The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated cha...The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.展开更多
In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with...In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.展开更多
6G IoT networks aim for providing significantly higher data rates and extremely lower latency.However,due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices(IoDs)deployed,6G IoT netw...6G IoT networks aim for providing significantly higher data rates and extremely lower latency.However,due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices(IoDs)deployed,6G IoT networks face two critical challenges,i.e.,energy limitation and severe signal attenuation.Simultaneous wireless information and power transfer(SWIPT)and cooperative relaying provide effective ways to address these two challenges.In this paper,we investigate the energy self-sustainability(ESS)of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks.In the proposed strategy,the transmission process is equally divided into two phases.Specifically,in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy,respectively.In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy.We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation.Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.展开更多
Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the con...Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network.Therefore,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone domains.In ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes.This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination.The proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources.Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application throughput.The end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.展开更多
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.展开更多
As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of a...As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of applying Machine Learning-based cybersecurity in IoT devices is feature selection as most IoT devices are resource-constrained. This paper studies two feature selection algorithms: Information Gain and PSO-based, to select a minimum number of attack features, and Decision Tree and SVM are utilized for performance comparison. The consistent use of the same metrics in feature selection and detection algorithms substantially enhances the classification accuracy compared to the non-consistent use in feature selection by Information Gain (entropy) and Tree detection algorithm by classification. Furthermore, the Tree with consistent feature selection is comparable to the ensemble that provides excellent performance at the cost of computation complexity.展开更多
In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic.Though IoT network...In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic.Though IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging problem.Conventional intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and complexity.Therefore,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT networks.The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data normalization.To manage big data,Hadoop ecosystem is employed.Besides,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection efficiency.Moreover,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data.The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance.To validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects.The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.展开更多
The Internet of things(IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when Io T applications meet heterogeneous networks(HetNets) where macro cells are over...The Internet of things(IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when Io T applications meet heterogeneous networks(HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier Het Nets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals(UTs), and the number of base stations(BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.展开更多
The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress.Nevertheless,alongside this advancement,IoT networks are facing an ever-increasing rate of security risks ...The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress.Nevertheless,alongside this advancement,IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments.In order to overcome these security challenges,the fog system has delivered a powerful environment that provides additional resources for a more improved data security.However,because of the emerging of various breaches,several attacks are ceaselessly emerging in IoT and Fog environment.Consequently,the new emerging applications in IoT-Fog environment still require novel,distributed,and intelligent security models,controls,and decisions.In addition,the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems.This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks.In fact,to address the above problems,Artificial Intelligence(AI)methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems.In this respect,this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoTFog environments.We evaluated the performance of our distributed approach with the application of certain machine learningmechanisms.The experiments show that the proposed scheme,applied with the Random Forest(RF)is more efficient and provides better accuracy(99.50%),better scalability,and lower false alert rates.In this regard,the distribution character of our method brings about faster detection and better learning.展开更多
Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WS...Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WSNs may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized decision.On the other side the proliferation of these devices will soon cause radio spectrum shortage.So,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these networks.CR can sense the unutilized spectrum of licensed users and then use these empty bands when required.In order to keep the IoT nodes functional all time,continuous energy is required.For this reason the energy harvested techniques are preferred in IoT networks.Mainly it is preferred to harvest Radio Frequency(RF)energy in the network.In this paper a region based multi-channel architecture is proposed.In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region.The Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy.To harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel.From the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel.We also show that channel load can be highly reduced and channel utilization is proved to be more proficient.Thus,this proves the proposed architecture cost-effective and energy-efficient.展开更多
ZTE Corporation, a leading global provider of telecommunications equipment and networking solutions, announced on May 11,2010 that ZTE Corporation and Innofidei have jointly delivered a significant breakthrough for th...ZTE Corporation, a leading global provider of telecommunications equipment and networking solutions, announced on May 11,2010 that ZTE Corporation and Innofidei have jointly delivered a significant breakthrough for the Time Division Long Term Evolution （TD-LTE） industry with the industry＇s first successful Inter-Operability Test（IOT） of multiple TD-LTE USB dongles in a single mobile network cell. The successful test was first performed in Hong Kong,展开更多
The Distributed Queuing (DQ) algorithm is predicted as one of the solutions to the issues currently found in IoT networks over the use of Aloha based algorithms. Since recently, the algorithm has been of interest to m...The Distributed Queuing (DQ) algorithm is predicted as one of the solutions to the issues currently found in IoT networks over the use of Aloha based algorithms. Since recently, the algorithm has been of interest to many IoT researchers as a replacement of those Aloha variants for channel access. However, previous works analyzed and evaluated the DQ algorithm without any consideration of the stability of its queues, assuming it is stable for any given number of nodes in the network. In this paper, we define the DQ stability condition in a single-channel M2M environment considering a traffic model of periodic and urgent frames from each node in the network. Besides, a steady-state evaluation of the algorithm’s performance metrics is also presented. In general, the DQ algorithm, when it is stable, was observed not to efficiently use the contention slots for the collision resolution. In a single-channel environment, the DQ algorithm is found to outperform the Aloha based algorithms only in an idle-to-saturation scenario.展开更多
Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to hea...Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy traffic in cellular networks. To alleviate the transmission workload, offloading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular network.In this paper, we design a Cost Saving Offloading System（CoSOS）, where the Internet of Things（IoT） is used to undertake partial data traffic and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.展开更多
With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share information.But the IoT model is prone to security concerns as several attackers try to hit the networ...With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share information.But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable.In such scenarios,security concern is the most prominent.Different models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security breaches.The malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices.This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT environment.In the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT platform.The SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection rate.Besides,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique.The comparison study reported the enhancement of the SCADL-RWDC technique over other models.展开更多
The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things(IoT)networks that provide ultra-low-power communication for numerous miniaturized devices.Although the researc...The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things(IoT)networks that provide ultra-low-power communication for numerous miniaturized devices.Although the research community has paid great attention to wireless protocol designs for these networks,researchers are handicapped by the lack of an energy-efficient software-defined radio(SDR)platform for fast implementation and experimental evaluation.Current SDRs perform well in battery-equipped systems,but fail to support miniaturized IoT devices with stringent hardware and power constraints.This paper takes the first step toward designing an ultra-low-power SDR that satisfies the ultra-low-power or even battery-free requirements of intelligent and simplified IoT networks.To achieve this goal,the core technique is the effective integration ofµW-level backscatter in our SDR to sidestep power-hungry active radio frequency chains.We carefully develop a novel circuit design for efficient energy harvesting and power control,and devise a competent solution for eliminating the harmonic and mirror frequencies caused by backscatter hardware.We evaluate the proposed SDR using different modulation schemes,and it achieves a high data rate of 100 kb/s with power consumption less than 200µW in the active mode and as low as 10µW in the sleep mode.We also conduct a case study of railway inspection using our platform,achieving 1 kb/s battery-free data delivery to the monitoring unmanned aerial vehicle at a distance of 50 m in a real-world environment,and provide two case studies on smart factories and logistic distribution to explore the application of our platform.展开更多
Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumpt...Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay.However,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless environment.Therefore,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning.Specifically,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system.In this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance.Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.展开更多
基金funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah under grant No.(RG-6-611-43)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘The integration of the Internet of Things(IoT)into healthcare systems improves patient care,boosts operational efficiency,and contributes to cost-effective healthcare delivery.However,overcoming several associated challenges,such as data security,interoperability,and ethical concerns,is crucial to realizing the full potential of IoT in healthcare.Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems.In this context,this paper presents a novelmethod for healthcare data privacy analysis.The technique is based on the identification of anomalies in cloud-based Internet of Things(IoT)networks,and it is optimized using explainable artificial intelligence.For anomaly detection,the Radial Boltzmann Gaussian Temporal Fuzzy Network(RBGTFN)is used in the process of doing information privacy analysis for healthcare data.Remora Colony SwarmOptimization is then used to carry out the optimization of the network.The performance of the model in identifying anomalies across a variety of healthcare data is evaluated by an experimental study.This evaluation suggested that themodel measures the accuracy,precision,latency,Quality of Service(QoS),and scalability of themodel.A remarkable 95%precision,93%latency,89%quality of service,98%detection accuracy,and 96%scalability were obtained by the suggested model,as shown by the subsequent findings.
基金supported by the Natural Science Foundation of Beijing Municipality under Grant L192034。
文摘In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.
基金This work was supported by China National Science Foundation under Grant No.61871348by University Key Laboratory of Advanced Wireless Communications of Guangdong Province,by the Project funded by China Postdoctoral Science Foundation under Grant 2019T120531+1 种基金by the Science and Technology Development Fund,Macao,China under Grant 0162/2019/A3by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RFA2019001.
文摘6G IoT networks aim for providing significantly higher data rates and extremely lower latency.However,due to the increasingly scarce spectrum bands and ever-growing massive number IoT devices(IoDs)deployed,6G IoT networks face two critical challenges,i.e.,energy limitation and severe signal attenuation.Simultaneous wireless information and power transfer(SWIPT)and cooperative relaying provide effective ways to address these two challenges.In this paper,we investigate the energy self-sustainability(ESS)of 6G IoT network and propose an OFDM based bidirectional multi-relay SWIPT strategy for 6G IoT networks.In the proposed strategy,the transmission process is equally divided into two phases.Specifically,in phase1 two source nodes transmit their signals to relay nodes which will then use different subcarrier sets to decode information and harvest energy,respectively.In phase2 relay nodes forward signals to corresponding destination nodes with the harvested energy.We maximize the weighted sum transmission rate by optimizing subcarriers and power allocation.Our proposed strategy achieves larger weighted sum transmission rate comparing with the benchmark scheme.
基金funded by Wenzhou Kean University under the IRSP Program“Hop by Hop Resource Reservation based Scheduling Function for Deterministic IoT networks”.
文摘Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network.Therefore,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone domains.In ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes.This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination.The proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources.Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application throughput.The end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
文摘As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of applying Machine Learning-based cybersecurity in IoT devices is feature selection as most IoT devices are resource-constrained. This paper studies two feature selection algorithms: Information Gain and PSO-based, to select a minimum number of attack features, and Decision Tree and SVM are utilized for performance comparison. The consistent use of the same metrics in feature selection and detection algorithms substantially enhances the classification accuracy compared to the non-consistent use in feature selection by Information Gain (entropy) and Tree detection algorithm by classification. Furthermore, the Tree with consistent feature selection is comparable to the ensemble that provides excellent performance at the cost of computation complexity.
文摘In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic.Though IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging problem.Conventional intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and complexity.Therefore,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT networks.The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data normalization.To manage big data,Hadoop ecosystem is employed.Besides,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection efficiency.Moreover,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data.The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance.To validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects.The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.
基金supported by the National Natural Science Foundation of China (NSFC) under Grants 61427801 and 61671251the Natural Science Foundation Program through Jiangsu Province of China under Grant BK20150852+3 种基金the open research fund of National Mobile Communications Research Laboratory, Southeast University under Grant 2017D05China Postdoctoral Science Foundation under Grant 2016M590481Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 1501018Asupported by NSFC under Grants 61531011 and 61625106
文摘The Internet of things(IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when Io T applications meet heterogeneous networks(HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier Het Nets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals(UTs), and the number of base stations(BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.
文摘The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress.Nevertheless,alongside this advancement,IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments.In order to overcome these security challenges,the fog system has delivered a powerful environment that provides additional resources for a more improved data security.However,because of the emerging of various breaches,several attacks are ceaselessly emerging in IoT and Fog environment.Consequently,the new emerging applications in IoT-Fog environment still require novel,distributed,and intelligent security models,controls,and decisions.In addition,the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems.This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks.In fact,to address the above problems,Artificial Intelligence(AI)methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems.In this respect,this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoTFog environments.We evaluated the performance of our distributed approach with the application of certain machine learningmechanisms.The experiments show that the proposed scheme,applied with the Random Forest(RF)is more efficient and provides better accuracy(99.50%),better scalability,and lower false alert rates.In this regard,the distribution character of our method brings about faster detection and better learning.
文摘Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion etc.WSNs may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized decision.On the other side the proliferation of these devices will soon cause radio spectrum shortage.So,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these networks.CR can sense the unutilized spectrum of licensed users and then use these empty bands when required.In order to keep the IoT nodes functional all time,continuous energy is required.For this reason the energy harvested techniques are preferred in IoT networks.Mainly it is preferred to harvest Radio Frequency(RF)energy in the network.In this paper a region based multi-channel architecture is proposed.In which the coverage area of primary node is divided as Energy Harvesting Region and Communication Region.The Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency energy.To harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal channel.From the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single channel.We also show that channel load can be highly reduced and channel utilization is proved to be more proficient.Thus,this proves the proposed architecture cost-effective and energy-efficient.
文摘ZTE Corporation, a leading global provider of telecommunications equipment and networking solutions, announced on May 11,2010 that ZTE Corporation and Innofidei have jointly delivered a significant breakthrough for the Time Division Long Term Evolution （TD-LTE） industry with the industry＇s first successful Inter-Operability Test（IOT） of multiple TD-LTE USB dongles in a single mobile network cell. The successful test was first performed in Hong Kong,
文摘The Distributed Queuing (DQ) algorithm is predicted as one of the solutions to the issues currently found in IoT networks over the use of Aloha based algorithms. Since recently, the algorithm has been of interest to many IoT researchers as a replacement of those Aloha variants for channel access. However, previous works analyzed and evaluated the DQ algorithm without any consideration of the stability of its queues, assuming it is stable for any given number of nodes in the network. In this paper, we define the DQ stability condition in a single-channel M2M environment considering a traffic model of periodic and urgent frames from each node in the network. Besides, a steady-state evaluation of the algorithm’s performance metrics is also presented. In general, the DQ algorithm, when it is stable, was observed not to efficiently use the contention slots for the collision resolution. In a single-channel environment, the DQ algorithm is found to outperform the Aloha based algorithms only in an idle-to-saturation scenario.
基金supported by the National Natural Science Foundation of China (Nos. 61300207, 61370084, and 61502116)
文摘Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy traffic in cellular networks. To alleviate the transmission workload, offloading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular network.In this paper, we design a Cost Saving Offloading System（CoSOS）, where the Internet of Things（IoT） is used to undertake partial data traffic and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.
基金This work was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant No.(RGP-1443-0051).
文摘With the advent of the Internet of Things(IoT),several devices like sensors nowadays can interact and easily share information.But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable.In such scenarios,security concern is the most prominent.Different models were intended to address these security problems;still,several emergent variants of botnet attacks like Bashlite,Mirai,and Persirai use security breaches.The malware classification and detection in the IoT model is still a problem,as the adversary reliably generates a new variant of IoT malware and actively searches for compromise on the victim devices.This article develops a Sine Cosine Algorithm with Deep Learning based Ransomware Detection and Classification(SCADL-RWDC)method in an IoT environment.In the presented SCADL-RWDCtechnique,the major intention exists in recognizing and classifying ransomware attacks in the IoT platform.The SCADL-RWDC technique uses the SCA feature selection(SCA-FS)model to improve the detection rate.Besides,the SCADL-RWDC technique exploits the hybrid grey wolf optimizer(HGWO)with a gated recurrent unit(GRU)model for ransomware classification.A widespread experimental analysis is performed to exhibit the enhanced ransomware detection outcomes of the SCADL-RWDC technique.The comparison study reported the enhancement of the SCADL-RWDC technique over other models.
基金Project supported by the National Key R&D Program of China(Nos.2020YFB1806606 and 2016YFB1200100)the National Natural Science Foundation of China(No.62071194)。
文摘The recent decade has witnessed an upsurge in the demands of intelligent and simplified Internet of Things(IoT)networks that provide ultra-low-power communication for numerous miniaturized devices.Although the research community has paid great attention to wireless protocol designs for these networks,researchers are handicapped by the lack of an energy-efficient software-defined radio(SDR)platform for fast implementation and experimental evaluation.Current SDRs perform well in battery-equipped systems,but fail to support miniaturized IoT devices with stringent hardware and power constraints.This paper takes the first step toward designing an ultra-low-power SDR that satisfies the ultra-low-power or even battery-free requirements of intelligent and simplified IoT networks.To achieve this goal,the core technique is the effective integration ofµW-level backscatter in our SDR to sidestep power-hungry active radio frequency chains.We carefully develop a novel circuit design for efficient energy harvesting and power control,and devise a competent solution for eliminating the harmonic and mirror frequencies caused by backscatter hardware.We evaluate the proposed SDR using different modulation schemes,and it achieves a high data rate of 100 kb/s with power consumption less than 200µW in the active mode and as low as 10µW in the sleep mode.We also conduct a case study of railway inspection using our platform,achieving 1 kb/s battery-free data delivery to the monitoring unmanned aerial vehicle at a distance of 50 m in a real-world environment,and provide two case studies on smart factories and logistic distribution to explore the application of our platform.
基金This work was partially supported by the National Key Research and Development Program of China(No.2020YFB1005900)the National Natural Science Foundation of China(Nos.62102232,62122042,and 61971269)the Natural Science Foundation of Shandong Province(No.ZR2021QF064).
文摘Most blockchain systems currently adopt resource-consuming protocols to achieve consensus between miners;for example,the Proof-of-Work(PoW)and Practical Byzantine Fault Tolerant(PBFT)schemes,which have a high consumption of computing/communication resources and usually require reliable communications with bounded delay.However,these protocols may be unsuitable for Internet of Things(IoT)networks because the IoT devices are usually lightweight,battery-operated,and deployed in an unreliable wireless environment.Therefore,this paper studies an efficient consensus protocol for blockchain in IoT networks via reinforcement learning.Specifically,the consensus protocol in this work is designed on the basis of the Proof-of-Communication(PoC)scheme directly in a single-hop wireless network with unreliable communications.A distributed MultiAgent Reinforcement Learning(MARL)algorithm is proposed to improve the efficiency and fairness of consensus for miners in the blockchain system.In this algorithm,each agent uses a matrix to depict the efficiency and fairness of the recent consensus and tunes its actions and rewards carefully in an actor-critic framework to seek effective performance.Empirical results from the simulation show that the fairness of consensus in the proposed algorithm is guaranteed,and the efficiency nearly reaches a centralized optimal solution.
