Internet of Things最新文献

{"title":"Securing SCADA systems in smart grids with IoT integration: A Self-Defensive Post-Quantum Blockchain Architecture","authors":"Mohammad Tazeem Naz,&nbsp;Wael Elmedany,&nbsp;Mazen Ali","doi":"10.1016/j.iot.2024.101381","DOIUrl":"10.1016/j.iot.2024.101381","url":null,"abstract":"<div><div>The rapid development of smart city infrastructures has brought increased attention to the security of critical systems such as Supervisory Control and Data Acquisition (SCADA) systems, which are central to smart grids. SCADA systems are highly susceptible to cyberattacks, particularly false data injection attacks that can lead to catastrophic failures. While blockchain technology has been explored as a means to secure SCADA systems, traditional blockchain models are vulnerable to the emerging threat of quantum computing, which can break classical cryptographic algorithms.</div><div>This paper proposes a Self-Defensive Post-Quantum Blockchain Architecture (SD-PQBA) specifically designed to protect SCADA systems within smart cities from both classical and quantum cyber threats. The SD-PQBA framework introduces a novel Proof of Derived Authority (PoDA) consensus mechanism and a post-quantum 3-key cryptography scheme that ensures the immutability and integrity of data in the SCADA system. By addressing the limitations of existing blockchain solutions in the context of quantum computing, this architecture provides a robust, future-proof layer of defense, enhancing the resilience of SCADA systems against advanced cyber attacks. This approach not only strengthens SCADA security but also highlights critical gaps in the literature at the intersection of quantum computing, SCADA, and blockchain technology.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101381"},"PeriodicalIF":6.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358845","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":"Load-balanced offloading of multiple task types for mobile edge computing in IoT","authors":"Ye Zhang ,&nbsp;Xingyun He ,&nbsp;Jin Xing ,&nbsp;Wuyungerile Li ,&nbsp;Winston K.G. Seah","doi":"10.1016/j.iot.2024.101385","DOIUrl":"10.1016/j.iot.2024.101385","url":null,"abstract":"<div><div>The continuous development of mobile networks poses new challenges for end devices with limited computing power. Mobile or multi-access edge computing (MEC) has been proposed for providing the computing resources close to the end devices that need them. However, in real network environments, MEC servers also have limited computing resources that need to be shared among many devices and efficient resource allocation is critical to ensure that the limited resources are optimally used. In view of this, we propose the Balanced Offload for Multi-type Tasks (BOMT) algorithm. The tasks to be offloaded are first prioritized according to their type, size and maximum tolerable delay; then different offloading algorithms are executed for different priority tasks according to the level of the priority and the current load on the MEC server. Following which, the optimal offloading policy is determined iteratively. Simulation results show that BOMT can effectively reduce system delay, increase user coverage and offload task completion rates.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101385"},"PeriodicalIF":6.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358844","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":"Computation and transmission adaptive semantic communication for reliability-guarantee image reconstruction in IoT","authors":"Chen Lin,&nbsp;Yijun Guo,&nbsp;Jianjun Hao,&nbsp;Zhilong Zhang","doi":"10.1016/j.iot.2024.101383","DOIUrl":"10.1016/j.iot.2024.101383","url":null,"abstract":"<div><div>Semantic communication can significantly compress source data, improving transmission efficiency. However, semantic communication systems under varying channel condition have not been well studied, especially for tasks that require reliability guarantee. This paper focus on the reliability-guarantee image reconstruction tasks, and study the computation and transmission adaptive semantic communication. First, a computation and transmission adaptive semantic communication (CTASC) system is proposed. It is able to adjust the computation load and transmission load of an image reconstruction task adaptively while guaranteeing the reconstruction reliability. Specifically, a semantic encoder with multiple convolutional neural network (CNN) slices with different network depths is designed to adjust the transmission load and computation load. Second, a joint computation and transmission resource allocation problem aimed at minimizing the maximum delay within system is formulated. To solve this problem, we decompose it into two nested sub-problems and propose a Simulated Annealing with Re-perturbation Mechanism (SA-RPM) algorithm and an Alternating Optimization (AO) algorithm to solve these sub-problems, respectively. Simulation results demonstrate that compared to variable code length enabled DeepJSCC (DeepJSCC-V), our system can achieve higher compression ratio(CR) with similar LPIPS performance. Simulation results also show that our resource allocation scheme can obtain an approaching value to the optimal maximum delay, with an average difference not exceeding 0.2%.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101383"},"PeriodicalIF":6.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358847","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 empirical investigation into the enhancement of decision-making capabilities in corporate sustainability leadership through Internet of Things (IoT) integration","authors":"Ming Yuan Hsieh","doi":"10.1016/j.iot.2024.101382","DOIUrl":"10.1016/j.iot.2024.101382","url":null,"abstract":"<div><div>This research investigates the impact of Internet of Things (IoT) applications on decision-making capacity (DMC) development in Corporate Sustainable Leadership (CSL). The research focuses on how IoT enhances Environmental, Social, and Governance (ESG) practices within organizations, addressing key sustainability challenges and supporting Sustainable Development Goals (SDGs). Through quantitative and qualitative analyses, the study identifies three primary findings: (1) IoT applications directly enhance power source management in the environmental domain, improving Business Strategy and Long-term Growth (BSLG) and Measurement and Reporting (M&amp;R) capabilities. This is achieved through real-time monitoring, smart building management, and predictive maintenance. In the social sphere, IoT strengthens supply chain transparency, bolstering Stakeholder Management (SM) through real-time tracking and blockchain-integrated systems for product authenticity verification. IoT comprehensively empowers performing risk management in the governance realm, improving Organizational Culture (OC) through early warning systems and predictive analytics for risk mitigation. (2) IoT integration facilitates Data-Driven Decision Making (DDDM), Real-Time Responsiveness (RTR), and Business Risk Management (BRM), key components of IoT-DMC. Furthermore, it supports the development of Effective Stakeholder Management (ESM), Resource Efficiency (RE), and Sustainable Competitive Outcomes (SCO) within IoT-CSL. (3) While highlighting the potential of IoT in advancing CSL, the research also acknowledges challenges such as initial investment costs, data privacy concerns, technological complexity, energy consumption of IoT devices, and electronic waste management. The study concludes that successful IoT implementation in CSL requires careful planning, robust data management, and a holistic approach considering both benefits and potential drawbacks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101382"},"PeriodicalIF":6.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327217","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":"A review of LoRaWAN performance optimization through cross-layer-based approach for Internet of Things","authors":"Melchizedek Alipio ,&nbsp;Carl Christian Chaguile ,&nbsp;Miroslav Bures","doi":"10.1016/j.iot.2024.101378","DOIUrl":"10.1016/j.iot.2024.101378","url":null,"abstract":"<div><div>The Internet of Things (IoT) is proliferating in technology and automation. Some popular IoT applications include environmental monitoring, home automation, agriculture, aquaculture, healthcare, transportation, and logistics. The construction of the IoT system is determined by where it will be used. The design can be tailor-made for short-range or long-range communication, rural or urban areas, indoor or outdoor applications, real-time or delay-tolerant communication, and much more. Low-Power Wide Area Networks (LPWAN) are frequently used for long-distance, energy-efficient, cost-effective communication. In LPWAN technology, Long-Range Wide Area Network (LoRaWAN) is one of the most popular choices because of its remarkable features and openness, making it highly suitable for IoT applications. However, despite its exceptional features, there are still ways to optimize the system to become more efficient and address several challenges during runtime. One possible way to address issues and challenges in LoRaWAN is through cross-layer optimization. This optimization technique violates the restrictions set by the Open System Interconnection (OSI) model and gives freedom to its protocol layers to communicate depending on the intended purpose. This paper surveys the state-of-the-art cross-layer approaches that optimize LoRaWAN for IoT applications. The cross-layer approaches were categorized according to the layer combinations and architecture. In addition, this paper provided observations on the effects of cross-layer optimization in LoRaWAN. Lastly, possible issues and solutions, challenges, and future directives from cross-layer optimization approaches were included.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101378"},"PeriodicalIF":6.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315596","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":"Enhancing real-time intrusion detection and secure key distribution using multi-model machine learning approach for mitigating confidentiality threats","authors":"Ju Lu ,&nbsp;Arindam Bhar ,&nbsp;Arindam Sarkar ,&nbsp;Abdulfattah Noorwali ,&nbsp;Kamal M. Othman","doi":"10.1016/j.iot.2024.101377","DOIUrl":"10.1016/j.iot.2024.101377","url":null,"abstract":"<div><div>Ensuring strong security measures against intrusions is of utmost importance in the ever-changing field of information management systems. Conventional Intrusion Detection Systems (IDS) frequently have difficulties in dealing with the ever-changing and intricate characteristics of contemporary cyber threats, particularly in the realm of the Internet of Things (IoT). The current body of research emphasizes the difficulties in attaining both high precision and real-time speed while still preserving the anonymity of data. This work tackles these concerns by presenting a scalable multi-model Machine Learning (ML) technique developed to improve real-time intrusion detection and ensure safe cryptographic key distribution. The suggested solution takes use of the widespread use of IoT devices, which increases the likelihood of advanced cyberattacks. Our approach involves implementing a ML-based automated IDS specifically designed for various IoT environments. These IDS enhance adaptability and accuracy. We also utilize Maximum–Minimum (Max–Min) normalization on the UNSW-NB15 and CICIoT2023 datasets to improve the accuracy of detecting intrusions. Furthermore, we classify a wide range of contemporary threats and typical internet traffic into nine distinct attack categories. To streamline data processing and improve system efficiency, we employ Principal Component Analysis (PCA) for dimensionality reduction. Additionally, we deploy six advanced ML models to optimize detection capabilities and accurately identify threats. Finally, we develop a secure key distribution mechanism using synchronized Artificial Neural Networks (ANNs). The process of mutual learning guarantees the secure distribution of keys among IoT networks, thus reducing the risks to secrecy. This novel methodology not only reinforces the ability to identify intrusions in real-time, but also improves the overall security stance of information management systems. This work significantly contributes to the field of digital security in information management by addressing the limits of current IDS solutions and presenting a complete, multi-faceted security strategy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101377"},"PeriodicalIF":6.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323743","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":"Adaptive Single-layer Aggregation Framework for Energy-efficient and Privacy-preserving Load Forecasting in Heterogeneous Federated Smart Grids","authors":"Habib Ullah Manzoor,&nbsp;Atif Jafri,&nbsp;Ahmed Zoha","doi":"10.1016/j.iot.2024.101376","DOIUrl":"10.1016/j.iot.2024.101376","url":null,"abstract":"<div><div>Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating data from distributed load networks while ensuring data privacy. However, the heterogeneous nature of smart grid load forecasting introduces significant challenges that current methods struggle to address, particularly for resource-constrained devices due to high computational and communication demands. To overcome these challenges, we propose a novel Adaptive Single Layer Aggregation (ASLA) framework tailored for resource-constrained smart grid networks. The ASLA framework mitigates data heterogeneity issues by focusing on local learning and incorporating partial updates from local devices for model aggregation in adaptive manner. It is optimized for resource-constrained environments through the implementation of a stopping criterion during model training and weight quantization. Our evaluation on two distinct datasets demonstrates that quantization results in a minimal loss function degradation of 0.01% for Data 1 and 1.25% for Data 2. Furthermore, local model layer optimization for aggregation achieves substantial communication cost reductions of 829.2-fold for Data 1 and 5522-fold for Data 2. The use of an 8-bit fixed-point representation for neural network weights leads to a 75% reduction in storage/memory requirements and decreases computational costs by replacing complex floating-point units with simpler fixed-point units. By addressing data heterogeneity and reducing storage, computation, and communication overheads, the ASLA framework is well-suited for deployment in resource-constrained smart grid networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101376"},"PeriodicalIF":6.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524003172/pdfft?md5=d25363c62faa252df41a3050d5c0712d&pid=1-s2.0-S2542660524003172-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310612","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 through continuous biometric authentication using wearable sensors","authors":"Laxmi Divya Chhibbar,&nbsp;Sujay Patni,&nbsp;Siddarth Todi,&nbsp;Ashutosh Bhatia,&nbsp;Kamlesh Tiwari","doi":"10.1016/j.iot.2024.101374","DOIUrl":"10.1016/j.iot.2024.101374","url":null,"abstract":"<div><div>The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101374"},"PeriodicalIF":6.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319855","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":"iLocator—A low cost IoT-based hybrid architecture for tracking and locating objects in indoor environments","authors":"Lucas Marquezan,&nbsp;Elmer A. Gamboa Peñaloza,&nbsp;Paulo J.D. de Oliveira Evald,&nbsp;Marlon M. Hernandez Cely,&nbsp;Marcelo L. Rossi,&nbsp;Sigmar de Lima","doi":"10.1016/j.iot.2024.101369","DOIUrl":"10.1016/j.iot.2024.101369","url":null,"abstract":"<div><div>The tracking of objects in external environments is well established in the literature. Various approaches are based on the data provided by satellite navigation systems. However, this type of technology does not offer the accuracy and precision needed for the location and tracking of objects inside buildings, factories, hospitals, or any other place that is not open-air. This paper presents a novel hybrid low-cost architecture for locating and tracking objects in indoor environments. The proposed solution is based on the concepts of the internet of things, modularity, and low energy consumption, aiming to enhance the usability of the tracking system by using only off-the-shelf components that are easily purchasable and inexpensive. The experimental results indicate high accuracy and the robustness in sending and receiving data, making it feasible to locate objects in indoor environments with obstacles.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101369"},"PeriodicalIF":6.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319856","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":"Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment","authors":"Khalid Mahmood ,&nbsp;Muhammad Asad Saleem ,&nbsp;Zahid Ghaffar ,&nbsp;Salman Shamshad ,&nbsp;Ashok Kumar Das ,&nbsp;Mohammed J.F. Alenazi","doi":"10.1016/j.iot.2024.101372","DOIUrl":"10.1016/j.iot.2024.101372","url":null,"abstract":"<div><div>The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101372"},"PeriodicalIF":6.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310610","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}