Internet of Things最新文献

{"title":"Adaptation in Smart Home Automation Systems: A systematic review of decision-making and interaction","authors":"Jordan Rey-Jouanchicot ,&nbsp;Eric Campo ,&nbsp;Jean-Léon Bouraoui ,&nbsp;André Bottaro ,&nbsp;Nadine Vigouroux ,&nbsp;Frédéric Vella","doi":"10.1016/j.iot.2025.101588","DOIUrl":"10.1016/j.iot.2025.101588","url":null,"abstract":"<div><div>Smart home automation systems have gained in popularity recently due to the advent of the Internet of Things. These systems offer homeowners convenience, comfort, and energy efficiency using various devices such as thermostats and voice assistants. A key factor in the success of these systems in the future will be their ability to make effective decisions and seamlessly interact with users and their surroundings. However, as technology continues to advance, it is essential to investigate how these systems can adapt, especially in terms of decision-making and interaction. This work presents a systematic review of the literature on adaptivity in smart home automation systems, focusing on general-purpose and comfort use cases.</div><div>The review explores and discusses various proposals and approaches to adaptation, with a specific emphasis on the use of artificial intelligence. The aim is to provide an overview of existing approaches and highlight recent research contributions. It also discusses limitations, challenges, and emerging trends in decision-making systems for smart homes. Finally, it suggests future research directions to improve their adaptivity.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101588"},"PeriodicalIF":6.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AS-MPCA: An adaptive scheduling algorithm for industrial Internet of Things based on multi-population co-evolution","authors":"Anying Chai ,&nbsp;Lei Wang ,&nbsp;Chenyang Guo ,&nbsp;Mingshi Li ,&nbsp;Wanda Yin ,&nbsp;Zhaobo Fang","doi":"10.1016/j.iot.2025.101596","DOIUrl":"10.1016/j.iot.2025.101596","url":null,"abstract":"<div><div>The Industrial Internet of Things (IIoT) enables real-time data collection, analysis, and decision-making by tightly connecting physical devices, sensors, control systems, and information systems. Meanwhile, in the communication environment of industrial parks, a multi-dimensional heterogeneous information sensing network is constructed by sensor networks, shop floor Ethernet, field buses, etc. In this kind of network, multi-source heterogeneous data types are complex and diverse, the data scale is huge, and all kinds of data flows have different requirements for transmission volume and real-time performance. Especially in a communication environment with limited network resources, the transmission delay of real-time service data makes it difficult to meet the actual production requirements. These lead to problems such as low real-time and insufficient reliability of sensory data transmission. To address these problems, we propose an Adaptive Scheduling Algorithm for the Industrial Internet of Things Based on Multi-swarm Co-evolution(AS-MPCA). The algorithm combines a two-stage multiple swarm genetic algorithm with an adaptive routing mechanism. Firstly, the two-stage multiple swarm genetic algorithm expands the search space and enhances the diversity of the scheduling scheme through the combination of global search and multiple swarm strategies, which provides diversified path selection strategies for the adaptive routing mechanism. Then, the adaptive routing mechanism dynamically adjusts the optimal path according to the scheduling results of the above genetic algorithm. Simulation and experimental results demonstrate that the proposed method significantly enhances system schedulability. Compared with traditional algorithms, the proposed algorithm improves the task acceptance rate by an average of 10% across various conditions, effectively reduces the transmission delay of time-sensitive data, and ensures the quality of service for industrial IoT communication systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101596"},"PeriodicalIF":6.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing waste management with integrated AIoT, edge computing, and LoRaWAN communication technologies","authors":"Abdelaziz Daas ,&nbsp;Bilal Sari ,&nbsp;Fouzi Semchedine ,&nbsp;Mourad Amad","doi":"10.1016/j.iot.2025.101546","DOIUrl":"10.1016/j.iot.2025.101546","url":null,"abstract":"<div><div>This work presents <strong>Smart EcoRecycler Manager</strong>, an integrated waste management system designed to address inefficiencies in traditional recycling through automation and user engagement. The system combines a smart bin with AI-driven waste sorting, low-power wireless communication (LoRaWAN), and a user-friendly mobile app to incentivize recycling. Key innovations include: <strong>AI-powered waste classification</strong> achieving 99% accuracy for plastic and metal sorting, enabled by machine learning on low-cost edge devices (ESP32-CAM), <strong>Real-time optimization</strong> of waste collection routes, reducing operational costs compared to conventional methods, A <strong>gamified rewards system</strong> that boosts user participation through redeemable points, addressing low recycling rates.</div><div>The system uniquely integrates edge computing for real-time processing, LoRaWAN for long-range communication, and cloud platforms (Firebase) for scalable data management. Performance testing demonstrates significant improvements in waste segregation accuracy, cost efficiency, and user engagement. By combining these features, our solution addresses critical gaps in existing systems, such as limited scalability, high energy consumption, and poor user incentives. This work advances smart waste management by providing a practical, low-cost framework suitable for urban and remote areas alike, with measurable environmental and economic benefits.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101546"},"PeriodicalIF":6.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EVACUSCAPE: Internet of Things-enabled emergency evacuation based on matching theory","authors":"Joshua R. Atencio ,&nbsp;Md Sadman Siraj ,&nbsp;Eirini Eleni Tsiropoulou","doi":"10.1016/j.iot.2025.101581","DOIUrl":"10.1016/j.iot.2025.101581","url":null,"abstract":"<div><div>The effective evacuation during disaster scenarios, either physical or man-made, is critical in order to ensure the safety and survival of the victims, minimize the casualties, and facilitate the rapid and organized movement of people to safety. In this paper, the EVACUSCAPE model is introduced to optimize the matching between victims and evacuation routes during an evacuation process. Initially, the characteristics of both the victims and the evacuation routes are analyzed to establish a foundational matching mechanism among them. The Approximate EVACUSCAPE algorithm is developed to perform an initial matching between the victims and the evacuation routes by disregarding externalities that influence the victims’ decisions, such as the actions of other evacuees. Then, the Accurate EVACUSCAPE algorithm refines the matching process by incorporating the principles of coalition games to account for these externalities and ultimately derive an optimal and stable evacuation strategy. A comprehensive evaluation using real-world datasets demonstrates the effectiveness and robustness of the EVACUSCAPE model, which significantly outperforms conventional evacuation strategies where the victims select routes based solely on proximity or time-optimization in a selfish manner.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101581"},"PeriodicalIF":6.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Explainable Artificial Intelligence empowered energy efficient indoor localization framework for smart buildings","authors":"Zeynep Turgut","doi":"10.1016/j.iot.2025.101586","DOIUrl":"10.1016/j.iot.2025.101586","url":null,"abstract":"<div><div>The indoor localization problem remains a prominent and extensively debated area of research, lacking a universally accepted solution, especially within the context of smart buildings. A major concern revolves around the energy consumption associated with indoor localization systems. This study presents a proposed framework for an energy-efficient indoor localization system designed for smart buildings. The approach focuses on a fingerprinting indoor localization technique that involves constructing a signal map. To address challenges arising from distinct signal effects and the environment-specific structure of signal maps, the study introduces a framework incorporating an adaptive filter selection scheme. This scheme includes Kalman, particle, and Savitzky–Golay filters in the pre-processing stage to enhance the signal map. Rather than resorting to additional hardware for improved localization accuracy, the study advocates for optimizing the signal map to minimize energy consumption. Additionally, the research emphasizes the selection of effective features for machine learning techniques to enhance performance and boost localization accuracy. The findings are subjected to analysis using Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) Explainable Artificial Intelligence (XAI) models. The investigation delves into the impact of each signal and filter on positioning estimation, providing a comprehensive understanding of the system’s functionality.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101586"},"PeriodicalIF":6.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-augmented multi-agent collaboration for Smart Industrial Internet of Things task offloading","authors":"Yihang Wang ,&nbsp;Shengchao Su ,&nbsp;Yiwang Wang","doi":"10.1016/j.iot.2025.101572","DOIUrl":"10.1016/j.iot.2025.101572","url":null,"abstract":"<div><div>The integration of Multi-access Edge Computing (MEC) technology within the Smart Industrial Internet of Things (SIIoT) ecosystem can significantly enhance both computational and storage capabilities. This advancement facilitates improved data processing and a more efficient utilization of resources in industrial applications. However, the high density of devices typical of SIIoT environments often presents several challenges, including a low success rate for task offloading, increased latency, higher energy consumption, and the risk of overloading edge servers. This paper addresses these challenges by treating Smart Devices (SDs) as agents and proposing a collaborative multi-agent task offloading strategy. A computational offloading model has been developed to minimize delayed energy consumption, which is then formulated as a Multi-Agent Partially Observable Markov Decision Process (MAPOMDP) featuring a hybrid action space composed of discrete and continuous elements. An attention mechanism is introduced to tackle the complex competition for edge server resources among SDs during the offloading process, enabling the observation of the actions and states of other devices within the system. A Prioritized Experience Replay (PER) mechanism is employed to optimize the training process. A Multi-Agent Attention Deep Reinforcement Learning (MA2DRL) algorithm is proposed to improve computational task offloading. Experimental results demonstrate that the proposed algorithm outperforms other comparative algorithms regarding task offloading latency, average energy consumption, offloading success rate, and server load variance.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101572"},"PeriodicalIF":6.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An effective IoT interface considering an eye-tracking method for autonomous vehicle","authors":"Junghoon Park","doi":"10.1016/j.iot.2025.101583","DOIUrl":"10.1016/j.iot.2025.101583","url":null,"abstract":"<div><div>The number of Internet-connected devices is steadily increasing, exceeding 25 billion globally and projected to surpass 50 billion about 6.5 times the world population. At the core of IoT is data collection via sensors, forming big data for AI-driven analysis, optimization, and visualization. A convenient control environment is essential for devices like autonomous vehicles, requiring new interfaces such as touchless eye movement. This research proposes a real-time eye-tracking method using a single web camera, easily installed in cars and integrated with IoT. The system detects gaze by recognizing iris shape, enabling software-only tracking without extra hardware. Experiments show a mean absolute error (MAE) of 3.49°, ensuring accuracy even with head movement. Unlike existing infrared (IR) LED or head-mounted methods, this approach offers a cost-effective, real-time solution. Using lightweight image processing instead of deep learning, the system achieves real-time tracking with low latency, making it ideal for low-power IoT and autonomous vehicles. It is expected to become a next-generation input interface for these applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101583"},"PeriodicalIF":6.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeMiRaR-6T: A new defense method for detecting and mitigating rank attacks in RPL-based 6TiSCH networks","authors":"Hakan Aydin,&nbsp;Burak Aydin,&nbsp;Sedat Gormus","doi":"10.1016/j.iot.2025.101582","DOIUrl":"10.1016/j.iot.2025.101582","url":null,"abstract":"<div><div>The Industrial Internet of Things (IIoT) has revolutionized the industrial sector with advanced automation and connectivity. IIoT applications widely implement the IPv6 over the Time Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4e (6TiSCH) protocol in conjunction with the Routing Protocol for Low-Power and Lossy Networks (RPL) to ensure reliable communication. However, the RPL protocol is vulnerable to security attacks that target network topology, traffic, and node resources. These attacks pose significant risks to the integrity and reliability of IIoT systems, particularly rank attacks, in which malicious nodes manipulate rank values to disrupt communication. In order to enhance the security of IIoT technology, this study introduces DeMiRaR-6T, a method that effectively detects and mitigates rank attacks in 6TiSCH networks. DeMiRaR-6T relies on two key components. First, it incorporates a monitoring mechanism that continuously tracks node behavior, analyzing network activities to identify abnormal patterns indicative of rank attacks. Second, it utilizes a centralized authority (Join Registrar/Coordinator, Rank Control Unit) to maintain and disseminate an updated list of attacker nodes. This trusted entity collects information about malicious nodes and notifies other network participants, enabling them to adapt their communication strategies and take preventive actions. Through this comprehensive approach, DeMiRaR-6T enhances the security of RPL-based 6TiSCH networks. Experimental results demonstrate that under attack conditions, DeMiRaR-6T achieves up to a 12% increase in packet delivery rate and a 20% improvement in throughput compared to state-of-the-art methods. Additionally, notable enhancements are observed in control packet overhead, end-to-end delay, and energy consumption.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101582"},"PeriodicalIF":6.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A holistic evaluation of success factors for advancing IoT adoption through mitigating barriers in construction site safety","authors":"Mohamed Elrifaee ,&nbsp;Tarek Zayed ,&nbsp;Ali Hassan Ali ,&nbsp;Abdelazim Ibrahim ,&nbsp;Roy Dong Wang","doi":"10.1016/j.iot.2025.101595","DOIUrl":"10.1016/j.iot.2025.101595","url":null,"abstract":"<div><div>The construction industry is characterized by persistently high accident rates and pervasive hazards, highlighting a critical need for innovative safety interventions. One promising solution is the integration of the Internet of Things (IoT), which offers capabilities such as real-time data analysis, early hazard detection, and rigorous enforcement of safety protocols. However, despite its considerable potential, the widespread implementation of IoT technologies in construction remains limited due to significant barriers, including high financial costs, technical integration challenges, and resistance to change. While previous studies have focused on identifying barriers, they have overlooked critical success factors (CSFs) and best practices necessary for effective IoT adoption. This gap hinders widespread implementation, leaving construction sites vulnerable to preventable accidents. This study addresses this gap by identifying CSFs that simplify IoT adoption and reduce barriers, offering a unified framework for stakeholders. A four-phase methodology was employed: a systematic literature review to identify CSFs and barriers, a questionnaire survey to collect data from industry and academic professionals, and partial least squares-structural equation modeling (PLS-SEM) to analyze the relationships between CSFs and barriers. The results reveal a moderate correlation (R² = 0.137), demonstrating that CSFs such as stakeholder collaboration, cost-effective implementation strategies, and workforce training significantly mitigate adoption barriers. For instance, integrating IoT with existing safety protocols and providing targeted training programs can enhance adoption rates. These findings enable governments and construction organizations to develop actionable strategies, such as incentivizing IoT investments, fostering public-private partnerships, and standardizing implementation guidelines. By addressing barriers and leveraging CSFs, this research provides a pathway for improved IoT adoption, ultimately enhancing construction site safety and reducing accident rates. The study's practical insights and best practices offer valuable guidance for construction companies, policymakers, and technology developers, paving the way for safer and more efficient construction environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101595"},"PeriodicalIF":6.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MmWave beam path blockage prevention through codebook value prediction under domain shift","authors":"Bram van Berlo,&nbsp;Tanir Ozcelebi,&nbsp;Nirvana Meratnia","doi":"10.1016/j.iot.2025.101584","DOIUrl":"10.1016/j.iot.2025.101584","url":null,"abstract":"<div><div>Use of millimeter and terahertz spectra for communication is very sensitive to obstacles blocking signal beam paths. Beam angle codebook values can be adapted to control beam operation angles for blockage prevention, but this requires prediction of beam paths that are blocked. The performance of the prediction pipeline may be affected by domain factors such as physical characteristics of an operation environment and a specific blocker. This can be illustrated by artificially introducing domain factor shifts between training and test data subsets where a specific domain factor is left out of the training subset. Our experiments reveal significant performance drops in the blockage prediction performance on left-out test subset folds that contain all the samples of a specific domain factor. Thus, the prediction pipeline must employ effective domain shift mitigation techniques to attain consistent prediction performance in different domains. Pipeline performance should be supported by logical input data to prediction causation. We quantify causation by means of Shapley importance values with input regions attributable to signal aspects such as linear array antennas. Shapley importance results show high neural network prediction confidence value affection for amplitude variance and a limited set of subsequent fast-time blocks. Random inductively biased convolutions affection differs in a limited number of spatially separated antennas causing affection. Equally high prediction confidence value affection is assumed for iterative component search due to internal extraction mechanics and time complexity increases when zero-masking input data regions. We link equally high assumed prediction confidence value affection for iterative component search to highly logical IF signal to prediction causation. The affection of amplitude variance and a limited set of subsequent fast-time blocks shows weaker causation, still considered logical if the neural network can separate observations in representation distributions for varying distance and angle combination sets. The random inductively biased convolutions show illogical causation. They rely on direct IF signal features. Affection by a limited number of antennas indicates reliance on features with inadequate separation ability along angles at appropriate resolution.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101584"},"PeriodicalIF":6.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}