Editor＇s Desk：The Internet of Things is an important part of the emerging high-tech industry and has been recognized as one of the important technologies for addressing global financial crisis and revitalizing the e...Editor＇s Desk：The Internet of Things is an important part of the emerging high-tech industry and has been recognized as one of the important technologies for addressing global financial crisis and revitalizing the economy. Therefore, at the beginning of the new year, we are honored to have invited Mr. Samuel Qi （Qi Qingzhong）, a famous Chinese communication technology expert and GM of Shanghai Symphony Telecommunications Co., Ltd., to give his views on the development strategy of the Internet of Things and Machine to Machine （M2M） Communications.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
In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to im...In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.展开更多
Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service communi...Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.展开更多
The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service composition.The SIoT aims to socialize the IoT devices ...The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service composition.The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings.In IoT,an object can offer multiple services and different objects can offer the same services with different parameters and interest factors.The proliferation of offered services led to difficulties during service customization and service filtering.This problem is known as service explosion.The selection of suitable service that fits the requirements of applications and objects is a challenging task.To address these issues,we propose an efficient automated query-based service search model based on the local network navigability concept for the SIoT.In the proposed model,objects can use information from their friends or friends of their friends while searching for the desired services,rather than exploring a global network.We employ a centrality metric that computes the degree of importance for each object in the social IoT that helps in selecting neighboring objects with high centrality scores.The distributed nature of our navigation model results in high scalability and short navigation times.We verified the efficacy of our model on a real-world SIoT-related dataset.The experimental results confirm the validity of our model in terms of scalability,navigability,and the desired objects that provide services are determined quickly via the shortest path,which in return improves the service search process in the SIoT.展开更多
The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Alt...The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Although the basic concept of the Internet of Things sounds simple, its application is difficult and, so far, the respective existing architectural models are rather monolithic and are dominated by several limitations. The paper introduces a generic Internet of Things architecture trying to resolve the existing restrictions of current architectural models by integrating both RFID and smart object-based infrastructures, while also exploring a third parameter, i.e. the social potentialities of the Internet of Things building blocks towards shaping the “Social Internet of Things”. The proposed architecture is based on a layered lightweight and open middle-ware solution following the paradigm of Service Oriented Architecture and the Semantic Model Driven Ap-proach, which is realized at both design-time and deployment–time covering the whole service lifecycle for the corresponding services and applications provided.展开更多
With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originatin...With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the access network of 6G IoT.Based on historical communication behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.展开更多
In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost e...In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.展开更多
Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These...Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.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
The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(...The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.展开更多
The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed block...The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed blocking and other such consequences. The problem is exacerbated by the growth in size, complexity and the number of chronic diseases in the NHS. In addition, there is an increase in demand for high quality care, processes and planning. Effective Discharge Planning (DP) requires practitioners to have appropriate, patient personalised and updated knowledge in order to be able to make informed and holistic decisions about a patients’ discharge. This paper examines the role of Knowledge Management (KM) in both sharing knowledge and using tacit knowledge to create appropriate patient discharge pathways. The paper details the factors resulting in inadequate DP, and demonstrates the use of Internet of Things (IoT) and Machine2Machine (M2M) as candidate technologies and possible solutions which can help reduce the problem. The use of devices that a patient can take home and devices which are perused in the hospital generate information, which can serve useful when presented to the right person at the right time, thus harvesting knowledge. The knowledge when fed back can support practitioners in making holistic decisions with regards to a patients’ discharge.展开更多
针对过去几年物联网以垂直行业应用为主,系统部署成本高,缺乏系统间跨行业的资源共享,用户只能被动接受服务,无法参与到服务的生成中,不利推广等问题,将Web技术作为Internet of Things实现和推广的手段,对目前国内外Web of Things工作...针对过去几年物联网以垂直行业应用为主,系统部署成本高,缺乏系统间跨行业的资源共享,用户只能被动接受服务,无法参与到服务的生成中,不利推广等问题,将Web技术作为Internet of Things实现和推广的手段,对目前国内外Web of Things工作进行了总结分析,提出了扁平化的Web of Things开放架构。首先分析了现有Inter-net of Things架构的不足和需求,并结合现有的Web of Things概念模型,提出了Web of Things开放架构和其中的关键技术、开放模式。最后,对Web of Things和Internet of Things进行了系统对比。展开更多
With the continuous development and evolvement of Internet of Things （IoT）, monolithic application becomes much larger in scale and even more complex in structure. This leads to poor scalability, extensibility and m...With the continuous development and evolvement of Internet of Things （IoT）, monolithic application becomes much larger in scale and even more complex in structure. This leads to poor scalability, extensibility and maintainability. In response to those challenges, microservice architecture has been introduced in the field of IoT application, due to its flexibility, lightweight and loose coupling. However, the existing loT framework of microservice mainly focus on a specific domain, therefore, this greatly limits its application. In this paper, we propose a general microservice system framework for the loT application, which is a better scalable, extendable and maintainable architecture. We introduce its system design and related microservices, and emphasize on core service and device communication from service layer to physical layer. It has better capacity to support interoperability and accommodate heterogeneous objects. In addition, this framework can easily achieve more application integration such as automation, intelligence, Geo service and Big Data.展开更多
With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing co...With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing concern.This has led to great difficulty in establishing trust between the users and the service providers,hindering the development of LBS for more comprehensive functions.In this paper,we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem.This mechanism not only guarantees the confidentiality of the user s information during the subsequent information interaction and dynamic data transmission,but also meets the service provider's requirements for related data.展开更多
Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of he...Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human life.There are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and telemedicine.These applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service management.The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users.The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services.There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model.The proposed ontological model is derived from different classes,relationships,and constraints to integrate health care services.This model is developed using Protégébased on each interrelated/correlated health care service having different values.Semantic querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge acquisition.The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model.Comparative to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.展开更多
With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key te...With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.展开更多
The development of the Internet of Things(IoT)has rapidly progressed,revolutionizing numerous industries and transforming how we interact with technology.IoT relies on seamless connectivity between devices,allowing th...The development of the Internet of Things(IoT)has rapidly progressed,revolutionizing numerous industries and transforming how we interact with technology.IoT relies on seamless connectivity between devices,allowing them to collect,share,and analyze data.As a result,compatibility and connectivity have become major concerns for customers adopting smart devices.Achieving compatibility in a smart device is a shared responsibility between the IoT platform provider and the smart device manufacturer.However,the impact of their respective efforts on smart device compatibility depends on investment efficiency and the service level.In this study,we develop a game-theoretical model to examine how investment efficiency affects the incentives of the upstream platform and the downstream manufacturer to exert compatibility efforts under the licensing pricing and revenue sharing models in a supply chain setting.Our findings indicate that,given a certain IoT platform service level,a manufacturer’s effort decreases as the relative weight decreases in the licensing pricing model.Additionally,when the platform’s effort cost is lower,the platform can invest more in compatibility,thereby improving the compatibility of smart devices.In such cases,the manufacturer can benefit from the platform’s incentives to exert compatibility efforts by reducing her efforts.We also identify two effects:the compatibility effect and the service value-added effect.When the relative weight of compatibility is small,the role of platform compatibility diminishes.Consequently,the platform cannot increase technology licensing fees to augment IoT service income,reducing profits.Our results also provide insights into how an IoT platform provider can strategically target a particular IoT service based on their cost characteristics and service level in smart device supply chains.展开更多
研究了物联网的设计过程,根据物联网的三层结构,提出了一种面向搜索的Restful Web Service的三段式物联网设计方法,根据该方法,设计并实现了一个制造服务管理系统物联网项目。研究表明,基于Restful Web Service的三段式设计理论对物联...研究了物联网的设计过程,根据物联网的三层结构,提出了一种面向搜索的Restful Web Service的三段式物联网设计方法,根据该方法,设计并实现了一个制造服务管理系统物联网项目。研究表明,基于Restful Web Service的三段式设计理论对物联网的实现具有很重要的应用价值。展开更多
文摘Editor＇s Desk：The Internet of Things is an important part of the emerging high-tech industry and has been recognized as one of the important technologies for addressing global financial crisis and revitalizing the economy. Therefore, at the beginning of the new year, we are honored to have invited Mr. Samuel Qi （Qi Qingzhong）, a famous Chinese communication technology expert and GM of Shanghai Symphony Telecommunications Co., Ltd., to give his views on the development strategy of the Internet of Things and Machine to Machine （M2M） Communications.
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%.
基金supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No.2021R1C1C1013133)supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP)grant funded by the Korea Government (MSIT) (RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for The Smart City)supported by the Soonchunhyang University Research Fund.
文摘In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.
基金Supported by the China Postdoctoral Science Foundation(No. 20100480701)the Ministry of Education of Humanities and Social Sciences Youth Fund Project(11YJC880119)
文摘Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(2020R1A2B5B01002145).
文摘The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service composition.The SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings.In IoT,an object can offer multiple services and different objects can offer the same services with different parameters and interest factors.The proliferation of offered services led to difficulties during service customization and service filtering.This problem is known as service explosion.The selection of suitable service that fits the requirements of applications and objects is a challenging task.To address these issues,we propose an efficient automated query-based service search model based on the local network navigability concept for the SIoT.In the proposed model,objects can use information from their friends or friends of their friends while searching for the desired services,rather than exploring a global network.We employ a centrality metric that computes the degree of importance for each object in the social IoT that helps in selecting neighboring objects with high centrality scores.The distributed nature of our navigation model results in high scalability and short navigation times.We verified the efficacy of our model on a real-world SIoT-related dataset.The experimental results confirm the validity of our model in terms of scalability,navigability,and the desired objects that provide services are determined quickly via the shortest path,which in return improves the service search process in the SIoT.
文摘The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Although the basic concept of the Internet of Things sounds simple, its application is difficult and, so far, the respective existing architectural models are rather monolithic and are dominated by several limitations. The paper introduces a generic Internet of Things architecture trying to resolve the existing restrictions of current architectural models by integrating both RFID and smart object-based infrastructures, while also exploring a third parameter, i.e. the social potentialities of the Internet of Things building blocks towards shaping the “Social Internet of Things”. The proposed architecture is based on a layered lightweight and open middle-ware solution following the paradigm of Service Oriented Architecture and the Semantic Model Driven Ap-proach, which is realized at both design-time and deployment–time covering the whole service lifecycle for the corresponding services and applications provided.
基金This work was supported in part by the National Key R&D Program of China under Grant 2020YFA0711301in part by the National Natural Science Foundation of China under Grant 61922049,and Grant 61941104in part by the Tsinghua University-China Mobile Communications Group Company Ltd.,Joint Institute.
文摘With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT devices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the access network of 6G IoT.Based on historical communication behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.
文摘In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.
文摘Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.
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funded by the Deanship of Scientific Research at Najran University for this research through a Grant(NU/RG/SERC/12/50)under the Research Groups at Najran University,Saudi Arabia.
文摘The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.
文摘The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed blocking and other such consequences. The problem is exacerbated by the growth in size, complexity and the number of chronic diseases in the NHS. In addition, there is an increase in demand for high quality care, processes and planning. Effective Discharge Planning (DP) requires practitioners to have appropriate, patient personalised and updated knowledge in order to be able to make informed and holistic decisions about a patients’ discharge. This paper examines the role of Knowledge Management (KM) in both sharing knowledge and using tacit knowledge to create appropriate patient discharge pathways. The paper details the factors resulting in inadequate DP, and demonstrates the use of Internet of Things (IoT) and Machine2Machine (M2M) as candidate technologies and possible solutions which can help reduce the problem. The use of devices that a patient can take home and devices which are perused in the hospital generate information, which can serve useful when presented to the right person at the right time, thus harvesting knowledge. The knowledge when fed back can support practitioners in making holistic decisions with regards to a patients’ discharge.
文摘针对过去几年物联网以垂直行业应用为主,系统部署成本高,缺乏系统间跨行业的资源共享,用户只能被动接受服务,无法参与到服务的生成中,不利推广等问题,将Web技术作为Internet of Things实现和推广的手段,对目前国内外Web of Things工作进行了总结分析,提出了扁平化的Web of Things开放架构。首先分析了现有Inter-net of Things架构的不足和需求,并结合现有的Web of Things概念模型,提出了Web of Things开放架构和其中的关键技术、开放模式。最后,对Web of Things和Internet of Things进行了系统对比。
文摘With the continuous development and evolvement of Internet of Things （IoT）, monolithic application becomes much larger in scale and even more complex in structure. This leads to poor scalability, extensibility and maintainability. In response to those challenges, microservice architecture has been introduced in the field of IoT application, due to its flexibility, lightweight and loose coupling. However, the existing loT framework of microservice mainly focus on a specific domain, therefore, this greatly limits its application. In this paper, we propose a general microservice system framework for the loT application, which is a better scalable, extendable and maintainable architecture. We introduce its system design and related microservices, and emphasize on core service and device communication from service layer to physical layer. It has better capacity to support interoperability and accommodate heterogeneous objects. In addition, this framework can easily achieve more application integration such as automation, intelligence, Geo service and Big Data.
基金This work has been partly supported by the National Natural Science Foundation of China under Grant No.61702212the Fundamental Research Funds for the Central Universities under Grand NO.CCNU19TS017.
文摘With the rapid development of the Internet of Things(IoT),Location-Based Services(LBS)are becoming more and more popular.However,for the users being served,how to protect their location privacy has become a growing concern.This has led to great difficulty in establishing trust between the users and the service providers,hindering the development of LBS for more comprehensive functions.In this paper,we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem.This mechanism not only guarantees the confidentiality of the user s information during the subsequent information interaction and dynamic data transmission,but also meets the service provider's requirements for related data.
基金the Deanship of Scientific Research(DSR),King Abdul-Aziz University,Jeddah,Saudi Arabia under Grant No.(D-504-611-1443).
文摘Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human life.There are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and telemedicine.These applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service management.The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users.The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services.There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model.The proposed ontological model is derived from different classes,relationships,and constraints to integrate health care services.This model is developed using Protégébased on each interrelated/correlated health care service having different values.Semantic querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge acquisition.The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model.Comparative to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.
基金supported by National Natural Science Foundation of China under Grants No.62076249,62022092,62293545.
文摘With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.
基金supported in part by International Chinese Language Education Research Foundation of Ministry of Education of China,under Grant No.23YH39DPhilosophical and Social Science Planning Project of Tianjin,under grants No.TJGLQN23-006。
文摘The development of the Internet of Things(IoT)has rapidly progressed,revolutionizing numerous industries and transforming how we interact with technology.IoT relies on seamless connectivity between devices,allowing them to collect,share,and analyze data.As a result,compatibility and connectivity have become major concerns for customers adopting smart devices.Achieving compatibility in a smart device is a shared responsibility between the IoT platform provider and the smart device manufacturer.However,the impact of their respective efforts on smart device compatibility depends on investment efficiency and the service level.In this study,we develop a game-theoretical model to examine how investment efficiency affects the incentives of the upstream platform and the downstream manufacturer to exert compatibility efforts under the licensing pricing and revenue sharing models in a supply chain setting.Our findings indicate that,given a certain IoT platform service level,a manufacturer’s effort decreases as the relative weight decreases in the licensing pricing model.Additionally,when the platform’s effort cost is lower,the platform can invest more in compatibility,thereby improving the compatibility of smart devices.In such cases,the manufacturer can benefit from the platform’s incentives to exert compatibility efforts by reducing her efforts.We also identify two effects:the compatibility effect and the service value-added effect.When the relative weight of compatibility is small,the role of platform compatibility diminishes.Consequently,the platform cannot increase technology licensing fees to augment IoT service income,reducing profits.Our results also provide insights into how an IoT platform provider can strategically target a particular IoT service based on their cost characteristics and service level in smart device supply chains.
