Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(I...Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.展开更多
The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and ...The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.展开更多
The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge t...The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.展开更多
With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analy...With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour recognition.These applications have dramatically increased the diversity of IoT systems.Specifically,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension.Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial information.However,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos.In this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing systems.In particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and UCF101.The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.展开更多
In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need t...In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.展开更多
The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective se...The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verify their performance,particularly in terms of various evaluation metrics,i.e.,processing,communication,and storage overheads.These results have verified the superiority of the proposed scheme against existing state-of-the-art approaches,preferably in terms of communication,storage,and processing costs.展开更多
Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the...Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.展开更多
Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is ...Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still thebiggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services providedby an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures,data, and devices. Authentication, as the first line of defense against security threats, becomes the priority ofeveryone. It can either grant or deny users access to resources according to their legitimacy. As a result, studyingand researching authentication issues within IoT is extremely important. As a result, studying and researchingauthentication issues within IoT is extremely important. This article presents a comparative study of recent researchin IoT security;it provides an analysis of recent authentication protocols from2019 to 2023 that cover several areaswithin IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security researchsummary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. Itillustrates that the resistance of protocols against attacks, and their computational and communication cost arelinked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemesand provides some future research directions.展开更多
The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a re...The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a result,research on network analysis has become vital.Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods.In this paper,we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework.The results indicate that using the proposed ensemble techniques improves accuracy by up to 1.7%compared to singlealgorithm approaches.These results also suggest that the proposed framework can flexibly adapt to general IoT network analysis scenarios.Unlike existing frameworks,which only exhibit high performance in specific situations,the proposed framework can serve as a fundamental approach for addressing a wide range of issues.展开更多
As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for...As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both cus...Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.展开更多
The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent ...The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.展开更多
Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly...Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.展开更多
With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smar...With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.展开更多
Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,etc.Recognizing Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessi...Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,etc.Recognizing Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems.Deep learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a DoS outbreak.The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and functionality.Subsequently,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT environment.The PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service(DDoS)attack detection in an IoT platform.Initially,the PIODL-ADC model utilizes Z-score normalization to scale input data into a uniformformat.For handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s accuracy.Also,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDoS attacks.Moreover,reptile search algorithm(RSA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC method.The experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.展开更多
Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reli...Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.展开更多
There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The re...There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The regular random access(RA)protocols may generate large amounts of collisions,which degrade the system throughout severally.The near-far effect and power control technologies are not applicable in capture effect to obtain power difference,resulting in the collisions that cannot be separated.In fact,the optimal design at the receiving end can also realize the condition of packet power domain separation,but there are few relevant researches.In this paper,an auxiliary beamforming scheme is proposed for power domain signal separation.It adds an auxiliary reception beam based on the conventional beam,utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation.The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio(CNR)difference.This paper uses the genetic algorithm to optimize the auxiliary beam direction.Simulation results show that the proposed scheme outperforms slotted ALOHA(SA)in terms of system throughput per-formance and without bringing terminals additional control burden.展开更多
Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)systems.However,conventional mode-based authentication methods(e.g.,passwords and sm...Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)systems.However,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral characteristics.Behavioral characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in practice.However,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication.Thus,we review the literature on the use of AI in physiological characteristics recognition pub-lished after 2015.We use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their limitations.We also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.展开更多
With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we p...With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.展开更多
文摘Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.
文摘The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation.However,FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios.The paper systematically reviewed the available literature using the PRISMA guiding principle.The study aims to provide a detailed overview of the increasing use of FL in IoT networks,including the architecture and challenges.A systematic review approach is used to collect,categorize and analyze FL-IoT-based articles.Asearch was performed in the IEEE,Elsevier,Arxiv,ACM,and WOS databases and 92 articles were finally examined.Inclusion measures were published in English and with the keywords“FL”and“IoT”.The methodology begins with an overview of recent advances in FL and the IoT,followed by a discussion of how these two technologies can be integrated.To be more specific,we examine and evaluate the capabilities of FL by talking about communication protocols,frameworks and architecture.We then present a comprehensive analysis of the use of FL in a number of key IoT applications,including smart healthcare,smart transportation,smart cities,smart industry,smart finance,and smart agriculture.The key findings from this analysis of FL IoT services and applications are also presented.Finally,we performed a comparative analysis with FL IID(independent and identical data)and non-ID,traditional centralized deep learning(DL)approaches.We concluded that FL has better performance,especially in terms of privacy protection and resource utilization.FL is excellent for preserving privacy becausemodel training takes place on individual devices or edge nodes,eliminating the need for centralized data aggregation,which poses significant privacy risks.To facilitate development in this rapidly evolving field,the insights presented are intended to help practitioners and researchers navigate the complex terrain of FL and IoT.
基金supported by the National Natural Science Foundation of China(62072392)the National Natural Science Foundation of China(61972360)the Major Scientific and Technological Innovation Projects of Shandong Province(2019522Y020131).
文摘The development of the Internet of Things(IoT)has brought great convenience to people.However,some information security problems such as privacy leakage are caused by communicating with risky users.It is a challenge to choose reliable users with which to interact in the IoT.Therefore,trust plays a crucial role in the IoT because trust may avoid some risks.Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning.However,trust propagation is time-consuming,and trust changes with the interaction process in social networks.To track the dynamic changes in trust values,a dynamic trust inference algorithm named Dynamic Double DQN Trust(Dy-DDQNTrust)is proposed to predict the indirect trust values of two users without direct contact with each other.The proposed algorithm simulates the interactions among users by double DQN.Firstly,CurrentNet and TargetNet networks are used to select users for interaction.The users with high trust are chosen to interact in future iterations.Secondly,the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction.Finally,the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Averagebased Similarity(SMCFAvg)aggregation strategy.Experiments are carried out on the FilmTrust and the Epinions datasets.Compared with TidalTrust,MoleTrust,DDQNTrust,DyTrust and Dynamic Weighted Heuristic trust path Search algorithm(DWHS),our dynamic trust inference algorithm has higher prediction accuracy and better scalability.
基金supported by National Natural Science Foundation of China under grant No.62271125,No.62273071Sichuan Science and Technology Program(No.2022YFG0038,No.2021YFG0018)+1 种基金by Xinjiang Science and Technology Program(No.2022273061)by the Fundamental Research Funds for the Central Universities(No.ZYGX2020ZB034,No.ZYGX2021J019).
文摘With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour recognition.These applications have dramatically increased the diversity of IoT systems.Specifically,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension.Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial information.However,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos.In this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing systems.In particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and UCF101.The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.
基金the support of the Korea Research Foundation with the funding of the Ministry of Science and Information and Communication Technology(No.2018-0-88457,development of translucent solar cells and Internet of Things technology for Solar Signage).
文摘In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.
文摘The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verify their performance,particularly in terms of various evaluation metrics,i.e.,processing,communication,and storage overheads.These results have verified the superiority of the proposed scheme against existing state-of-the-art approaches,preferably in terms of communication,storage,and processing costs.
基金supported in part by the National Natural Science Foundation of China (62072248, 62072247)the Jiangsu Agriculture Science and Technology Innovation Fund (CX(21)3060)。
文摘Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
文摘Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 andsmart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still thebiggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services providedby an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures,data, and devices. Authentication, as the first line of defense against security threats, becomes the priority ofeveryone. It can either grant or deny users access to resources according to their legitimacy. As a result, studyingand researching authentication issues within IoT is extremely important. As a result, studying and researchingauthentication issues within IoT is extremely important. This article presents a comparative study of recent researchin IoT security;it provides an analysis of recent authentication protocols from2019 to 2023 that cover several areaswithin IoT (such as smart cities, healthcare, and industry). This survey sought to provide an IoT security researchsummary, the biggest susceptibilities, and attacks, the appropriate technologies, and the most used simulators. Itillustrates that the resistance of protocols against attacks, and their computational and communication cost arelinked directly to the cryptography technique used to build it. Furthermore, it discusses the gaps in recent schemesand provides some future research directions.
基金supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(IITP2024-00156287,50%)funded by the Institute for Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2022-0-01203,Regional Strategic Industry Convergence Security Core Talent Training Business,50%).
文摘The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a result,research on network analysis has become vital.Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods.In this paper,we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework.The results indicate that using the proposed ensemble techniques improves accuracy by up to 1.7%compared to singlealgorithm approaches.These results also suggest that the proposed framework can flexibly adapt to general IoT network analysis scenarios.Unlike existing frameworks,which only exhibit high performance in specific situations,the proposed framework can serve as a fundamental approach for addressing a wide range of issues.
基金supported by the National Natural Science Foundation of China(Grant Nos.62102240,62071283)the China Postdoctoral Science Foundation(Grant No.2020M683421)the Key R&D Program of Shaanxi Province(Grant No.2020ZDLGY10-05).
文摘As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金Ministry of Higher Education of Malaysia under theResearch GrantLRGS/1/2019/UKM-UKM/5/2 and Princess Nourah bint Abdulrahman University for financing this researcher through Supporting Project Number(PNURSP2024R235),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.
文摘The recent development of the Internet of Things(IoTs)resulted in the growth of IoT-based DDoS attacks.The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices.Anomaly detection models evaluate transmission patterns,network traffic,and device behaviour to detect deviations from usual activities.Machine learning(ML)techniques detect patterns signalling botnet activity,namely sudden traffic increase,unusual command and control patterns,or irregular device behaviour.In addition,intrusion detection systems(IDSs)and signature-based techniques are applied to recognize known malware signatures related to botnets.Various ML and deep learning(DL)techniques have been developed to detect botnet attacks in IoT systems.To overcome security issues in an IoT environment,this article designs a gorilla troops optimizer with DL-enabled botnet attack detection and classification(GTODL-BADC)technique.The GTODL-BADC technique follows feature selection(FS)with optimal DL-based classification for accomplishing security in an IoT environment.For data preprocessing,the min-max data normalization approach is primarily used.The GTODL-BADC technique uses the GTO algorithm to select features and elect optimal feature subsets.Moreover,the multi-head attention-based long short-term memory(MHA-LSTM)technique was applied for botnet detection.Finally,the tree seed algorithm(TSA)was used to select the optimum hyperparameter for the MHA-LSTM method.The experimental validation of the GTODL-BADC technique can be tested on a benchmark dataset.The simulation results highlighted that the GTODL-BADC technique demonstrates promising performance in the botnet detection process.
基金supported in part by the National Key R&D Program of China(No.2021YFB3300100)the National Natural Science Foundation of China(No.62171062)。
文摘Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.62072074,62076054,62027827,62002047)the Sichuan Science and Technology Innovation Platform and Talent Plan(Nos.2020JDJQ0020,2022JDJQ0039)+2 种基金the Sichuan Science and Technology Support Plan(Nos.2020YFSY0010,2022YFQ0045,2022YFS0220,2023YFG0148,2021YFG0131)the YIBIN Science and Technology Support Plan(No.2021CG003)the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China(Nos.ZYGX2021YGLH212,ZYGX2022YGRH012).
文摘With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.
文摘Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,etc.Recognizing Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT systems.Deep learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a DoS outbreak.The uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and functionality.Subsequently,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT environment.The PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service(DDoS)attack detection in an IoT platform.Initially,the PIODL-ADC model utilizes Z-score normalization to scale input data into a uniformformat.For handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s accuracy.Also,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDoS attacks.Moreover,reptile search algorithm(RSA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC method.The experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.
基金supported by Science and Technology Project of China Southern Power Grid Company Limited under Grant Number 036000KK52200058(GDKJXM20202001).
文摘Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.
基金supported by the National Science Foundation of China(No.U21A20450)Natural Science Foundation of Jiangsu Province Major Project(No.BK20192002)+1 种基金National Natural Science Foundation of China(No.61971440)National Natural Science Foundation of China(No.62271266).
文摘There are numerous terminals in the satellite Internet of Things(IoT).To save cost and reduce power consumption,the system needs terminals to catch the characteristics of low power consumption and light control.The regular random access(RA)protocols may generate large amounts of collisions,which degrade the system throughout severally.The near-far effect and power control technologies are not applicable in capture effect to obtain power difference,resulting in the collisions that cannot be separated.In fact,the optimal design at the receiving end can also realize the condition of packet power domain separation,but there are few relevant researches.In this paper,an auxiliary beamforming scheme is proposed for power domain signal separation.It adds an auxiliary reception beam based on the conventional beam,utilizing the correlation of packets in time-frequency domain between the main and auxiliary beam to complete signal separation.The roll-off belt of auxiliary beam is used to create the carrier-to-noise ratio(CNR)difference.This paper uses the genetic algorithm to optimize the auxiliary beam direction.Simulation results show that the proposed scheme outperforms slotted ALOHA(SA)in terms of system throughput per-formance and without bringing terminals additional control burden.
基金funded in part by the National Natural Science Foundation of China under Grant No.61872038in part by the Fundamental Research Funds for the Central Universities under Grant No.FRF-GF-20-15B.
文摘Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)systems.However,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral characteristics.Behavioral characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in practice.However,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication.Thus,we review the literature on the use of AI in physiological characteristics recognition pub-lished after 2015.We use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their limitations.We also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.
基金supported by the National Natural Science Foundation of China(No.62171051)。
文摘With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.
