Internet of Things最新文献

{"title":"Video streaming on fog and edge computing layers: A systematic mapping study","authors":"André Luiz S. de Moraes ,&nbsp;Douglas D.J. de Macedo ,&nbsp;Laércio Pioli Junior","doi":"10.1016/j.iot.2024.101359","DOIUrl":"10.1016/j.iot.2024.101359","url":null,"abstract":"<div><div>Video streaming has become increasingly dominant in internet traffic and daily applications, significantly influenced by emerging technologies such as autonomous cars, augmented reality, and immersive videos. The computing community has extensively discussed aspects like latency, device power consumption, 5G, and computing. The advent of 6G technology, an emerging communication paradigm beyond existing technologies, promises to revolutionize these areas with enhanced bandwidth, reduced latency, and advanced connectivity features. Fog and Edge Computing environments intensify data generation, control, and analysis at the network edge. Consequently, adopting metrics such as QoE (Quality of Experience) and QoS (Quality of Service) is crucial for developing adaptive streaming services that dynamically adjust video quality based on network conditions. This work systematically maps the literature on video streaming approaches in Fog and Edge Computing that utilize QoS and QoE metrics to evaluate performance in managing Live Streaming and Streaming on Demand. The results highlight the most used metrics and discuss resource management strategies, providing valuable insights for developing new approaches and enhancing existing communication protocols like DASH (Dynamic Adaptive Streaming over HTTP) and HLS (HTTP Live Streaming).</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101359"},"PeriodicalIF":6.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310850","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":"Empirical evaluation of feature selection methods for machine learning based intrusion detection in IoT scenarios","authors":"José García,&nbsp;Jorge Entrena,&nbsp;Álvaro Alesanco","doi":"10.1016/j.iot.2024.101367","DOIUrl":"10.1016/j.iot.2024.101367","url":null,"abstract":"<div><div>This paper delves into the critical need for enhanced security measures within the Internet of Things (IoT) landscape due to inherent vulnerabilities in IoT devices, rendering them susceptible to various forms of cyber-attacks. The study emphasizes the importance of Intrusion Detection Systems (IDS) for continuous threat monitoring. The objective of this study was to conduct a comprehensive evaluation of feature selection (FS) methods using various machine learning (ML) techniques for classifying traffic flows within datasets containing intrusions in IoT environments. An extensive benchmark analysis of ML techniques and FS methods was performed, assessing feature selection under different approaches including Filter Feature Ranking (FFR), Filter-Feature Subset Selection (FSS), and Wrapper-based Feature Selection (WFS). FS becomes pivotal in handling vast IoT data by reducing irrelevant attributes, addressing the curse of dimensionality, enhancing model interpretability, and optimizing resources in devices with limited capacity. Key findings indicate the outperformance for traffic flows classification of certain tree-based algorithms, such as J48 or PART, against other machine learning techniques (naive Bayes, multi-layer perceptron, logistic, adaptive boosting or k-Nearest Neighbors), showcasing a good balance between performance and execution time. FS methods' advantages and drawbacks are discussed, highlighting the main differences in results obtained among different FS approaches. Filter-feature Subset Selection (FSS) approaches such as CFS could be more suitable than Filter Feature Ranking (FFR), which may select correlated attributes, or than Wrapper-based Feature Selection (WFS) methods, which may tailor attribute subsets for specific ML techniques and have lengthy execution times. In any case, reducing attributes via FS has allowed optimization of classification without compromising accuracy. In this study, F1 score classification results above 0.99, along with a reduction of over 60% in the number of attributes, have been achieved in most experiments conducted across four datasets, both in binary and multiclass modes. This work emphasizes the importance of a balanced attribute selection process, taking into account threat detection capabilities and computational complexity.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101367"},"PeriodicalIF":6.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003081/pdfft?md5=2c59c06adc897db3e81bd94a83f7572e&pid=1-s2.0-S2542660524003081-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315585","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}

{"title":"ASAP: A lightweight authenticated secure association protocol for IEEE 802.15.6 based medical BAN","authors":"Walid I. Khedr ,&nbsp;Aya Salama ,&nbsp;Marwa M. Khashaba ,&nbsp;Osama M. Elkomy","doi":"10.1016/j.iot.2024.101363","DOIUrl":"10.1016/j.iot.2024.101363","url":null,"abstract":"<div><p>Medical Body Area Networks (MBANs), a specialized subset of Wireless Body Area Networks (WBANs), are crucial for enabling medical data collection, processing, and transmission. The IEEE 802.15.6 standard governs these networks but falls short in practical MBAN scenarios. This paper introduces ASAP, a Lightweight Authenticated Secure Association Protocol integrated with IEEE 802.15.6. ASAP prioritizes patient privacy with randomized node ID generation and temporary shared keys, preventing node tracking and privacy violations. It optimizes network performance by consolidating Master Keys (MK), Pairwise Temporal Keys (PTK), and Group Temporal Keys (GTK) creation into a unified process, ensuring the efficiency of the standard four-message association protocol. ASAP enhances security by eliminating the need for pre-shared keys, reducing the attack surface, and improving forward secrecy. The protocol achieves mutual authentication without pre-shared keys or passwords and supports advanced cryptographic algorithms on nodes with limited processing capabilities. Additionally, it imposes connection initiation restrictions, requiring valid certificates for nodes, thereby addressing gaps in IEEE 802.15.6. Formal verification using Verifpal confirms ASAP's resilience against various attacks. Implementation results show ASAP's superiority over standard IEEE 802.15.6 protocols, establishing it as a robust solution for securing MBAN communications in medical environments.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101363"},"PeriodicalIF":6.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233832","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":"Exploring the boundaries of energy-efficient Wireless Mesh Networks with IEEE 802.11ba","authors":"Roger Sanchez-Vital,&nbsp;Carles Gomez,&nbsp;Eduard Garcia-Villegas","doi":"10.1016/j.iot.2024.101366","DOIUrl":"10.1016/j.iot.2024.101366","url":null,"abstract":"<div><p>In traditional IoT applications, energy saving is essential while high bandwidth is not always required. However, a new wave of IoT applications exhibit stricter requirements in terms of bandwidth and latency. Broadband technologies like Wi-Fi could meet such requirements. Nevertheless, these technologies come with limitations: high energy consumption and limited coverage range. In order to address these two shortcomings, and based on the recent IEEE 802.11ba amendment, we propose a Wi-Fi-based mesh architecture where devices are outfitted with a supplementary Wake-up Radio (WuR) interface. According to our analytical and simulation studies, this design maintains latency figures comparable to conventional single-interface networks while significantly reducing energy consumption (by up to almost two orders of magnitude). Additionally, we verify via real device measurements that battery lifetime can be increased by as much as 500% with our approach.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101366"},"PeriodicalIF":6.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S254266052400307X/pdfft?md5=bb82afe0042ffeccf2459f8320a44178&pid=1-s2.0-S254266052400307X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147574","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}

{"title":"Combining Multi-Agent Systems and Artificial Intelligence of Things: Technical challenges and gains","authors":"Pedro Hilario Luzolo ,&nbsp;Zeina Elrawashdeh ,&nbsp;Igor Tchappi ,&nbsp;Stéphane Galland ,&nbsp;Fatma Outay","doi":"10.1016/j.iot.2024.101364","DOIUrl":"10.1016/j.iot.2024.101364","url":null,"abstract":"&lt;div&gt;&lt;p&gt;A Multi-Agent System (MAS) usually refers to a network of autonomous agents that interact with each other to achieve a common objective. This system is therefore composed of several software components or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems.&lt;/p&gt;&lt;p&gt;MAS is not a substitute or rival for Artificial Intelligence (AI). Instead, AI techniques can be integrated within agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous.&lt;/p&gt;&lt;p&gt;The Internet of Things (IoT) and AI are two technologies that have been applied for a long time to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Smart behavior, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of AI and IoT, known as AIoT, allows these electronic devices to make more intelligent, autonomous, and automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze these data to derive valuable insights and make informed decisions. The authors devoted their efforts to the critical analysis of AIoT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, &lt;em&gt;the contribution of the authors is in the formulation of innovative research directions, which outline a clear guide for researchers and professionals in the expansion of knowledge in AIoT integration. The results of the research are significant contributions to the continuous advance of the area, enriching the understanding of the challenges and boosting the development of solutions and strategies in this technological convergence&lt;/em&gt;. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results, the research directions are: (&lt;em&gt;i&lt;/em&gt;) Development of a methodology showing how to integrate the different applications independently of the scenarios in which they are deployed. Additionally, elaboration of the tools used in the integration process. (&lt;em&gt;ii&lt;/em&gt;) Deployment of an agent in a microprocessor. (&lt;em&gt;iii","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101364"},"PeriodicalIF":6.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147576","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":"TinyWolf — Efficient on-device TinyML training for IoT using enhanced Grey Wolf Optimization","authors":"Subhrangshu Adhikary ,&nbsp;Subhayu Dutta ,&nbsp;Ashutosh Dhar Dwivedi","doi":"10.1016/j.iot.2024.101365","DOIUrl":"10.1016/j.iot.2024.101365","url":null,"abstract":"<div><p>Training a deep learning model generally requires a huge amount of memory and processing power. Once trained, the learned model can make predictions very fast with very little resource consumption. The learned weights can be fitted into a microcontroller to build affordable embedded intelligence systems which is also known as TinyML. Although few attempts have been made, the limits of the state-of-the-art training of a deep learning model within a microcontroller can be pushed further. Generally deep learning models are trained with gradient optimizers which predict with high accuracy but require a very high amount of resources. On the other hand, nature-inspired meta-heuristic optimizers can be used to build a fast approximation of the model’s optimal solution with low resources. After a rigorous test, we have found that Grey Wolf Optimizer can be modified for enhanced uses of main memory, paging and swap space among <span><math><mrow><mi>α</mi><mo>,</mo><mspace></mspace><mi>β</mi><mo>,</mo><mspace></mspace><mi>δ</mi></mrow></math></span> and <span><math><mi>ω</mi></math></span> wolves. This modification saved up to 71% memory requirements compared to gradient optimizers. We have used this modification to train the TinyML model within a microcontroller of 256KB RAM. The performances of the proposed framework have been meticulously benchmarked on 13 open-sourced datasets.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101365"},"PeriodicalIF":6.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003068/pdfft?md5=ab42e32e095597b7bee6c567498b913a&pid=1-s2.0-S2542660524003068-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147573","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}

{"title":"DISFIDA: Distributed Self-Supervised Federated Intrusion Detection Algorithm with online learning for health Internet of Things and Internet of Vehicles","authors":"Erol Gelenbe ,&nbsp;Baran Can Gül ,&nbsp;Mert Nakıp","doi":"10.1016/j.iot.2024.101340","DOIUrl":"10.1016/j.iot.2024.101340","url":null,"abstract":"<div><p>Networked health systems are often the victims of cyberattacks with serious consequences for patients and healthcare costs, with the Internet of Things (IoT) being an additional prime target. In future systems we can imagine that the Internet of Vehicles (IoV) will also be used for conveying patients for diagnosis and treatment in an integrated manner. Thus the medical field poses very significant and specific challenges since even for a single patient, several providers may carry out tests or offer healthcare services, and may have distinct interconnected sub-contractors for services such as ambulances and connected cars, connected devices or temporary staff providers, that have distinct confidentiality requirements on top of possible commercial competition. On the other hand, these distinct entities can be subject to similar or coordinated attacks, and could benefit from each others’ cybersecurity experience to better detect and mitigate cyberattacks. Thus the present work proposes a novel Distributed Self-Supervised Federated Intrusion Detection Algorithm (DISFIDA), with Online Self-Supervised Federated Learning, that uses Dense Random Neural Networks (DRNN). In DISFIDA learning data is private, and neuronal weights are shared among Federated partners. Each partner in DISFIDA combines its synaptic weights with those it receives other partners, with a preference for those weights that have closer numerical values to its own weights which it has learned on its own. DISFIDA is tested with three open-access datasets against five benchmark methods, for two relevant IoT healthcare applications: networks of devices (e.g., body sensors), and Connected Smart Vehicles (e.g., ambulances that transport patients). These tests show that the DISFIDA approach offers 100% True Positive Rate for attacks (one percentage point better than comparable state of the art methods which attain 99%) so that it does better at detecting attacks, with 99% True Negative Rate similar to state-of-the-art Federated Learning, for Distributed Denial of Service (DDoS) attacks.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101340"},"PeriodicalIF":6.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002816/pdfft?md5=3f8cac47e530cac8c010a7b776652d64&pid=1-s2.0-S2542660524002816-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147571","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}

{"title":"Fine-grained vulnerability detection for medical sensor systems","authors":"Le Sun ,&nbsp;Yueyuan Wang ,&nbsp;Huiyun Li ,&nbsp;Ghulam Muhammad","doi":"10.1016/j.iot.2024.101362","DOIUrl":"10.1016/j.iot.2024.101362","url":null,"abstract":"<div><p>The Internet of Things (IoT) has revolutionized the healthcare system by connecting medical sensors to the internet, while also posing challenges to the security of medical sensor networks (MSN). Given the extreme sensitivity of medical data, any vulnerability may result in data breaches and misuse, impacting patient safety and privacy. Therefore, safeguarding MSN security is critical. As medical sensor devices rely on smart healthcare software systems for data management and communication, precisely detecting system code vulnerabilities is essential to ensuring network security. Effective software vulnerability detection targets two key objectives: (i) achieving high accuracy and (ii) directly identifying vulnerable code lines for developers to fix. To address these challenges, we introduce Vulcoder, a novel vulnerability-oriented, encoder-driven model based on the Bidirectional Encoder Representations from Transformers (BERT) architecture. We propose a one-to-one mapping function to capture code semantics through abstract syntax trees (AST). Combined with multi-head attention, Vulcoder achieves precise function- and line-level detection of software vulnerabilities in MSN. This accelerates the vulnerability remediation process, thereby strengthening network security. Experimental results on various datasets demonstrate that Vulcoder outperforms previous models in identifying vulnerabilities within MSN. Specifically, it achieves a 1%–419% improvement in function-level prediction F1 scores and a 12.5%–380% increase in line-level localization precision. Therefore, Vulcoder helps enhance security defenses and safeguard patient privacy in MSN, facilitating the development of smart healthcare.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101362"},"PeriodicalIF":6.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003032/pdfft?md5=ec517a1daef40dd544058b39166a1eae&pid=1-s2.0-S2542660524003032-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163601","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}

{"title":"Enhancing security of Internet of Robotic Things: A review of recent trends, practices, and recommendations with encryption and blockchain techniques","authors":"Ehsanul Islam Zafir ,&nbsp;Afifa Akter ,&nbsp;M.N. Islam ,&nbsp;Shahid A. Hasib ,&nbsp;Touhid Islam ,&nbsp;Subrata K. Sarker ,&nbsp;S.M. Muyeen","doi":"10.1016/j.iot.2024.101357","DOIUrl":"10.1016/j.iot.2024.101357","url":null,"abstract":"<div><p>The Internet of Robotic Things (IoRT) integrates robots and autonomous devices, transforming industries such as manufacturing, healthcare, and transportation. However, security vulnerabilities in IoRT systems pose significant challenges to data privacy and system integrity. To address these issues, encryption is essential for protecting sensitive data transmitted between devices. By converting data into ciphertext, encryption ensures confidentiality and integrity, reducing the risk of unauthorized access and data breaches. Blockchain technology also enhances IoRT security by offering decentralized, tamper-proof data storage solutions. By offering comprehensive insights, practical recommendations, and future directions, this paper aims to contribute to the advancement of knowledge and practice in securing interconnected robotic systems, thereby ensuring the integrity and confidentiality of data exchanged within IoRT ecosystems. Through a thorough examination of encryption requisites, scopes, and current implementations in IoRT, this paper provides valuable insights for researchers, engineers, and policymakers involved in IoRT security efforts. By integrating encryption and blockchain technologies into IoRT systems, stakeholders can foster a secure and dependable environment, effectively manage risks, bolster user confidence, and expedite the widespread adoption of IoRT across diverse sectors. The findings of this study underscore the critical role of encryption and blockchain technology in IoRT security enhancement and highlight potential avenues for further exploration and innovation. Furthermore, this paper suggests future research areas, such as threat intelligence and analytics, security by design, multi-factor authentication, and AI for threat detection. These recommendations support ongoing innovation in securing the evolving IoRT landscape.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101357"},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002981/pdfft?md5=a4332066960a6d42faa3e5a09581d2cb&pid=1-s2.0-S2542660524002981-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147575","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}

{"title":"CLARA: A cluster-based node correlation for sampling rate adaptation and fault tolerance in sensor networks","authors":"Hassan Harb ,&nbsp;Clara Abou Nader ,&nbsp;Ali Jaber ,&nbsp;Mourad Hakem ,&nbsp;Jean-Claude Charr ,&nbsp;Chady Abou Jaoude ,&nbsp;Chamseddine Zaki","doi":"10.1016/j.iot.2024.101345","DOIUrl":"10.1016/j.iot.2024.101345","url":null,"abstract":"<div><p>Recently, wireless sensor networks (WSNs) have been proven as an efficient and low-cost solution for monitoring various kind of applications. However, the massive amount of data collected and transmitted by the sensor nodes, which are mostly redundant, will quickly consume their limited battery power, which is sometimes difficult to replace or recharge. Although the huge efforts made by researchers to solve such problem, most of the proposed techniques suffer from their accuracy and their complexity, which is not suitable for limited-resources sensors. Therefore, designing new data reduction techniques to reduce the raw data collected in such networks is becoming essential to increase their lifetime. In this paper, we propose a CLuster-based node correlation for sAmpling Rate adaptation and fAult tolerance, abbreviated CLARA, mechanism dedicated to periodic sensor network applications. Mainly, CLARA works on two stages: node correlation and fault tolerance. The first stage introduces a data clustering method that aims to search the correlation among neighboring nodes. Then, it accordingly adapts their sensing frequencies in a way to reduce the amount of data collected in such networks while preserving the information integrity at the sink. In the second stage, a fault tolerance model is proposed that allows the sink to regenerate the raw sensor data based on two methods: moving average (MA) and exponential smoothing (ES). We demonstrated the efficiency of our technique through both simulations and experiments. The best obtained results show that the first stage can reduce the sensor sampling rate, and accordingly the sensor energy, up to 64% while the second stage can accurately regenerate the raw data with an error loss less than 0.15.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101345"},"PeriodicalIF":6.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142147680","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}