Search Results (3,548)

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article

Internet of things (IoT) and big data technologies are increasingly gaining significance in the implementation of various services and applications. Consequently, much of the research focused on energy efficiency and building management revolves around integrating IoT and big data technologies for data collection and processing. Occupancy detection, comfort, and energy management are the most important services for optimizing building energy consumption in smart buildings, and environmental data play a key role in improving these services. Furthermore, the integration of advanced and recent techniques, such as IoT, big data, and machine learning, is progressively becoming more vital for both researchers and industries. This paper presents and discusses various emerging technologies that will contribute to designing novel IoT-based architectures to improve smart building services. These technologies offer innovative solutions to address the challenges of interoperability, scalability, and real-time data processing within intelligent environments, paving the way for more efficient, adaptive, and user-centric smart building systems. The main aim of this research is to help researchers define an optimal architecture that presents all layers, from sensing to big data stream processing. We established comparative criteria between the most popular data processing techniques to select the appropriate framework for developing intelligent platforms for managing building services, such as occupancy detection systems and occupants’ comfort management, and further, to enhance the deployment of digital twins for critical environment monitoring and anomaly detection. The proposed architecture uses Apache Kafka, Apache Storm, and Apache SAMOA as its core components, creating a comprehensive platform for efficient data collection, monitoring, and processing with high performance in terms of fault tolerance and low latency. Full article

Digital transformation is reshaping business strategies and driving innovation across various industries including Cultural Heritage (CH) and tourism. Digital technologies, such as eXtended Reality (XR) and the Internet of Things (IoT), are increasingly being adopted to enhance visitors’ experiences, foster interactive engagement, and promote cultural knowledge. Despite the growing number of digital solutions proposed in the CH sector, several challenges remain in differentiating digital products and services, including matching industry needs and user expectations. This aspect is of particular interest when dealing with small and medium enterprises (SMEs), which often suffer from limited resources. Therefore, to design an effective digital solution, like a cloud-based platform for tourism and heritage applications, it is essential to first identify the key requirements, expectations, and preferences of SMEs and customers. This study presents the findings of a survey-based analysis conducted among 122 CH and tourism professionals, focusing on the most relevant features, services, and functionalities that such platforms should integrate. Results indicate a strong demand for cloud-based solutions that incorporate XR, IoT, sensors, and smart devices to collect context data and deliver personalized, immersive, and context-aware experiences. These insights suggest valuable practices for the development of digital tools that effectively support cultural organizations in engaging visitors. Full article

The prospect of integrating every object under a unified infrastructure, which provides humans with the possibility to monitor, access, and control objects and systems, has played a significant role in the geometric growth of the Internet of Things (IoT) paradigm, across various applications. However, despite the numerous possibilities that the IoT paradigm offers, security and privacy within and between the different interconnected devices and systems are integral to the long-term growth of IoT networks. Various sophisticated intrusions and attack variants have continued to plague the sustainability of IoT technologies and networks. Thus, effective methodologies for the prompt identification, detection, and mitigation of these menaces are priorities for stakeholders. Recently, data-driven artificial intelligence (AI) models have been considered effective in numerous applications. Hence, in recent literature studies, various single and ensemble AI subset models, such as deep learning and reinforcement learning models, have been proposed, resulting in effective decision-making for the secured operation of IoT networks. Considering the growth trends, this study presents a critical review of recently published articles whereby learning models were proposed for IoT security analysis. The aim is to highlight emerging IoT security issues, current conventional strategies, methodology procedures, achievements, and also, importantly, the limitations and research gaps identified in those specific IoT security analysis studies. By doing so, this study provides a research-based resource for scholars researching IoT and general industrial control systems security. Finally, some research gaps, as well as directions for future studies, are discussed. Full article

Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional encryption methods. To address these challenges, we propose investigating a quantum-safe solution for IoT-trusted computing. Specifically, we implement the first lightweight, practical integration of a quantum random number generator (QRNG) with a software-based trusted platform module (TPM) to create a deployable quantum trusted platform module (QTPM) prototype for IoT systems to improve cryptographic capabilities. The proposed quantum entropy as a service (QEaaS) framework further extends quantum entropy access to legacy and resource-constrained devices. Through the evaluation, we compare the performance of QRNG with traditional Pseudo-random Number Generators (PRNGs), demonstrating the effectiveness of the quantum TPM. Our paper highlights the transformative potential of integrating quantum technology to bolster IoT security. Full article

The backbone of the Internet of Things ecosystem relies heavily on wireless sensor networks and low-power wide area network technologies, such as LoRa modulation, to provide the long-range, energy-efficient communications essential for applications as diverse as smart homes, healthcare, agriculture, smart grids, and transportation. With the number of IoT devices expected to reach approximately 41 billion by 2034, managing radio spectrum resources becomes a critical issue. However, as these devices are deployed at an increasing rate, the limited spectral resources will result in increased interference, packet collisions, and degraded quality of service. Current methods for increasing network capacity have limitations and require advanced solutions. This paper proposes a novel hybrid spectrum sensing framework that combines traditional signal processing and artificial intelligence techniques specifically designed for LoRa spreading factor detection and communication channel analytics. Our proposed framework processes wideband signals directly from IQ samples to identify and classify multiple concurrent LoRa transmissions. The results show that the framework is highly effective, achieving a detection accuracy of 96.2%, a precision of 99.16%, and a recall of 95.4%. The proposed framework’s flexible architecture separates the AI processing pipeline from the channel analytics pipeline, ensuring adaptability to various communication protocols beyond LoRa. Full article

Private consumers, small businesses, and even large enterprises are all at risk from botnets. These botnets are known for spearheading Distributed Denial-Of-Service (DDoS) attacks, spamming large populations of users, and causing critical harm to major organizations. The development of Internet of Things (IoT) devices led to the use of these devices for cryptocurrency mining, in-transit data interception, and sending logs containing private data to the master botnet. Different techniques were developed to identify these botnet activities, but only a few use Graph Neural Networks (GNNs) to analyze host activity by representing their communications with a directed graph. Although GNNs are intended to extract structural graph properties, they risk causing overfitting, which leads to failure when attempting to do so from an unidentified network. In this study, we test the notion that structural graph patterns might be used for efficient botnet detection. In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. Our approach is built to work well with untested data, and our model is able to provide a vector representation for every node that captures its structural information. Finally, we demonstrate that, when the collection of node representation vectors is incorporated into a neural network classifier, our model outperforms the state-of-the-art GNN-based algorithms in the detection of bot nodes within unknown networks. Full article

Financial technology (FinTech) rapidly transforms financial landscapes across ASEAN-4 countries by enhancing financial inclusion and digital service accessibility. However, the key factors driving FinTech development in these economies remain ambiguous. While existing studies highlight the economic and technological aspects of FinTech adoption, limited research distinguishes the unique conditions shaping FinTech’s evolution in developing ASEAN markets. This study bridges this gap by identifying economic and non-economic determinants and exploring their mediating effects. This research aims to investigate the primary drivers of FinTech development in ASEAN-4, emphasizing the roles of financial access and technological readiness as mediators in fostering a sustainable FinTech ecosystem. Utilizing structural equation modeling (SEM) with SmartPLS3, this study analyzes secondary data from 2008 to 2018, evaluating macroeconomic indicators, banking conditions, internet penetration, innovation levels, population dynamics, and human development factors. General banking conditions, access to finance, and technological readiness significantly impact FinTech development. Additionally, financial accessibility and technological infrastructure mediate the influence of economic stability, innovation, and digital penetration on FinTech growth. This study underscores policymakers’ and stakeholders’ need to enhance digital infrastructure and financial accessibility to accelerate FinTech growth. Strengthening financial ecosystems will drive digital transformation and economic resilience in emerging ASEAN economies. Full article

The rapid development of mobile Internet technology has made users’ requirements for quality of service (QoS) continuously improve. The task unloading process of mobile edge computing has the problem that it is impossible to balance delay and energy consumption for task unloading under the condition of fluctuating network bandwidth. To address this issue, this paper proposes a task offloading scheme based on the Proximal Policy Optimization (PPO) algorithm. On the basis of traditional cloud edge collaborative architecture, the collaborative computing mechanism between edge node devices is further integrated, and the concept of service caching is introduced to reduce duplicate data transmission, reduce communication latency and network load, and improve overall system performance. Firstly, this article constructs an energy efficiency function with a certain weight ratio of energy consumption and latency as the core optimization objective. Then, the task offloading process of mobile terminal devices is modeled as a Markov Decision Process (MDP). Finally, the deep reinforcement learning PPO algorithm is used for training and learning, and the model is solved. The simulation results show that the proposed scheme has significant advantages in reducing energy consumption and latency compared to the comparative scheme. Full article

The number of services on the internet has experienced explosive growth, and the rapid and accurate discovery of required services among a vast array of similarly functioning services with differing degrees of quality has become a critical and challenging aspect of service computing. In this paper, we propose a trusted service discovery algorithm based on an ant colony system (TSDA-ACS). The algorithm integrates a credibility-based trust model with the ant colony search algorithm to facilitate the discovery of trusted web services. During the evaluation process, the trust model employs a pseudo-stochastic proportion to select nodes, where nodes with higher reputation have a greater probability of being chosen. The ant colony uses a voting method to calculate the credibility of service nodes, factoring in both credibility and non-credibility from the query node’s perspective. The algorithm employs an information acquisition strategy, a trust information merging strategy, a routing strategy, and a random wave strategy to guide ant search. To evaluate the effectiveness of the TSDA-ACS, this paper introduces the random walk search algorithm (RW), the classic max–min ant colony algorithm (MMAS), and a trustworthy service discovery based on a modified ant colony algorithm (TSDMACS) for comparison with the TSDA-ACS algorithm. The experiments demonstrate that this method can achieve the discovery of trusted web services with high recall and precision rates. Finally, the efficacy of the proposed algorithm is validated through comparison experiments across various network environments. Full article

Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. In intensive livestock systems, PLF is now quite widespread, allowing for the optimisation of management, thanks to the early recognition of diseases and the possibility of monitoring animals’ feeding and reproductive behaviour, with an overall improvement of their welfare. Similarly, PLF systems represent an opportunity to improve the profitability and sustainability of extensive farming systems, including those of small ruminants, rationalising the use of pastures by avoiding overgrazing and controlling animals. Despite the livestock distribution in several parts of the world, the low profit and the relatively high cost of the devices cause delays in implementing PLF systems in small ruminants compared to those in dairy cows. Applying these tools to animals in extensive systems requires customisation compared to their use in intensive systems. In many cases, the unit prices of sensors for small ruminants are higher than those developed for large animals due to miniaturisation and higher production costs associated with lower production numbers. Sheep and goat farms are often in mountainous and remote areas with poor technological infrastructure and ineffective electricity, telephone, and internet services. Moreover, small ruminant farming is usually associated with advanced age in farmers, contributing to poor local initiatives and delays in PLF implementation. A targeted literature analysis was carried out to identify technologies already applied or at an advanced stage of development for the management of grazing animals, particularly sheep and goats, and their effects on nutrition, production, and animal welfare. The current technological developments include wearable, non-wearable, and network technologies. The review of the technologies involved and the main fields of application can help identify the most suitable systems for managing grazing sheep and goats and contribute to selecting more sustainable and efficient solutions in line with current environmental and welfare concerns. Full article

With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. CIM represents an advanced urban management paradigm that integrates Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to establish a multidimensional digital framework for comprehensive urban data management and intelligent decision making. While the existing research has primarily focused on technical architectures, governance models, and application scenarios, a systematic exploration of CIM’s data-driven characteristics remains limited. This paper reviews the evolution of CIM from a data-centric view introducing a research framework that systematically examines the data lifecycle, including acquisition, processing, analysis, and decision support. Furthermore, it explores the application of CIM in key areas such as smart transportation and digital twin cities, emphasizing its deep integration with big data, artificial intelligence (AI), and cloud computing to enhance urban governance and intelligent services. Despite its advancements, CIM faces critical challenges, including data security, privacy protection, and cross-sectoral data sharing. This survey highlights these limitations and points out the future research directions, including adaptive data infrastructure, ethical frameworks for urban data governance, intelligent decision-making systems leveraging multi-source heterogeneous data, and the integration of CIM with emerging technologies such as AI and blockchain. These innovations will enhance CIM’s capacity to support intelligent, resilient, and sustainable urban development. By establishing a theoretical foundation for CIM as a data-intensive framework, this survey provides valuable insights and forward-looking guidance for its continued research and practical implementation. Full article

Background: The rapid expansion of telemedicine globally, especially during the COVID-19 pandemic, has been critical for maintaining the continuity of chronic care, including in low- and middle-income countries (LMICs). In the context of maintaining health services during major hazards, telemedicine offers a potential solution for reducing the impact of associated disruptions and maintaining the functionality of hospitals. This study aims to evaluate the application of telemedicine for chronic diseases in LMICs during COVID-19, with a focus on its role in enhancing health system resilience during disastrous events. Methods: A systematised review was conducted by searching PubMed, Scopus, Global Health, and Google Scholar for primary literature published between January 2020 and July 2023. English-language articles on chronic disease management were targeted; they were freely accessible and excluded abstracts, conference papers, posters, and grey literature. A multilevel evaluation framework was applied, covering access, cost, patient and health worker experiences, and the effectiveness of telemedicine interventions. Results: After screening one thousand six hundred seventy-eight records, twenty-three studies and two additional snowball-sourced papers from ten countries were included. Findings revealed that while telemedicine enhanced access to care, patient experiences, and effectiveness, cost analysis remains an understudied area. Discrepancies in perspectives were noted between patients and health workers, particularly regarding access and effectiveness. Nevertheless, the majority of studies agree on telemedicine’s positive impact on the accessibility and resilience of health systems during major emergencies, which reduces costs and improves the overall patient experience. However, concerns such as outdated regulations and policies and poor internet connectivity pose a challenge that needs to be addressed. Conclusions: This review highlights the potential of telemedicine in strengthening health system resilience, particularly in LMICs where more work is needed to update regulations and policies and to strengthen infrastructure for more affordable and uninterruptable connectivity. Further research is needed to explore the long-term sustainability of telemedicine in these contexts and to identify strategies for successful implementation across diverse public health challenges. Full article

Effective investment decision-making in today’s volatile financial market demands the integration of advanced predictive analytics, alternative data sources, and behavioral insights. This paper introduces an innovative Internet of Behaviors (IoB) ecosystem that integrates real-time data acquisition, advanced feature engineering, predictive modeling, explainability, automated portfolio management, and an intelligent decision support engine to enhance financial decision-making. Our framework effectively captures complex temporal dependencies in financial data by combining robust technical indicators and sentiment-driven metrics—derived from BERT-based sentiment analysis—with a multi-layer LSTM forecasting model. To enhance the model’s performance and transparency and foster user trust, we apply XAI methods, namely, TimeSHAP and TIME. The IoB ecosystem also proposes a portfolio management engine that translates the predictions into actionable strategies and a continuous feedback loop, enabling the system to adapt and refine its strategy in real time. Empirical evaluations demonstrate the effectiveness of our approach: the LSTM forecasting model achieved an RMSE of 0.0312, an MAE of 0.0250, an MSE of 0.0010, and a directional accuracy of 95.24% on TSLA stock returns. Furthermore, the portfolio management algorithm successfully transformed an initial balance of USD 15,000 into a final portfolio value of USD 21,824.12, yielding a net profit of USD 6824.12. These results highlight the potential of IoB-driven methodologies to revolutionize financial services by enabling more personalized, transparent, and adaptive investment solutions. Full article

This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where exact optimization methods are not applicable. Ad hoc representation and search operators are applied to optimize relevant metrics from the point of view of both system administrators and users. In the evaluation of problem instances built using real data, the evolutionary multiobjective optimization approach was able to compute more accurate solutions in terms of cost and quality of service when compared to the exact resolution method. The obtained results represent an improvement over greedy heuristics from 47.6% to 93.3% in terms of cost while maintaining competitive quality of service. In addition, the computed solutions had different tradeoffs between the problem objectives. This can provide different options for content distribution network design, allowing for a fast configuration that fulfills specific quality of service demands. Full article