The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and ...The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.展开更多
The digital revolution era has impacted various domains,including healthcare,where digital technology enables access to and control of medical information,remote patient monitoring,and enhanced clinical support based ...The digital revolution era has impacted various domains,including healthcare,where digital technology enables access to and control of medical information,remote patient monitoring,and enhanced clinical support based on the Internet of Health Things(IoHTs).However,data privacy and security,data management,and scalability present challenges to widespread adoption.This paper presents a comprehensive literature review that examines the authentication mechanisms utilized within IoHT,highlighting their critical roles in ensuring secure data exchange and patient privacy.This includes various authentication technologies and strategies,such as biometric and multifactor authentication,as well as the influence of emerging technologies like blockchain,fog computing,and Artificial Intelligence(AI).The findings indicate that emerging technologies offer hope for the future of IoHT security,promising to address key challenges such as scalability,integrity,privacy and other security requirements.With this systematic review,healthcare providers,decision makers,scientists and researchers are empowered to confidently evaluate the applicability of IoT in healthcare,shaping the future of this field.展开更多
The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these netwo...The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these networks,as shared channels are used to transmit packets.In this paper,a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT.Initially,every device works collaboratively to form a distributed network.In this model,if a device is deployed outside the coverage area of the nearest server,it communicates indirectly through the neighboring devices.For this purpose,every device collects data from the respective neighboring devices,such as hop count,average packet transmission delay,criticality factor,link reliability,and RSSI value,etc.These parameters are used to find an optimal route from the source to the destination.Secondly,the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly.Moreover,these devices and server modules must ensure that every packet is transmitted securely,which is possible only if it is encrypted with an encryption algorithm.For this purpose,a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase.Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission.Moreover,the proposed approach has outperformed the existing approaches in terms of communication cost,processing overhead,end-to-end delay,packet loss ratio,and throughput.Finally,the proposed approach is adoptable in different networking infrastructures.展开更多
The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of ...The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.展开更多
Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concern...Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure, quantum-resistant blind signatures. In this paper, we introduce lattice-based forward-secure blind signature (LFSBS), a lattice-based forward-secure blind signature scheme for medical privacy preservation in BIoMT. LFSBS achieves forward security by constructing a key evolution mechanism using a binary tree structure. This mechanism ensures that even if future encryption keys are leaked, past data can still remain secure. Meanwhile, LFSBS realizes post-quantum security based on the hardness assumption of small integer solution (SIS), making it resistant to potential quantum computing attacks. In addition, we formally define and prove the security of LFSBS in a random oracle model, including blindness and forward-secure unforgeability. Comprehensive performance evaluation shows that LFSBS performs well in terms of computational overhead, with a reduction of 22%–73% compared to previous schemes.展开更多
The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These in...The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the critical issues, which is the prediction of fraudulent devices, i.e., adversaries, preferably as early as possible in the IoT. In this paper, a hybrid communication mechanism is presented where the Hidden Markov Model (HMM) predicts the legitimacy of the requesting device (both source and destination), and the Advanced Encryption Standard (AES) safeguards the reliability of the transmitted data over a shared communication medium, preferably through a secret shared key, i.e., , and timestamp information. A device becomes trusted if it has passed both evaluation levels, i.e., HMM and message decryption, within a stipulated time interval. The proposed hybrid, along with existing state-of-the-art approaches, has been simulated in the realistic environment of the IoT to verify the security measures. These evaluations were carried out in the presence of intruders capable of launching various attacks simultaneously, such as man-in-the-middle, device impersonations, and masquerading attacks. Moreover, the proposed approach has been proven to be more effective than existing state-of-the-art approaches due to its exceptional performance in communication, processing, and storage overheads, i.e., 13%, 19%, and 16%, respectively. Finally, the proposed hybrid approach is pruned against well-known security attacks in the IoT.展开更多
The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Syst...The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Systems(IDS)often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems.To address these challenges,we propose an innovative privacy-preserving approach leveraging Federated Learning(FL)for distributed intrusion detection.Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates,ensuring enhanced privacy and scalability without compromising detection accuracy.Key innovations of this research include the integration of advanced deep learning techniques for real-time threat detection with minimal latency and a novel model to fortify the system’s resilience against diverse cyber-attacks such as Distributed Denial of Service(DDoS)and malware injections.Our evaluation on three benchmark IoT datasets demonstrates significant improvements:achieving 92.78%accuracy on NSL-KDD,91.47%on BoT-IoT,and 92.05%on UNSW-NB15.The precision,recall,and F1-scores for all datasets consistently exceed 91%.Furthermore,the communication overhead was reduced to 85 MB for NSL-KDD,105 MB for BoT-IoT,and 95 MB for UNSW-NB15—substantially lower than traditional centralized IDS approaches.This study contributes to the domain by presenting a scalable,secure,and privacy-preserving solution tailored to the unique characteristics of IoT environments.The proposed framework is adaptable to dynamic and heterogeneous settings,with potential applications extending to other privacy-sensitive domains.Future work will focus on enhancing the system’s efficiency and addressing emerging challenges such as model poisoning attacks in federated environments.展开更多
The Internet of Things(IoT)technology provides new impetus for the development of building intelligence.This research focuses on the intelligent design and management of buildings based on IoT engineering.It expounds ...The Internet of Things(IoT)technology provides new impetus for the development of building intelligence.This research focuses on the intelligent design and management of buildings based on IoT engineering.It expounds on the system design principles such as sensor technology,communication network technology,and data storage and analysis,and analyzes the key points of design,including design requirement analysis,equipment layout,and system integration.Through specific cases,it demonstrates the application practice of the system in buildings,and presents the application effect of intelligent system management with multi-parameter values,providing theoretical and practical references for the development of building intelligence and helping to achieve efficient,energy-saving,and safe building operation.展开更多
Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resourc...Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.展开更多
The Internet of MedicalThings(IoMT)connects healthcare devices and sensors to the Internet,driving transformative advancements in healthcare delivery.However,expanding IoMT infrastructures face growing security threat...The Internet of MedicalThings(IoMT)connects healthcare devices and sensors to the Internet,driving transformative advancements in healthcare delivery.However,expanding IoMT infrastructures face growing security threats,necessitating robust IntrusionDetection Systems(IDS).Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems,especially when securing interconnected medical devices.This paper introduces SNN-IoMT(Stacked Neural Network Ensemble for IoMT Security),an AI-driven IDS framework designed to secure dynamic IoMT environments.Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron(MLP),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM),the model optimizes data management and integration while ensuring system scalability and interoperability.Trained on the WUSTL-EHMS-2020 and IoT-Healthcare-Security datasets,SNN-IoMT surpasses existing IDS frameworks in accuracy,precision,and detecting novel threats.By addressing the primary challenges in AI-driven healthcare systems,including privacy,reliability,and ethical data management,our approach exemplifies the importance of AI to enhance security and trust in IoMT-enabled healthcare.展开更多
The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This pape...The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.展开更多
Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-ba...Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.展开更多
The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent require...The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.展开更多
With the development of the future Web of Healthcare Things(WoHT),there will be a trend of densely deploying medical sensors with massive simultaneous online communication requirements.The dense deployment and simulta...With the development of the future Web of Healthcare Things(WoHT),there will be a trend of densely deploying medical sensors with massive simultaneous online communication requirements.The dense deployment and simultaneous online communication of massive medical sensors will inevitably generate overlapping interference.This will be extremely challenging to support data transmission at the medical-grade quality of service level.To handle the challenge,this paper proposes a hypergraph interference coordination-aided resource allocation based on the Deep Reinforcement Learning(DRL)method.Specifically,we build a novel hypergraph interference model for the considered WoHT by analyzing the impact of the overlapping interference.Due to the high complexity of directly solving the hypergraph interference model,the original resource allocation problem is converted into a sequential decision-making problem through the Markov Decision Process(MDP)modeling method.Then,a policy and value-based resource allocation algorithm is proposed to solve this problem under simultaneous online communication and dense deployment.In addition,to enhance the exploration ability of the optimal allocation strategy for the agent,we propose a resource allocation algorithm with an asynchronous parallel architecture.Simulation results verify that the proposed algorithms can achieve higher network throughput than the existing algorithms in the considered WoHT scenario.展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
With the development of Internet of things technology,the real-time collection and transmission of meteorological data has become particularly important.Especially in response to emergencies such as natural disasters,...With the development of Internet of things technology,the real-time collection and transmission of meteorological data has become particularly important.Especially in response to emergencies such as natural disasters,it is very important to improve the efficiency of decision-making by quickly obtaining accurate meteorological observation data.However,the traditional method of meteorological data collection and transmission has a large delay in data acquisition due to the conversion of public network and internal network,which affects the timeliness of emergency decision-making.This paper proposes a solution based on the Internet of things platform combined with MQTT protocol,which aims to realize the efficient and reliable real-time collection and transmission of meteorological data,shorten the data acquisition time,improve the emergency response speed,and meet the needs of temporary observation.展开更多
With the recent advances in quantum computing,the key agreement algorithm based on traditional cryptography theory,which is applied to the Internet of Things(IoT)scenario,will no longer be secure due to the possibilit...With the recent advances in quantum computing,the key agreement algorithm based on traditional cryptography theory,which is applied to the Internet of Things(IoT)scenario,will no longer be secure due to the possibility of information leakage.In this paper,we propose a anti-quantum dynamic authenticated group key agreement scheme(AQDA-GKA)according to the ring-learning with errors(RLWE)problem,which is suitable for IoT environments.First,the proposed AQDA-GKA scheme can implement a group key agreement against quantum computing attacks by leveraging an RLWE-based key agreement mechanism.Second,this scheme can achieve dynamic node management,ensuring that any node can freely join or exit the current group.Third,we formally prove that the proposed scheme can resist quantum computing attacks as well as collusion attacks.Finally,the performance and security analysis reveals that the proposed AQDA-GKA scheme is secure and effective.展开更多
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
文摘The Internet of Things(IoT)has gained substantial attention in both academic research and real-world applications.The proliferation of interconnected devices across various domains promises to deliver intelligent and advanced services.However,this rapid expansion also heightens the vulnerability of the IoT ecosystem to security threats.Consequently,innovative solutions capable of effectively mitigating risks while accommodating the unique constraints of IoT environments are urgently needed.Recently,the convergence of Blockchain technology and IoT has introduced a decentralized and robust framework for securing data and interactions,commonly referred to as the Internet of Blockchained Things(IoBT).Extensive research efforts have been devoted to adapting Blockchain technology to meet the specific requirements of IoT deployments.Within this context,consensus algorithms play a critical role in assessing the feasibility of integrating Blockchain into IoT ecosystems.The adoption of efficient and lightweight consensus mechanisms for block validation has become increasingly essential.This paper presents a comprehensive examination of lightweight,constraint-aware consensus algorithms tailored for IoBT.The study categorizes these consensus mechanisms based on their core operations,the security of the block validation process,the incorporation of AI techniques,and the specific applications they are designed to support.
文摘The digital revolution era has impacted various domains,including healthcare,where digital technology enables access to and control of medical information,remote patient monitoring,and enhanced clinical support based on the Internet of Health Things(IoHTs).However,data privacy and security,data management,and scalability present challenges to widespread adoption.This paper presents a comprehensive literature review that examines the authentication mechanisms utilized within IoHT,highlighting their critical roles in ensuring secure data exchange and patient privacy.This includes various authentication technologies and strategies,such as biometric and multifactor authentication,as well as the influence of emerging technologies like blockchain,fog computing,and Artificial Intelligence(AI).The findings indicate that emerging technologies offer hope for the future of IoHT security,promising to address key challenges such as scalability,integrity,privacy and other security requirements.With this systematic review,healthcare providers,decision makers,scientists and researchers are empowered to confidently evaluate the applicability of IoT in healthcare,shaping the future of this field.
基金supported by the Princess Nourah bint Abdulrahman University Riyadh,Saudi Arabia,through Project number(PNURSP2025R235).
文摘The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these networks,as shared channels are used to transmit packets.In this paper,a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT.Initially,every device works collaboratively to form a distributed network.In this model,if a device is deployed outside the coverage area of the nearest server,it communicates indirectly through the neighboring devices.For this purpose,every device collects data from the respective neighboring devices,such as hop count,average packet transmission delay,criticality factor,link reliability,and RSSI value,etc.These parameters are used to find an optimal route from the source to the destination.Secondly,the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly.Moreover,these devices and server modules must ensure that every packet is transmitted securely,which is possible only if it is encrypted with an encryption algorithm.For this purpose,a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase.Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission.Moreover,the proposed approach has outperformed the existing approaches in terms of communication cost,processing overhead,end-to-end delay,packet loss ratio,and throughput.Finally,the proposed approach is adoptable in different networking infrastructures.
基金This research is conducted as part of the project titled“Digital Twin-based Intrusion Detection System Using Federated Learning for IoMT”(2024-2027),supported by C3iHub,IIT Kanpur,India,under Sanction Order No.:IHUB-NTIHAC/2024/01/3.
文摘The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.
基金funded by the Yunnan Key Laboratory of Blockchain Application Technology(202105AG070005,202305AG340008)&YNB202301,NSFC(Grant Nos.72293583,72293580,62476007,62176273,62271234)the Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2024-1-06)+2 种基金the Project of Science and Technology Major Project of Yunnan Province(202302AF080006)Open Foundation of State Key Laboratory of Public Big Data(Guizhou University)under Grant No.PBD2022-16Double First-Class Project for Collaborative Innovation Achievements inDisciplines Construction in Heilongjiang Province under Grant No.GXCG2022-054.
文摘Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure, quantum-resistant blind signatures. In this paper, we introduce lattice-based forward-secure blind signature (LFSBS), a lattice-based forward-secure blind signature scheme for medical privacy preservation in BIoMT. LFSBS achieves forward security by constructing a key evolution mechanism using a binary tree structure. This mechanism ensures that even if future encryption keys are leaked, past data can still remain secure. Meanwhile, LFSBS realizes post-quantum security based on the hardness assumption of small integer solution (SIS), making it resistant to potential quantum computing attacks. In addition, we formally define and prove the security of LFSBS in a random oracle model, including blindness and forward-secure unforgeability. Comprehensive performance evaluation shows that LFSBS performs well in terms of computational overhead, with a reduction of 22%–73% compared to previous schemes.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University via Grant No.(QU-APC-2025).
文摘The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the critical issues, which is the prediction of fraudulent devices, i.e., adversaries, preferably as early as possible in the IoT. In this paper, a hybrid communication mechanism is presented where the Hidden Markov Model (HMM) predicts the legitimacy of the requesting device (both source and destination), and the Advanced Encryption Standard (AES) safeguards the reliability of the transmitted data over a shared communication medium, preferably through a secret shared key, i.e., , and timestamp information. A device becomes trusted if it has passed both evaluation levels, i.e., HMM and message decryption, within a stipulated time interval. The proposed hybrid, along with existing state-of-the-art approaches, has been simulated in the realistic environment of the IoT to verify the security measures. These evaluations were carried out in the presence of intruders capable of launching various attacks simultaneously, such as man-in-the-middle, device impersonations, and masquerading attacks. Moreover, the proposed approach has been proven to be more effective than existing state-of-the-art approaches due to its exceptional performance in communication, processing, and storage overheads, i.e., 13%, 19%, and 16%, respectively. Finally, the proposed hybrid approach is pruned against well-known security attacks in the IoT.
基金supported and funded by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Systems(IDS)often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems.To address these challenges,we propose an innovative privacy-preserving approach leveraging Federated Learning(FL)for distributed intrusion detection.Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates,ensuring enhanced privacy and scalability without compromising detection accuracy.Key innovations of this research include the integration of advanced deep learning techniques for real-time threat detection with minimal latency and a novel model to fortify the system’s resilience against diverse cyber-attacks such as Distributed Denial of Service(DDoS)and malware injections.Our evaluation on three benchmark IoT datasets demonstrates significant improvements:achieving 92.78%accuracy on NSL-KDD,91.47%on BoT-IoT,and 92.05%on UNSW-NB15.The precision,recall,and F1-scores for all datasets consistently exceed 91%.Furthermore,the communication overhead was reduced to 85 MB for NSL-KDD,105 MB for BoT-IoT,and 95 MB for UNSW-NB15—substantially lower than traditional centralized IDS approaches.This study contributes to the domain by presenting a scalable,secure,and privacy-preserving solution tailored to the unique characteristics of IoT environments.The proposed framework is adaptable to dynamic and heterogeneous settings,with potential applications extending to other privacy-sensitive domains.Future work will focus on enhancing the system’s efficiency and addressing emerging challenges such as model poisoning attacks in federated environments.
文摘The Internet of Things(IoT)technology provides new impetus for the development of building intelligence.This research focuses on the intelligent design and management of buildings based on IoT engineering.It expounds on the system design principles such as sensor technology,communication network technology,and data storage and analysis,and analyzes the key points of design,including design requirement analysis,equipment layout,and system integration.Through specific cases,it demonstrates the application practice of the system in buildings,and presents the application effect of intelligent system management with multi-parameter values,providing theoretical and practical references for the development of building intelligence and helping to achieve efficient,energy-saving,and safe building operation.
基金funded by Researchers Supporting Project Number(RSPD2025R947)King Saud University,Riyadh,Saudi Arabia.
文摘Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.However,existing methods often fail in dynamic and high-demand environments,leading to resource bottlenecks and increased energy consumption.This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management(QIARM)model,which introduces novel algorithms inspired by quantum principles for enhanced resource allocation.QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically.In addition,an energy-aware scheduling module minimizes power consumption by selecting optimal configurations based on energy metrics.The simulation was carried out in a 360-minute environment with eight distinct scenarios.This study introduces a novel quantum-inspired resource management framework that achieves up to 98%task offload success and reduces energy consumption by 20%,addressing critical challenges of scalability and efficiency in dynamic fog computing environments.
文摘The Internet of MedicalThings(IoMT)connects healthcare devices and sensors to the Internet,driving transformative advancements in healthcare delivery.However,expanding IoMT infrastructures face growing security threats,necessitating robust IntrusionDetection Systems(IDS).Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems,especially when securing interconnected medical devices.This paper introduces SNN-IoMT(Stacked Neural Network Ensemble for IoMT Security),an AI-driven IDS framework designed to secure dynamic IoMT environments.Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron(MLP),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM),the model optimizes data management and integration while ensuring system scalability and interoperability.Trained on the WUSTL-EHMS-2020 and IoT-Healthcare-Security datasets,SNN-IoMT surpasses existing IDS frameworks in accuracy,precision,and detecting novel threats.By addressing the primary challenges in AI-driven healthcare systems,including privacy,reliability,and ethical data management,our approach exemplifies the importance of AI to enhance security and trust in IoMT-enabled healthcare.
文摘The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.
基金funding of the Deanship of Graduate Studies and Scientific Research,Jazan University,Saudi Arabia,through Project Number:ISP-2024.
文摘Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.GPIP:2040-611-2024。
文摘The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.
基金supported in part by the National Natural Science Foundation of China under Grant No.62301094in part by the Researchers Supporting Project Number(RSPD2024R681)King Saud University,Riyadh,Saudi Arabia,in part by the Science and Technology Research Program of the Chongqing Education Commission of China under Grants KJQN202201157 and KJQN202301135.
文摘With the development of the future Web of Healthcare Things(WoHT),there will be a trend of densely deploying medical sensors with massive simultaneous online communication requirements.The dense deployment and simultaneous online communication of massive medical sensors will inevitably generate overlapping interference.This will be extremely challenging to support data transmission at the medical-grade quality of service level.To handle the challenge,this paper proposes a hypergraph interference coordination-aided resource allocation based on the Deep Reinforcement Learning(DRL)method.Specifically,we build a novel hypergraph interference model for the considered WoHT by analyzing the impact of the overlapping interference.Due to the high complexity of directly solving the hypergraph interference model,the original resource allocation problem is converted into a sequential decision-making problem through the Markov Decision Process(MDP)modeling method.Then,a policy and value-based resource allocation algorithm is proposed to solve this problem under simultaneous online communication and dense deployment.In addition,to enhance the exploration ability of the optimal allocation strategy for the agent,we propose a resource allocation algorithm with an asynchronous parallel architecture.Simulation results verify that the proposed algorithms can achieve higher network throughput than the existing algorithms in the considered WoHT scenario.
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
基金Supported by Wuzhou Science and Technology Planning Project(202202047).
文摘With the development of Internet of things technology,the real-time collection and transmission of meteorological data has become particularly important.Especially in response to emergencies such as natural disasters,it is very important to improve the efficiency of decision-making by quickly obtaining accurate meteorological observation data.However,the traditional method of meteorological data collection and transmission has a large delay in data acquisition due to the conversion of public network and internal network,which affects the timeliness of emergency decision-making.This paper proposes a solution based on the Internet of things platform combined with MQTT protocol,which aims to realize the efficient and reliable real-time collection and transmission of meteorological data,shorten the data acquisition time,improve the emergency response speed,and meet the needs of temporary observation.
基金Supported by the National Engineering Research Center of Classified Protection and Safeguard Technology for Cybersecurity(No.C23640-XD-07)the Open Foundation of Key Laboratory of Cyberspace Security of Ministry of Education of China and Henan Key Laboratory of Network Cryptography(No.KLCS20240301)。
文摘With the recent advances in quantum computing,the key agreement algorithm based on traditional cryptography theory,which is applied to the Internet of Things(IoT)scenario,will no longer be secure due to the possibility of information leakage.In this paper,we propose a anti-quantum dynamic authenticated group key agreement scheme(AQDA-GKA)according to the ring-learning with errors(RLWE)problem,which is suitable for IoT environments.First,the proposed AQDA-GKA scheme can implement a group key agreement against quantum computing attacks by leveraging an RLWE-based key agreement mechanism.Second,this scheme can achieve dynamic node management,ensuring that any node can freely join or exit the current group.Third,we formally prove that the proposed scheme can resist quantum computing attacks as well as collusion attacks.Finally,the performance and security analysis reveals that the proposed AQDA-GKA scheme is secure and effective.
