance. Moreover, only one study has been identified that defines the device’s cost based on the materials used (S49), highlighting that proposals within the academic context are not primarily oriented toward business models for these types of devices, despite the fact that it represents a promising market at present.

3.2. RQ2. How Has Internet of Things (IoT) Technology Evolved in the Field of Pet Care from Its Inception to the Present Day?

3.2.1. Technology Used for Prototyping at Academic and Research Level

Internet of Things (IoT) technology has significantly evolved in pet care, marked by a substantial expansion in the variety of sensors, actuators, and microcontrollers available. Today, IoT systems for pets incorporate a wide array of sensors, including activity monitors, temperature and humidity sensors, and smart cameras, enabling the detailed monitoring of the pet’s environment and health status. Additionally, modern actuators, such as automatic feeders and remote interaction devices, complement these sensors, facilitating more dynamic and adaptive care.

Table 3. Hardware and software used by the systems of the IoT for pet care.

ID	Type of Proposal	Type of Device	Sensors/Actuators	Microcontrollers	Wireless Standards	Software Infrastructure	Ref.
S1	Prototype	Tank	PH-4502C (pH Sensor), DS181B20 (Temperature Sensor), Gravity DO Meter V1.0 (Dissolved Oxygen Level), Electrical Conductivity Sensor (Salinity), SEN0189 (Water Turbidity), T1592 (Water Level)	ESP32	Bluetooth, Wi-Fi	HTML	[30]
S2	Prototype	Feeder	FC28 (Soil Moisture Sensor), ESP32 CAM (Camera), SG90 (Servo Motor), Water Pump	ESP8266 NodeMCU	Wi-Fi	Blink	[31]
S3	Prototype	Feeder	HX711 (Load Cell Sensor), PIR Motion Sensor (Movement Detection), DS18B20 (Temperature Sensor), SG90 (Servo Motor), Water Level Sensor	Arduino UNO	Wi-Fi	ThingSpeak Cloud	[32]
S4	Prototype	Robot	Pi Camera, Ultrasonic Sensor, L298N (Motor Driver)	Raspberry Pi 4, Arduino UNO	Wi-Fi	N/A	[33]
S5	Prototype	Feeder	HX711 (Load Cell), MG90S (Servo Motor)	Raspberry Pi Zero W	Wi-Fi	Node RED, MySQL, WhatsApp	[34]
S6	Prototype	Collar	MAX30102 (Heart Rate), JY901 (Gyroscope), A9G (GPS Location Tracking), Gravity Sensor (Weight)	ESP8266	Wi-Fi, GSM	Ali Cloud	[35]
S7	Prototype	Collar	GY-61 (Accelerometer), Neo 6M (GPS Module), Pulse Sensor (Heart Rate)	ESP32	Wi-Fi	TensorFlow, Flask, ThingSpeak Cloud	[36]
S8	Prototype simulation	Feeder	HC-SR04 (Ultrasonic Sensor), SG90 (Servo Motor), DS3231 (RTC), Piezo Buzzer (Auditory Alert System)	Arduino Uno R3	N/A	N/A	[37]
S9	Prototype	Feeder	HC-SR04 (Ultrasonic Sensor), SG90 (Servo Motor), DS3231 (RTC), A4988 (Stepper Motor Driver)	D1 Mini ESP8266	Wi-Fi	Blynk	[24]
S10	Prototype	Feeder	SIM900 (GSM/GPRS Module), DC Motor, Load Cell (Weight)	Arduino	Wi-Fi	Twitter API, Android	[38]
S11	Prototype	Exercise Box	Reed Switch (Rotation)	Arduino Mega 2560	Ethernet	ThingSpeak Cloud, MATLAB	[39]
S12	Prototype	Feeder	SG90 (Servo Motor), HX711 (Load Cell), HC-SR501 (PIR Motion Sensor)	Arduino Uno	Wi-Fi	Blynk	[9]
S13	Prototype	Feeder	MG995 (Servo Motor), HX711 (Weight Sensor), DS3231 (Real-Time Clock)	ESP32	Wi-Fi	Blink	[25]
S14	Prototype	Home	DHT11 (Temperature and Humidity Sensor), GP2Y1014AU (PM2.5 Sensor), MQ-2 (Smoke Sensor), MLX90615 (Non-Contact Pet Body Temperature), Pulse Sensor (Heart Rate)	STM32F103ZET6	Wi-Fi, Zig Bee	Witty Cloud	[40]
S15	Prototype	Door	ESP32-CAM, Servo Motor	Raspberry Pi 4	Wi-Fi	Python, Flask, AWS Cloud	[41]
S16	Prototype	Feeder, Defecation Pad, Door	6V 77RPM-SGM25-370 (DC Gear Motor), 12V Plastic Water Solenoid Valve (Water Flow), FS90 Micro Servo (Locking System), GP2Y0A21YK0F (Short-Range Infrared Sensor), BMP180 (Barometric Pressure and Temperature Sensor), Webcam	Raspberry Pi Model B+	Wi-Fi	Cayenne IoT Platform, Xamarin Mobile App Development, Python	[22]
S17	System	Tracking	ESP32-CAM	Raspberry Pi 4	Wi-Fi	Python, TensorFlow, OpenCV, MATLAB, Google Cloud	[23]
S18	Architecture	IPTV	CCTV Camera, Humidifier, Sprinkler (Watering), Ventilator, Heater (Temperature), Radiator, Motion Sensors, Temperature Sensors, Window/Door Sensors	N/A	Wi-Fi	WOA framework	[42]
S19	Prototype	Wearable	Pi Camera Module v1.3, DS18B20 (Temperature Sensor), Polar T34 (Heart Rate Sensor), Servo Motor	Raspberry Pi Model B+	Wi-Fi	Python, InitialState (Cloud Data)	[43]
S20	Prototype	Feeder, Pooping Pad, CCTV	HX711 (Load Cell), SG90 (Servo Motor), HC-SR501 (PIR Motion Sensor), DHT11 (Temperature and Humidity Sensor), Ultrasonic Sensor, Stepper Motor, Pi Camera	Arduino, Raspberry Pi	Wi-Fi	N/A	[44]
S21	Prototype	Collar, Feeder, Door	Light Motion Sensor, RFID Sensor, Servo Motor, Buzzer, Temperature and Humidity Sensors, Camera	ATmega128L	N/A	C#	[45]
S22	Architecture	Home	Load Cell Sensor (Food and Water Level), Solenoid Valve (Water Dispensing), Servo Motor, Infrared Sensor, Electromagnetic Lock, Buzzer	Arduino Mega 2560	Wi-Fi, GPRS	N/A	[21]
S23	Prototype	App Video Interface	IP-RFID Tags, Temperature Sensor, Humidity Sensor, Infrared Camera, GPS Module, Speakers and Microphone	N/A	Wi-Fi, RFID, GPS	MATLAB	[46]
S24	Service system	Robot	Pi Camera Module, SG90 Servo Motor, Relay Module, Speaker Module	Raspberry Pi 3B+	Wi-Fi	VNC Viewer, Python	[47]
S25	Prototype	Cage	ESP32-CAM, DHT22 (Temperature and Humidity Sensor), HX711 (Load Cell), SG90 Servo Motor	ESP32	Wi-Fi	N/A	[48]
S26	Prototype	Aquarium	BME280 (Temperature, Humidity, and Air Pressure), DS18B20 (Water Temperature Sensor), Camera	ESP32-WROVER	Wi-Fi	Google Apps Script	[49]
S27	Prototype	Feeder	ESP32-CAM, Ultrasonic Sensor, Servo Motor, L298N Motor Driver	ESP32	Wi-Fi	PHP, Firebase	[50]
S28	Prototype	Feeder	HC-SR04 (Ultrasonic Sensor), HX711 (Load Cell), Stepper Motor	N/A	Wi-Fi	Android Studio	[8]
S29	Prototype	Feeder	HC-SR04 (Ultrasonic Sensor), 20 kg Servo Motor	NodeMCU ESP8266	Wi-Fi	MIT App Inventor	[51]
S30	Prototype	Feeder	HX711 (Load Cell), Ultrasonic Sensor, Servo Motors, DC Motor with Driver L293D (Water Dispensing), Pi Camera	Arduino Mega 2560 Raspberry Pi 3B	Wi-Fi	Android	[52]
S31	Prototype	Feeder	HX711 (Load Cell), Stepper Motor, DC Motor with L293D H-Bridge	Raspberry Pi B+	Wi-Fi	Python Django	[53]
S32	Prototype	Cooling Box	DHT11 (Temperature and Humidity Sensor), Thermoelectric Peltier Module (Cooling Mechanism), CPU Cooler Fan (Heat Dissipation), Silica Gel Desiccant (Humidity Reduction), Relay Module (Cooling System), DC fan, DHT-11 Sensor	Arduino Uno NodeMCU ESP8266	Wi-Fi	Blynk	[54]
S33	Prototype	Collar	LM-393 (Noise Sensor)	Arduino Pro Mini	Wi-Fi	Python, TensorFlow, Keras	[55]
S34	Prototype	Collar Cage	DHT11 (Temperature and Humidity Sensor), BH1750 (Light Intensity Sensor), Gyroscope and Accelerometer, SG90 (Servo Motor)	Raspberry PI 3 ESP-32	Wi-Fi GSM	ML Yolv7, TensorFlow, Firebase, Flutter	[56]
S35	Prototype	Wearable	GY-87 (6-axis IMU), PMODTMP2 (Temperature Sensor), POLAR-OH1 (Optical Heart Rate Sensor)	Raspberry PI 3B	Wi-Fi Bluetooth Low Energy	React Native Language MySQL Python	[57]
S36	Prototype	Feeder	HX711 (Load Cell Amplifier), HC-SR04 (Ultrasonic Sensor), Infrared Sensor, SG90 Servo Motor	ESP32	Wi-Fi	Python IFTTT Adafruit	[58]
S37	Prototype	Feeder	Servo Motor	Arduino UNO	Wi-Fi	Bolt Cloud	[59]
S38	Prototype	Feeder	Load Cell Sensor, DHT11 Sensor, Servo Motor	NodeMCU	Wi-Fi	Php, MySQL	[60]
S39	Prototype	Door	RFID RC522, NEO-6M GPS Module, Solenoid Lock, Relay Module	Arduino UNO	LoRa Wan	Maps Application	[61]
S40	Prototype	Cage	DHT22 (Temperature and Humidity Sensor), HX711 Weight Sensor, Water Level Sensor, Servo Motor, Mini Electric Water Pump	ESP32	Wi-Fi	Blynk	[62]
S41	Prototype	Home	DHT11 (Temperature and Humidity Sensor), FS90R Servo Motor, MG996R Servo Motor, Brushless DC Fan	Durian Uno ESP8266	Wi-Fi	Blynk	[63]
S42	Prototype	Robot	Stepper Motor, Speaker, Camera, Water Dispenser	Raspberry Pi	Wi-Fi	Python	[64]
S43	Prototype	Robot	Servo Motor, Camera, Laser Range Finder (Obstacle Detection)	Arduino NodeMCU	Wi-Fi	Google Assistant, Adafruit, IFTTT	[65]
S44	Prototype	Feeder	Servo Motor, Load Cell Sensor, DS3231 RTC Module	Arduino Mega	Wi-Fi	N/A	[66]
S45	Prototype	Feeder	DC Motor, Pi Camera	Raspberry Pi	Wi-Fi	Python, Android	[67]
S46	Prototype	Home Barrier	50kg Load Cells (Weight), HX711 Load Cell, Amplifier, PIR Motion Sensor, SG90 Servo Motor	Things Uno	LoRaWAN Wi-Fi	Ubidots, C++	[68]
S47	Prototype	Feeder	Load Cell, HX711 ADC, Water Level Sensor, LM35 Temperature Sensor, MG995 Servo Motor, 775 DC Motor	ESP32	Wi-Fi Bluetooth	MIT App Inventor II, MySQL, PHP, C++, Google Assistant	[69]
S48	Prototype	Feeder	DC Motors, Servo Motor, Submerged Motor, IP Camera	Raspberry Pi 3	Wi-Fi	Android, Mosquitto, Raspbian OS	[70]
S49	Prototype	Feeder	Load Cell, HC-SR04 Ultrasonic Sensor, RFID Reader, ESP32-CAM, DC Motor	ESP32	Wi-Fi	Firebase, MIT App Inventor, Ngrok	[71]
S50	Prototype	Feeder	HX711 (Weight Sensor), Servo Motor, Solar PV Panel, Relay	ESP8266, Arduino	Wi-Fi	Blynk	[72]
S51	Prototype	Feeder	Load Cell, Ultrasonic Sensor, Servo Motor, ESP32-CAM, Infrared Sensor	ESP32	Wi-Fi	Telegram Bot	[73]
S52	Prototype	Feeder	Load Cell, Float Sensor (Water Level), Servo Motor	ATMEGA32	N/A	N/A	[74]
S53	Prototype	Aquarium	Waterproof Temperature Sensor, Load Cell, Servo Motor, Light Sensor, Water Pump, Infrared Sensor	Arduino Uno Wi-Fi 32	Wi-Fi	WiFiNINA Library	[75]
S54	Prototype	Feeder	Load Cell, Servo Motor, Detachable Pill Container	Arduino	Wi-Fi	N/A	[76]
S55	Prototype	Feeder	TEMT6000 (Light Sensor), Load Cell, RFID MFRC522, RTC DS1307, SG90 Servo Motor	Arduino Pro Mini	Wi-Fi	N/A	[77]
S56	Prototype	Feeder, Playmate, Door, Collar	Load Cell, Servo Motor, BLE HM-10 (Identification), IP Camera, Laser Module (Play Stimulation)	Raspberry Pi 3 Model B	Wi-Fi Bluetooth Low Energy (BLE)	Firebase, Node.js, Android App, RTMP Server	[78]
S57	Prototype	Feeder	Load Cell, Ultrasonic Sensor, RTC DS2321, Servo Motor, Mini Water Pump	Arduino UNO R3, ESP8266-01	Wi-Fi	Blynk	[79]
S58	Prototype	Aquarium	pH Sensor, Temperature Sensor, Ultrasonic Sensor, Servo Motor, Water Pump, LED Light, ESP32-CAM	Arduino MEGA 2560 ESP32	Wi-Fi	Blynk	[80]
S59	Prototype	House	AM2302 (Temperature and Humidity Sensor), GY-302 (Light Sensor), HX711 (Weight Sensor), TGS2602 (Air Quality Sensor), Infrared Thermal Image Sensor, Servo Motor	Arduino UNO	Wi-Fi	MIT App Inventor Android	[81]
S60	Prototype	Feeder	Load Cell, Ultrasonic Sensor, Servo Motor, RTC Module	NodeMCU-ESP8266	Wi-Fi	Blynk	[82]
S61	Prototype	Feeder	Load Cell, DHT Sensor (Temperature and Humidity), Servo Motor, HX711 Weight Sensor	NodeMCU ESP8266	Wi-Fi	Blynk	[83]
S62	Prototype	House	DHT11 (Temperature and Humidity Sensor), MQ135 (Air Quality), Ultrasonic Sensor, Servo Motor, Mini Bulb, Camera	ESP32	Wi-Fi	Blynk IoT App Google Assistant IFTTT	[84]
S63	Prototype	Feeder	DS3231 RTC, Servo Motor, Load Cell, Matrix Keypad	Arduino UNO	N/A	N/A	[85]
S64	Prototype	Feeder	Servo Motor	NodeMCU ESP8266	Wi-Fi	Google Assistant Adafruit	[86]
S65	Prototype	Feeder	Load Cell, Servo Motor, Solenoid Valve, Camera	ATSAMD21 chip Arduino MKR Wi-Fi 1010	Wi-Fi	N/A	[87]
S66	Prototype	Feeder, Collar	RFID Sensor, Ultrasonic Sensor, Servo Motor, IR Sensor (Presence Detection)	Arduino UNO, NodeMCU	Wi-Fi	ThingSpeak Cloud	[88]
S67	Prototype	Feeder	Ultrasonic Sensor, Servo Motor	NodeMCU ESP8266	Wi-Fi	N/A	[89]
S68	Prototype	Feeder	RFID Sensor, Load Cell, Servo Motor Real-Time Clock (Feeding Schedule)	Arduino UNO	Wi-Fi	Android Node.js, MySQL	[90]
S69	Prototype	Robot	Servo Motor, Ultrasonic Sensor, RFID Sensor, Camera	AtMega328P	Wi-Fi	MIT App Inventor	[91]
S70	Prototype	Feeder	Load Cell, Ultrasonic Sensor, Servo Motor ESP32-CAM	ESP32	Wi-Fi	Blynk	[92]
S71	Prototype	Feeder	Load Cell, Servo Motor, Solenoid Valve, Real-Time Clock	PIC16F877A	N/A	MikroC Compiler Proteus Simulation	[93]
S72	Prototype	Feeder	Load Cell, Ultrasonic Sensor, Servo Motor, Buzzer	Arduino UNO ATmega328	Wi-Fi	Blynk	[94]
S73	Prototype	Robot	Servo Motor, Pump Motor, Camera, Ultrasonic Sensor	ESP32	Wi-Fi	Blynk	[95]
S74	Prototype	Feeder	Load Cell, Stepper Motor, Real-Time Clock, Ultrasonic Sensor	ESP32	Wi-Fi	Android HiveMQ Broker	[96]
S75	Prototype	Feeder	Load Cell (Food Weight), Stepper Motor, RTC Module, Ultrasonic Sensor	ESP32	ESP32	Angular Node.js MongoDB	[97]
S76	Prototype	Feeder	Stepper Motor, Pi Camera	Raspberry Pi B+	Wi-Fi	Apache Web Server Python	[98]
S77	Prototype	Feeder	HX711 Weight Sensor, HEJO Camera, HC-SR04 Ultrasonic Sensor, Servo Motor	Arduino Mega 2560	Wi-Fi	YOLOv5	[99]
S78	Prototype	Feeder	Ultrasonic Sensor, Servo Motor Real-Time Clock	Arduino Uno	N/A	N/A	[100]
S79	Prototype	Feeder	Servo Motor (Food Dispensing), Ultrasonic Sensor, LM35 Sensor (Water Temperature)	NodeMCU	Wi-Fi	Blynk	[101]
S80	Prototype	Feeder	Load Cell (Food Weight), PIR Sensor (Motion Detection), Servo Motor, ESP32-CAM	ESP32	Wi-Fi	Blynk	[102]
S81	Prototype	Pet Detector	Pi Camera, Load Cell (Food Weight), Water Level Sensor, Servo Motor	Raspberry Pi 3B	Wi-Fi	TensorFlow Lite, Android	[103]

provides a detailed overview of the diverse sensors, actuators, microcontrollers, and software tools employed to create current IoT systems in the realm of pet care. Most of the studies present specific details, which favors replicability. However, some studies do not indicate some of the technologies used in the process of creating prototypes and systems.

The heterogeneity in hardware and software used in the analyzed pet device prototypes shows a clear focus on simplicity and low cost. Some of the prototypes featured high-quality finishes, while others used lower-quality casings. In all cases, the objective of each proposal was to demonstrate and validate the designs in practical terms.

In terms of the hardware, the most common microcontrollers used in the development of prototypes were ESP32, Arduino, and Raspberry Pi, which are widely valued for their affordability and adaptability. These microcontrollers allow for the integration of various low-cost sensors, such as pH, temperature, motion, and proximity sensors, which meet the basic requirements of prototypes without high precision demands. This choice of components is especially suitable in an academic context, where the goal is to investigate and experiment with reduced costs.

Furthermore, the analyzed prototypes mainly included accessible and easy-to-use sensors for food dispensers, smart collars, mini pet houses, or pet robots. Low-cost sensors, such as motion sensors, cameras, temperature and humidity sensors, and GPS and weight sensors, stand out for their ease of implementation, allowing developers to focus on the functional development of their prototypes. These sensors, along with basic actuators like servo motors and gear motors, enabled researchers to build functional prototypes that adequately address basic pet care needs without the need for high-end hardware. Some of the prototypes utilized timers or counters for scheduling feeding actions.

The software infrastructure also adapts to cost constraints. Many of the prototypes utilized IoT platforms, which offer tools for managing and storing data in the cloud economically and simply. This software choice allowed for the implementation of dashboards integrating functionalities such as remote control and real-time monitoring with low maintenance requirements. Messaging and notification platforms, such as Telegram (e.g., S51) and WhatsApp (e.g., S5), were also used to add alert features to the prototypes, enhancing their ability to interact remotely with users. Some of the proposals utilized assistants such as Google Assistant (e.g., S43, S47, S62, and S64) or Alexa (e.g., S1) to enable natural interaction with the prototype.

Regarding cloud computing, several of the analyzed devices in this study integrated cloud computing services. Among the most relevant services were AWS Cloud (e.g., S15), Google Cloud (e.g., S17), Bolt Cloud (e.g., S37), InitialState (e.g., S19), Witty Cloud (e.g., S14), and Ali Cloud (e.g., S6), ThingSpeak Cloud (e.g., S3, S7, S11, and S66), among others. Additionally, some of the analyzed prototypes also incorporated artificial intelligence (AI) to enable intelligent mechanisms such as pet recognition. To achieve this, they employed tools such as TensorFlow (S7, S17, S33, S34, and S81) and Keras (S33).

On the other hand, the proposed prototypes in the literature showed a trend of using easily implemented wireless standards like Wi-Fi (e.g., S1–S7, S9, S10, S12-S20, S22–S45, S47–S51, S53–S62, S64–S70, S72–S77, and S79–S81) and Bluetooth (e.g., S1, S35, and S47) due to their compatibility with a wide range of low-cost microcontrollers, such as ESP32 (e.g., S1, S7, S13, S25, S27, S36, S40, S47, S49, S51, S58, S62, S70, S74, S75, and S80), NodeMCU ESP8266 (e.g., S2, S9, S29, S38, S41, S57, S60, S61, and S67), Arduino (e.g., S3, S8, S10, S12, S21, S22, S23, S25, S28, S30, S37, S39, S44, S46, S50, S53, S54, S55, S57, S58, S60, S63, S66, S68, S71, S72, and S77), and Raspberry Pi (e.g., S4, S15, S16, S17, S19, S20, S24, S30, S31, S34, S35, S42, S45, S48, S56, S76, and S81). However, in a commercial setting, it would be ideal to consider more robust and secure options, such as Zigbee or LTE, which offer greater stability and range, and are better suited for mass-market products and continuous, secure monitoring applications. LoRa was used sparingly in systems where monitoring was carried out over extended areas. (e.g., S39 and S46).

3.2.2. Technology Used for Companies That Market Devices

For commercial prototypes, the hardware and software employed are significantly more advanced to meet the higher standards of precision and durability. High-precision sensors, such as high-resolution cameras and advanced biometric sensors, replace the lower-cost sensors used in previous prototypes (see Figure 5 and Figure 6). Similarly, device enclosures available in the formal market are of higher quality and made from materials that enhance the devices’ appeal to end-users. Furthermore, commercial devices feature a more robust software infrastructure with user-friendly interfaces designed to simplify user interaction.

Companies that market IoT products to support pet care are increasingly common. However, within the specifications, most of the products do not provide technical details about the sensors and tools integrated into the devices. Marketing focuses on describing their features and capabilities; for example, a device marketed for tracking pet activity only specifies that it assists in monitoring the pet’s activity and sleep, allowing the owner to track objectives and rankings according to the established plan. The devices sold by these companies are typically compatible with Wi-Fi, RFID, or Bluetooth. However, depending on the manufacturer, integrating them with other devices in an IoT network implemented in a connected smart home can be complex.

3.3. RQ3. How Has IoT Impacted Pet Wellness?

From a veterinary perspective, animal care aims to improve the daily lives of animals. Animal welfare, in this context, encompasses an animal’s lived experience, which is shaped by both positive and negative aspects across various domains, including nutrition, environment, physical health, and behavioral interactions. Broadly speaking, animal welfare largely reflects the interaction between the following three main components: the animal itself, human behavior, and the physical environment where the animal lives and performs its activities [115]. This concept is particularly applicable to pets, as they depend directly on their owners and are typically raised within the home. In the context of pet welfare, there is an added responsibility to ensure their health and well-being, minimize undue stress in their living environment, and enhance the human–animal relationship [116].

Pet welfare should be considered in all interactions between animals and people, with constant measurement and monitoring. However, as pets today are often adopted by young individuals or couples living alone, they spend much of the day in isolation, confined to yards or apartments. The analyzed IoT devices address this issue. For example, some studies introduce smart pet home appliances as technological solutions to enhance basic pet care, monitor health and safety, and improve overall quality of life. These appliances are categorized into the following five main types: training tools for behavior modification, automatic feeders with sensor and remote-control capabilities, monitoring devices for tracking health and activity, interactive toys for stimulation and engagement, and wearable trackers for monitoring health, activity levels, and location [117]. In this context, even artificial intelligence is starting to be integrated to develop sophisticated solutions [118].

In this study, the proposed pet care solutions vary in terms of the types of pets they are designed for and the locations in which they are implemented. The data in

Table 4. Main actions for wellness proposed in the analyzed studies. “x” means that the criterion is met.

ID	Type of Pet	Physical Environment	Health	Location	Feeding	Accessing	Monitoring	Watering	Comfort	Cleaning
S1	Fish	School					x		x
S2	General	Home			x		x	x
S3	Cat/Dog	Home			x			x
S4	General	Home		x	x		x
S5	General	Home			x			x
S6	General	Home	x	x			x
S7	General	Home	x	x			x
S8	Cat/Dog	Home			x
S9	Dog	Home	x		x		x
S10	Dog	Home	x		x
S11	Hedgehog, Rat, Hamster	Home Yard	x
S12	Cat	Home			x			x		x
S13	Dog/Cat	Home			x
S14	Mini pets	Home	x		x				x
S15	Cat	Home				x
S16	Cat/Dog	Home			x	x	x	x	x	x
S17	Cat	Home City		x
S18	Dog	Home	x				x		x
S19	Dog	Home	x				x
S20	Dog	Home			x		x			x
S21	Cat	Home		x	x	x	x		x
S22	General	Home			x	x	x	x		x
S23	General	Transportation	x	x			x		x
S24	Dog/Cat	Home			x			x
S25	Cat	Veterinary			x		x
S26	Turtle	Home					x		x
S27	General	Home			x		x
S28	General	Home			x
S29	Cat	Home			x
S30	Dog	Home			x		x	x
S31	General	Home			x			x
S32	General	Home							x
S33	Dog	Home					x
S34	Dog	Home	x				x
S35	Dog	Outdoor	x				x
S36	General	Home			x
S37	General	Home			x
S38	Cat	Home			x
S39	General	Home		x		x	x
S40	General	Home	x		x	x	x	x
S41	Cat	Home			x		x		x	x
S42	General	Home			x		x	x
S43	General	Farm	x	x	x		x	x	x
S44	Chicken	Farm			x
S45	General	Home			x		x
S46	General	Home				x
S47	Dog	Home	x		x			x
S48	General	Home			x		x	x
S49	Cat	Home			x		x
S50	General	Home			x
S51	Cat	Home			x		x			x
S52	General	Home			x			x
S53	Tortoise	Home							x	x
S54	General	Home	x		x
S55	General	Home			x		x
S56	Cat	Home	x		x	x	x
S57	Dog	Home			x			x
S58	Fish	Home	x		x				x	x
S59	General	Home	x		x	x	x	x	x	x
S60	General	Home			x					x
S61	General	Home	x		x
S62	General	Home			x		x		x	x
S63	General	Home			x
S64	General	Home			x
S65	General	Home			x		x
S66	General	Home			x
S67	General	Home			x
S68	Dog	Home			x
S69	Dog	Home			x			x
S70	General	Home			x		x
S71	Dog	Home			x			x
S72	Cat	Home			x
S73	General	Home			x		x	x
S74	General	Home			x
S75	Dog	Home			x
S76	Fish	Home			x		x
S77	General	Home			x
S78	Cat	Home	x		x		x
S79	Fish	Home			x
S80	General	Home			x		x	X		x
S81	Cat	Home			x		x	X
Total			20	8	64	12	40	22	14	12
Percentage			24.69	9.88	79.01	14.81	49.38	27.16	17.28	14.81

reveal that 93% of prototype implementations are within the home or indoors, indicating that this is the primary environment for technologies aimed at pet care. In contrast, other locations, such as schools, public transportation, farms, veterinary clinics (6%), and outdoor/urban spaces, accounted for 5% of cases. Some scenarios were proposed for both indoor and outdoor settings.

Regarding the actions aimed at providing pet well-being in the home, the results demonstrate a significant positive impact on pet well-being in several key areas. In terms of health, and specifically in the monitoring of health (e.g., S6, S7, S33, S34, and S35) and the activities performed by pets (e.g., S6, S7, S11, S17, S33, and S35), there was a focus on leveraging technology to enable owners to constantly track the physical condition of their pets, resulting in the early detection of health problems and a greater prevention of chronic diseases. This constant monitoring ensures that pets receive the necessary care in a timely manner, improving their quality of life in the long term. Pet surveillance is another kind of strategy of monitoring that has been implemented (e.g., S2, S18, S19, S25, S42, S45, S48, S49, S51, S56, S62, S70, S77, S80, and S81).

Automatic feeding had a crucial impact by ensuring that pets receive the appropriate amount of food, even in the absence of owners (e.g., S8, S9, S13, S30, S34, S49, S55, S59, S61, S72, and S78). This not only helped to prevent obesity and other nutritional problems, but also provided a greater peace of mind for owners, who could adjust and control feeding remotely (e.g., S9, S12, S27, S30, S47, S52, S60, and S74). Precision and regularity in feeding contribute to the overall well-being of pets by maintaining a balanced diet.

Access to the pet’s habitat is another aspect that has been worked on to improve in homes. In this sense, smart doors improved the mobility of pets within safe environments, allowing them to move freely without compromising their safety (e.g., S15, S16, S21, S22, S39, S46, S56, and S59). These doors are especially useful for allowing pets to have access to different areas of the home in a controlled manner, promoting a more dynamic and stimulating environment for them. Likewise, environmental and comfort monitoring ensured that the conditions within the home were optimal for pets, ensuring a safe and suitable environment for their well-being (all proposals).

Finally, automatic cleaning and waste management systems, present in three studies (e.g., S12, S20, S22, S41, S51, S59, S60, and S62), allowed for maintaining a clean and healthy habitat for pets, improving their environment and reducing the risk of hygiene-related diseases.

3.4. RQ4. How Has the Relationship Between Owners and Their Pets Changed with the Implementation of IoT?

The identified studies were grouped into seven main categories based on the functions of the prototypes. These categories are described as follows: proper feeding, preventive veterinary care, affection and care, a safe environment, a clean habitat, physical activity, and well-being.

• Proper Feeding. Firstly, prototypes designed to ensure proper feeding focused on automated pet feeding and nutritional management. These studies emphasized technological tools that enable the remote feeding and monitoring of feeding habits. Some also addressed obesity prevention by ensuring appropriate portion sizes and monitoring pet weight to adjust feeding as needed (e.g., S8, S9, S13, S30, S34, S49, S55, S59, S61, S72, and S78). Additionally, several proposals focused on balanced nutrition, notifying owners when food supplies run low or when adjustments are necessary for improved pet health. A shared priority across these studies was feeding, with additional emphasis on weight management and healthy eating habits.
• Preventive Veterinary Care. Secondly, devices oriented toward preventive veterinary care are useful for supporting veterinarians. These prototypes extended beyond feeding to offer comprehensive health monitoring, with an emphasis on disease prevention and the control of clinical variables. Some of the proposals monitored health indicators such as weight and behavior, while others included the detection of behavioral anomalies that may signal health issues (e.g., S6, S7, S33, S34, and S35). Additionally, certain devices monitored food portions and the animal’s nutritional status to prevent diet-related diseases. The common goal here was to use technology for continuous health monitoring, helping owners to maintain pet health and detect potential issues early to prevent serious conditions.
• Affection and Care. Thirdly, in the category of emotional care and companionship, some of the studies focused on tools that strengthen the bond between pets and owners. These prototypes enabled owners to maintain virtual contact with their animals, reducing anxiety for both (e.g., S56). Some of the devices allowed for direct communication, while others provided remote monitoring during owner absences. Collectively, these tools support pets’ emotional needs and companionship, even when the owner is not physically present (e.g., S16 and S69).
• Safe Environment. Fourthly, in the category of safe environment monitoring, the prototypes focused on ensuring secure surroundings for pets by tracking their location and environment. Some of the devices were particularly useful for pets in controlled habitats, like aquariums or cages, where they ensured stable, healthy conditions (e.g., S1, S6, S12, S14, S16, S20, S22, S25, S34, S40, S41, S49, S56, S58, S59, S60, S61, and S62). Other devices incorporated tracking systems to locate lost pets or monitor their movements in real time (e.g., S7). Additionally, some of the solutions provided secure access management, such as automated doors, reducing the risks of pets escaping or facing hazards (e.g., S15, S16, S21, S22, S39, S46, S56, and S59). The shared objective among these studies was to ensure pet safety by controlling both the physical environment and the pet’s location.
• Clean Habitat. Fifthly, in the habitat cleaning category, one study focused on aquarium management, aiming to reduce human error in tank maintenance and stabilize water conditions (e.g., S53 and S58). Other studies centered on automated cleaning, such as waste removal and feeding station cleanliness (e.g., S22, S41, S51, S59, S60, and S62). Studies were also proposed that focused on cleaning or managing the area where pets defecate, especially for devices designed for cats (e.g., S12, S16, and S20). Collectively, these studies aimed to ensure that pet habitats are safe and clean, enhancing both pet well-being and owner convenience.
• Physical Activity. Sixthly, studies focused on monitoring pet physical activity helped owners ensure their animals maintain adequate exercise levels (e.g., S6, S7, S11, S17, S33, and S35). Certain devices were relevant for pets with limited movement, like hamsters, by assessing physical activity throughout their lifespan or by season (e.g., S11). Other devices tracked pet movement actively, ensuring they obtained sufficient exercise for health. This group of studies emphasized physical activity as crucial for overall health, helping to prevent inactivity-related issues like obesity and cardiovascular disease (e.g., S7) or providing data for diagnostics in pet hospitals (e.g., S6).
• Well-Being. Finally, some of the studies prioritized enhancing pet well-being. These studies leveraged sensors and connected devices to monitor key variables such as health, feeding, and behavior. One device detected anomalies in pet behavior and notified the owner, while others used the IoT to monitor physical activity and pet location (e.g., S4, S6, S7, S21, S23, S39, and S43). The common theme here was the automation of monitoring and management, providing owners with real-time information and reducing the need for constant supervision. All of the proposals under analysis in this study contributed to improving pet welfare in one or more of the categories described herein.

3.5. RQ5. What Are the Emerging Trends in IoT for Pets and What Impact Might They Have in the Future?

The IoT is revolutionizing pet care through a series of technological innovations. Emerging trends in the IoT for pets increasingly leverage artificial intelligence (AI) and machine learning to enhance pet care, automate tasks, and improve pet–owner interaction. AI has been integrated into behavioral analysis and anomaly detection, allowing the real-time monitoring of activity levels, emotional states, and potential health risks (e.g., S7, S17, and S33). Computer vision and deep learning enable AI-powered pet recognition for access control, smart feeders, and stray pet detection (e.g., S15, S17, and S81). Sound event classification using AI helps to assess stress or behavioral changes in pets, enhancing the early diagnosis of potential issues (S33). Additionally, AI-driven environmental monitoring adjusts the temperature, humidity, and air quality to maintain optimal pet well-being.

As technology advances, there is a growing trend towards expanding and connecting devices in smart homes. The vision is to integrate a variety of appliances, from smart cages to interactive toys and wearable devices, into a cohesive system that responds to the demands of pet owners. This approach facilitates a more enriched ecosystem, improving the overall pet care experience and enabling the more personalized and effective management of pet well-being. In this line, multifunctional robots are designed to improve both the feeding and monitoring of pets, although they face technical challenges such as food obstruction and the transfer speed (e.g., S4, S42, S69, and S73). These issues are being addressed with new technologies, promising more precise food transfer and reduced operational errors, ensuring adequate and efficient nutrition for pets.

Moreover, the implementation of advanced identification and tracking technologies is revolutionizing pet monitoring. Multi-camera tracking systems and improved real-time data models allow for more detailed pet control, even in environments like air transport, where IP-RFID tags must function seamlessly to guarantee continuous supervision (e.g., S23). These innovations facilitate more effective tracking and provide greater peace of mind for owners.

Finally, the ability to interact with pets in real time from anywhere is constantly evolving. Future devices are designed to enable real-time communication, including the ability to transmit the owner’s voice to the pet and remote feeding (e.g., S19 and S69). Furthermore, the scalability of these systems enables their implementation in industrial settings, where managing the care of multiple animals simultaneously becomes more efficient. Additionally, it is crucial for prototypes to adapt to the specific characteristics of each pet, including the species, size, weight, and level of physical activity, ensuring personalized and effective care. These trends reflect a future where the IoT will transform pet care, making the process more accessible, interactive, cheap, and efficient.

4. Discussion

The integration of edge computing and fog computing promises to revolutionize pet care by enhancing the data processing efficiency and speed. Collaboration between edge devices enables data to be processed locally on the device, reducing latency, improving real-time responsiveness, and minimizing energy costs. This is crucial for applications requiring constant monitoring, such as pet health and behavior video surveillance. In these applications, optimal video transmission is essential to ensure a longer device lifespan and to enable more efficient video transmission, processing, and analysis [119]. An activity monitoring device can utilize edge computing to obtain and store physiological data and then provide immediate alerts in case of anomalies (e.g., heart rate and fever). Furthermore, fog computing extends processing to an intermediate layer between the edge and the cloud, achieving a greater analytical and storage capacity without relying entirely on a centralized infrastructure. This technology will improve the management and analysis of large volumes of data from multiple IoT devices to ensure more functionalities (e.g., malnutrition and mood).

On the other hand, cloud support is fundamental for the expansion and improvement of IoT solutions for pets. The cloud offers robust and scalable storage for the large volumes of data generated by monitoring devices such as cameras and sensors. Additionally, it facilitates remote access to these data, allowing owners to monitor and manage their pets from anywhere. The ability to perform large-scale data analysis in the cloud can also contribute to the creation of predictive models for pet health, enabling proactive care management. An activity enabled by cloud use is the ability to analyze behavioral and health patterns over time so to identify trends and offer personalized recommendations for pet care and disease prevention (e.g., obesity, diabetes, and chronic diseases).

However, it is crucial to optimize cloud usage, as it can lead to latency issues. To address this, it is recommended to implement optimization mechanisms for edge–cloud communications, such as intelligent task scheduling by dividing tasks between the edge and the cloud, using 5G networks to ensure high-speed and low-latency communication, and deploying mechanisms that distribute processing across multiple edge nodes to reduce the cloud load. For instance, algorithms can be employed to mitigate the impact of selfish user devices by allocating fewer edge server resources to those that maliciously discard tasks from other devices or offload an excessive number of tasks to edge or cloud servers [120].

There is a projected increase in the development of multifunctional devices that combine various capabilities in a single unit. These devices will integrate functions such as automatic feeding, health monitoring, and environmental management into a single appliance. By simplifying the daily management of pet care and reducing the need for multiple independent gadgets, these devices will not only improve the operational efficiency but also offer a more cohesive experience for owners. The studies analyzed show a trend towards integrated solutions that will facilitate more holistic and effective care tailored to the diverse needs of pets.

Artificial intelligence (AI) is a trending topic in the current society. The impact of AI on pet care is evident in several key areas that promise to revolutionize how owners interact with their animals. Firstly, AI optimizes health monitoring by analyzing large volumes of data from sensors and other devices connected to layers, such as the edge and fog. By analyzing historical and real-time data, AI can identify trends and patterns that allow for the anticipation and prevention of potential problems (e.g., early signs of obesity). These predictive applications offer recommendations based on future forecasts, such as diet adjustments, changes in exercise routine, or veterinary visits. This proactive approach improves the quality of care and preventive health of pets, ensuring more efficient and personalized management of their well-being.

Additionally, AI plays a crucial role in personalizing pet nutrition. AI-powered smart devices can create detailed profiles based on the individual nutritional needs of each pet. These systems automatically adjust portions and food types according to specific characteristics, such as the breed, weight, age, habitat, and activity level. This personalized approach improves overall pet health and allows owners to more accurately tailor the diet, adjusting to the needs of their animals and promoting optimal nutrition, which prevents diseases such as obesity-induced diabetes or cancer caused by the ingestion of harmful foods.

Finally, it is important to note that improving pet behavior and entertainment is another area where AI has a significant impact. Interactive devices and smart toys equipped with AI can analyze pet behavior and adapt their activities and games based on the animal’s responses and interest level. Smart toys could modify their challenges and stimuli to provide a more enriching and personalized experience, and the change their mood when alone in houses or apartments. This contributes to the mental and physical well-being of pets and provides owners with more effective tools to interact with their animals in a playful and constructive manner.

5. Conclusions and Future Directions

The revolution of the Internet of Things (IoT) has had a significant impact on pet care, enabling the development of what is known as the “Internet of Pets”. Through IoT sensors and devices, both academia and the market have explored solutions that facilitate pet monitoring and well-being. This study has shown that the IoT applied to animal care offers benefits in various areas, such as controlled feeding, health and activity monitoring, and safety within the home environment. Prototypes developed in academic settings, using accessible technologies like Arduino, Raspberry Pi, and NodeMCU, address key aspects of pet welfare, contributing to a strengthened bond between owners and their pets. These devices allow owners to manage proper feeding, monitor health, and ensure a safe and clean environment for their pets, even when they cannot supervise them constantly. The adoption of these technologies in the home significantly enhances pets’ quality of life, providing them with greater well-being and comprehensive care. In fact, some companies are already betting on the pet care market using IoT technology.

The devices proposed in the analyzed studies align with the animals classified as pets under the European Convention for the Protection of Pet Animals [11]. IoT pet care devices were primarily focused on dogs (15 studies) and cats (14 studies), while also considering aquatic pets such as fish (4 studies) and turtles (2 studies). Additionally, 1 study examined the care of hedgehogs, rats, or hamsters, while 43 studies described their proposals for household terrestrial pets in general.

Notably, 83% of the analyzed studies were conducted by Asian researchers, who developed prototypes aimed at improving pet care and well-being. These included food and water dispensers, collars, cages, tanks, surveillance systems, wearables, and pet care robots. While some of the systems integrated multiple functionalities, others, such as many of the food dispensers, were designed for a single purpose. Overall, the analyzed IoT pet care devices were diverse in terms of the functionality, design, technological tools, and testing methods, with some even conducting experiments on real pets.

In terms of future research, it is expected that the IoT for pet care will evolve through the integration of artificial intelligence (AI) technologies to achieve a sophisticated level of autonomy and intelligence. This would allow devices not only to collect data but also to learn from them, adapt their functions in real time, and share information with other smart home devices. This approach paves the way for multifunctional devices capable of addressing all of the relevant aspects of pet care, from automated and balanced feeding to emotional support, which is especially important for pets that spend long periods alone at home. Additionally, it is proposed to integrate the IoT with veterinary medicine so to develop devices that enable the early detection of diseases, thus facilitating preventive care. This is important because, nowadays, pets are important members of the family, and their loss can lead to grief and impact the owners’ emotional state. Lastly, a promising avenue for future developments is the adaptation of these devices for animals in captivity, such as those in zoos, to monitor their well-being without disturbing their habitat. This vision holds the potential to revolutionize animal care and welfare on a global scale, bridging the gap between technology, empathy, and science to create a world where animals thrive in both domestic and natural environments.