IoT

Integrating smart devices from the Internet of Things (IoT) with Customer Relationship Management (CRM) systems presents significant opportunities for enhancing customer experience through real-time data utilization. This article explores the technological frameworks and practical solutions for achieving seamless integration of IoT data within CRM platforms. By leveraging distributed Web systems, this study demonstrates how companies can improve scalability, responsiveness, and personalization in managing customer relationships. This paper outlines key architectural designs for distributed Web systems that ensure efficient real-time data processing while addressing challenges such as security, system integration, and the demands of analytics. This research provides insights into overcoming these challenges with strategies like load balancing, edge processing, and advanced encryption protocols. Results from simulations and practical implementations underscore the effectiveness of these approaches in optimizing operational efficiency and delivering hyper-personalized customer experiences. This study aims to bridge the gap between theoretical possibilities and real-world applications, offering actionable guidelines for organizations to fully leverage IoT-driven CRM systems. Full article

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings. Full article

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate. Full article

The objective of this study is to conduct an analysis of the scientific literature on the application of the Internet of Things (IoT) and artificial intelligence (AI) in enhancing supply chain operations. This research applies a dual approach combining bibliometric analysis and topic modeling to explore both quantitative citation trends and qualitative thematic insights. By examining 810 qualified articles, published between 2011 and 2024, this research aims to identify the main topics, key authors, influential sources, and the most-cited articles within the literature. The study addresses critical research questions on the state of IoT and AI integration into supply chains and the role of these technologies in resolving digital supply chain management challenges. The convergence of IoT and AI holds immense potential to redefine supply chain management practices, improving productivity, visibility, and sustainability in interconnected global supply chains. This research not only highlights the continuous evolution of the supply chain field in light of Industry 4.0 technologies—such as machine learning, big data analytics, cloud computing, cyber–physical systems, and 5G networks—but also provides an updated overview of advanced IoT and AI technologies currently applied in supply chain operations, documenting their evolution from rudimentary stages to their current state of advancement. Full article

The use of digital technology resources in public services enhances efficiency, responsiveness, and citizens’ quality of life through improved resource management, real-time monitoring, and service performance. The objective is to create and apply an IoT-based framework for connected municipal public services in a strategic digital city context. The research employed a modeling process validated in a Brazilian city, identifying seven related frameworks and four themes through a bibliometric review. The original framework comprises three constructs, eight subconstructs, and 12 variables, validated through a case study inquiry. The results revealed that the researched city has yet to enlarge IoT into its municipal public services as part of a digital city project initiative. Key recommendations for IoT implementation include prioritizing the preferences of digital citizens, expanding critical services suited for IoT, and updating municipal strategies to incorporate IT resources to streamline decision-making. The conclusion reiterates that the IoT framework for municipal services is effective when actionable information supports strategic planning and decision-making and highlights the transformative potential of IoT in driving more resilient and sustainable cities aligned with citizens’ needs. This approach allows public managers to enhance citizens’ quality of life while improving the efficiency and responsiveness of urban management processes and services. Full article

There are over 300 million smart homes worldwide and 60.4 million smart homes in the US, using devices like smart thermostats, smart plugs, smart door locks, etc. Yet in this age of smart and connected devices, we still use paper-based sticky notes on doors to display messages such as “Busy, do not disturb”, “In a Zoom meeting”, etc. In this project, a novel IoT-connected digital sticky note system was developed where the user can wirelessly send messages from a smartphone to a sticky note display. The sticky note displays can be hung on the doors of offices, hotels, homes, etc. The display could be updated with the user’s message sent from anywhere in the world. The key design challenge was to develop the display unit to consume as little power as possible to increase battery life. A prototype of the proposed system was developed comprising ultra-low-power sticky note display units consuming only 404 µA average current and having a battery life of more than six months, with a Wi-Fi-connected hub unit, an MQTT server, and a smartphone app for composing the message. Full article

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness. Full article

With the rapid increase in the human population and urbanization worldwide, the demand for food production has played a significant role in driving the integration of technology into agriculture. Various Cloud-based systems, such as livestock tracking systems, have been proposed. In those systems, data were collected by the sensors and sent to the Cloud for processing. However, significant issues with those systems were noted, such as high bandwidth utilization and security concerns, such as a high volume of row data traveling from the data collection devices (such as sensors) to the Cloud through the Internet. Additionally, the long distance between the Cloud and the data collection devices makes it unsuitable for latency-sensitive livestock disease monitoring and tracking systems. Therefore, this paper proposes a Fog–Cloud-based approach, where the processing is conducted at the Fog layer, closer to the data collection devices, and only the result is sent to the Cloud for remote viewing. The proposed method aims to reduce power consumption and latency in communication. To validate the proposed method, both the Cloud-based and Fog–Cloud-based scenarios are simulated using iFogSim (a novel simulation tool for IoT and Cloud computing), and the result shows that there is less than twice the power consumption in some scenarios and that the time consumed in the proposed Fog–Cloud-based system, depending on the number of sensors, is five to ten times lower. This study further supports the point that the Fog–Cloud-based is suitable for latency-dependent farming systems such as livestock tracking systems. Full article

Date palms are a vital resource in the oases of Morocco, providing economic and ecological benefits to local communities. However, monitoring these trees at scale presents challenges, particularly in rural areas where traditional IoT-based agricultural systems rely on extensive sensor networks. These systems face significant limitations, including high deployment costs, network coverage issues, and energy constraints. To address these challenges, we propose an agricultural robot equipped with multiple IoT sensors to monitor date palm health and environmental parameters, reducing the need for widespread fixed sensors. To overcome the latency, bandwidth, and energy inefficiencies associated with cloud-based solutions, the system integrates the fog computing paradigm. Fog computing brings computational resources closer to the data source, enabling real-time processing and decision-making while reducing network congestion. Using iFogSim, we evaluate the proposed system on key metrics, including latency, energy consumption, and network performance. The results demonstrate significant improvements over traditional approaches, highlighting the potential of IoT-based robotic systems combined with fog computing to revolutionize date palm monitoring in remote oases. Full article

Patient safety (PS) is essential in medical care, and preventing medication errors (MEs) is key to guaranteeing it. In Colombia, pharmaceutical services must comply with regulations that require adequate environmental monitoring to ensure medication quality. This study aims to propose an IoT-based smart system that automatizes temperature and relative humidity monitoring in the Colombian pharmaceutical service (CPS). Using the model for IoT platform design as a methodology, an efficient and flexible architecture that integrates data quality management (DQM) dimensions to improve the accuracy and reliability of the system was designed. In addition, tests based on the agile quadrant methodology demonstrate, as a result, its effectiveness, highlighting its ability to optimize environmental monitoring, prevent MEs, and improve PS. The successful implementation of this IoT-based smart system shows its potential in the pharmaceutical sector, offering an innovative solution that reduces risks and improves the quality of drug storage. Full article

The development of technologies using the Internet of Things (IoT) concept evolves daily. These numerous technologies, such as LoRa (Long Range) transceivers, find applications in various domains, including monitoring natural disasters and those caused by human error. Security vulnerabilities arise concurrently with the advancement of these new technologies. Cyberattacks seeking to disrupt device availability, such as denial-of-service (DoS) attacks, can effectively exploit vulnerabilities in LoRa devices, hindering disaster monitoring efforts. Therefore, our goal is to assess the network parameters that impact the development of a disaster monitoring environment using LoRaWAN. Specifically, we aim to identify the parameters that could result in network availability issues, whether caused by malicious actors or configuration errors. Our results indicate that certain LoRa network parameters (collision checks, packet size, and the number of nodes) can significantly affect network performance, potentially rendering this technology unsuitable for building robust disaster monitoring systems. Full article

Advances in deep learning (DL) models and next-generation edge devices enable real-time image classification, driving a transition from the traditional, purely cloud-centric IoT approach to edge-based AIoT, with cloud resources reserved for long-term data storage and in-depth analysis. This innovation is transformative for agriculture, enabling autonomous monitoring, localized decision making, early emergency detection, and precise chemical application, thereby reducing costs and minimizing environmental and health impacts. The workflow of an edge-based AIoT system for agricultural monitoring involves two main steps: optimal training and tuning of DL models through extensive experiments on high-performance AI-specialized computers, followed by effective customization for deployment on advanced edge devices. This review highlights key challenges in practical applications, including: (i) the limited availability of agricultural data, particularly due to seasonality, addressed through public datasets and synthetic image generation; (ii) the selection of state-of-the-art computer vision algorithms that balance high accuracy with compatibility for resource-constrained devices; (iii) the deployment of models through algorithm optimization and integration of next-generation hardware accelerators for DL inference; and (iv) recent advancements in AI models for image classification that, while not yet fully deployable, offer promising near-term improvements in performance and functionality. Full article

IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect to the network using IPv6. This study focuses on examining the performance and power consumption of routing protocols in the context of 6LoWPAN, drawing insights from prior research and utilizing simulation techniques. The simulation involves the application of routing protocols, namely Routing Protocol for Low-power and Lossy (RPL) Networks, Ad hoc On-demand Distance Vector (AODV), Lightweight On-demand Ad hoc Distance-vector Next Generation (LOADng), implemented through the Cooja simulator. The simulation also runs in different network topologies to gain an insight into the performance of the protocols in the specific topology including random, linear, and eclipse topology. The raw data gathered from the tools including Powertrace and Collect-View were then analyzed with Python code to transfer into useful information and visualize the graph. The results demonstrate that the power consumption, specifically CPU power, Listen Power, and Total Consumption Power, will increase with the incremental of motes. The result also shows that RPL is the most power-efficient protocol among the scenarios compared to LOADng and AODV. The result is helpful because it brings insights into the performance, specifically power consumption in the 6LoWPAN network. This result is valuable to further implement these protocols in the testbed as well as provide an idea of the algorithmic enhancements. Full article

The electric shower is the main form of heating water for bathing in Brazilian homes and one of the significant appliances related to electricity and water consumption. Internet of Things (IoT) projects make it possible to connect objects to the Internet and collect data from machines remotely. In this work, we developed an Internet of Things system for monitoring an electric shower, called Shower–IoT, whose sensor data are water temperature, electric tension, electric current, and water flow. To implement the software infrastructure, we used the services present in cloud computing, such as a broker, processing, and storage, in which the information about the electric shower was made available through an Android application. The results demonstrate that our system can monitor an electric shower integrated with cloud services, allowing the users to visualize its behavior in real time and detect possible failures by comparing sensor data from previous evaluations. Full article

This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas was conducted to ensure the IoT system’s good performance, reliability, and suitability for continuous cardiac monitoring and data processing. This study addresses the challenge of monitoring cardiac activity in patients with arrhythmias, focusing on the differences in heart rate variability (HRV) parameters between healthy individuals and those with extrasystolic arrhythmia. The purpose of this research is to evaluate the effectiveness of IoT-based systems using PPG and ECG sensors for cardiac data registration and HRV analysis. The system leverages time domain and frequency domain methods for HRV analysis to assess the states of the autonomic nervous system. Significant differences were observed in HRV parameters, such as the SDNN, SDANN, RMSSD, and the LF/HF ratio. The results demonstrated that both the PPG and ECG methods provide comparable HRV measurements, despite PPG’s higher susceptibility to noise. This study concludes that IoT-based monitoring systems with PPG and ECG integration can reliably detect arrhythmias and offer real-time data for cardiac care. Full article

The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly impeded by the potential security risks that it is susceptible to. While the potential of IoT is significant, the security challenges it faces are equally formidable. IoT security can be addressed from different angles, but one of the key issues is the access control model because among the many challenges, access control is a pivotal concern that determines the overall security of IoT systems. This eventually determines which device is given access to the IoT systems and which is denied access. In this work, we conduct a systematic and thorough survey on the state-of-the-art access control models in IoT. This study includes more than 100 related articles, including 77 best-quartile journal papers. We cover conventional as well as advanced access control models, taking the crucial period of various studies in this particular area. In addition, a number of critical questions are answered and key works are summarized. Furthermore, we identify significant gaps in existing models and propose new considerations and prospects for future developments. Since no existing survey explores both conventional and sophisticated access control models with essential challenges, trends and application domains analysis, and requirements analysis, our study significantly contributes to the literature, especially in the IoT security field. Full article

This article is devoted to the development and testing of a computer model of an IoT system that combines wireless network technologies for the online monitoring of climatic and soil conditions in agriculture. The system supports decision-making by predicting the probability of crop diseases. This study focuses on the processes of aggregation, wireless transmission, and processing of soil and climatic measurement data within infocommunication software and hardware solutions. This research makes both scientific and practical contributions. Specifically, it presents a computer model based on wireless sensor networks and edge-computing technologies. This model aggregates and intelligently processes agricultural monitoring data to predict crop diseases. The software component, developed using an adaptive neuro-fuzzy inference system (ANFIS), was integrated into the microcontroller unit of IoT systems for agricultural applications. This approach enabled the substantiation of an optimised algorithmic and structural organisation of the IoT system, enabling its use in designing reliable architectures for agricultural monitoring systems in open fields with decision-making support. Full article

Today, the development of the Internet of Things has grown, and the number of related IoT devices has reached the order of tens of billions. Most IoT devices are vulnerable to attacks, especially DdoS (Distributed Denial of Service attack) attacks. DDoS attacks can easily cause damage to IoT devices, and LDDoS is an attack launched against hardware resources through a small string of very slow traffic. Compared with traditional large-scale DDoS, their attacks require less bandwidth and generate traffic similar to that of normal users, making them difficult to distinguish when identifying them. This article uses the CICIoT2023 dataset combined with behavioral features and stacking mechanisms to extract information from the attack behavior of low-rate attacks as features and uses the stacking mechanism to improve the recognition effect. A method of behavioral characteristics and stacking mechanism is proposed to detect DDoS attacks. This method can accurately detect LDDoS. Experimental results show that the recognition rate of low-rate attacks of this scheme reaches 0.99, and other indicators such as accuracy, recall, and F1 score are all better than other LDDoS detection methods. Thus, the method model proposed in this paper can effectively detect LDDoS attacks. At present, DDoS attacks are relatively mature, and there are many related results, but there is less research on LDDoS detection alone. This paper focuses on the investigation and analysis of LDDoS attacks in DDoS attacks and deduces feasible LDDoS detection methods. Full article

Modern electrical grids are evolving towards distributed architectures, necessitating efficient and reliable state synchronization mechanisms to maintain structural and functional consistency. This paper investigates the application of conflict-free replicated data types (CRDTs) for representing and synchronizing the states of distributed electrical grid systems (DEGSs). We present a general structure for DEGSs based on CRDTs, focusing on the Convergent Replicated Data Type (CvRDT) model with delta state propagation to optimize the communication overhead. The Observed Remove Set (ORSet) and Last-Writer-Wins Register (LWW-Register) are utilized to handle concurrent updates and ensure that only the most recent state changes are retained. An actor-based framework, “Vigilant Hawk”, leveraging the Akka toolkit, was developed to simulate the asynchronous and concurrent nature of DEGSs. Each electrical grid node is modelled as an independent actor with isolated state management, facilitating scalability and fault tolerance. Through a series of experiments involving 100 nodes under varying latency degradation coefficients (LDK), we examined the impact of network conditions on the state synchronization efficiency. The simulation results demonstrate that CRDTs effectively maintain consistency and deterministic behavior in DEGSs, even with increased network latency and node disturbances. An effective LDK range was identified (LDK effective = 2 or 4), where the network remains stable without significant delays in state propagation. The linear relationship between the full state distribution time (FSDT) and LDK indicates that the system can scale horizontally without introducing complex communication overhead. The findings affirm that using CRDTs for state synchronization enhances the resilience and operational efficiency of distributed electrical grids. The deterministic and conflict-free properties of CRDTs eliminate the need for complex concurrency control mechanisms, making them suitable for real-time monitoring and control applications. Future work will focus on addressing identified limitations, such as optimizing message routing based on the network topology and incorporating security measures to protect state information in critical infrastructure systems. Full article