The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests gro...The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests grows,existing approaches suffer from increased discovery time and degraded Quality of Service(QoS).Moreover,the massive data generated by heterogeneous IoT devices often contain redundancy and noise,posing challenges to efficient data management.To address these issues,this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management.The architecture employs Modified-Ordered Points to Identify theClustering Structure(M-OPTICS)to cluster and eliminate redundant IoT data.The clustered data are then modelled into a lightweight ontology,enabling semantic relationship inference and rule generation through an embedded inference engine.User requests,transmitted via theConstrainedApplication Protocol(CoAP),are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm.Semantic matching is further refined using a bidirectional recurrent neural network(Bi-RNN),while a State–Action–Reward–State–Action(SARSA)reinforcement learning model dynamically defines and updates semantic rules to retrieve themost recent and relevant data across heterogeneous devices.Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time,service delay,execution time,precision,recall,and F-score under varying CoAP request loads and communication overheads.The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.展开更多
针对物联网(Internet of Things,IoT)的安全和隐私问题,以及传统的访问控制方法不适应于IoT环境的现状,提出了一种分布式的基于上下文和权能的访问控制架构.该架构的授权决策过程由嵌入到设备中的授权决策模块PDP来实现,以达到分布式的...针对物联网(Internet of Things,IoT)的安全和隐私问题,以及传统的访问控制方法不适应于IoT环境的现状,提出了一种分布式的基于上下文和权能的访问控制架构.该架构的授权决策过程由嵌入到设备中的授权决策模块PDP来实现,以达到分布式的授权目标;特别是权能令牌的特殊构造,不仅可方便实现基于设备上下文的访问控制,而且利用椭圆曲线密码体制来实现端到端的认证、完整性和不可抵赖性;消息传输机制采用更适合于物联网的受限应用协议CoAP(Constrained Application Protocol).实验结果表明,该架构是可行的.展开更多
文摘The Internet of Things(IoT)ecosystem is inherently heterogeneous,comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange.However,as the number of service requests grows,existing approaches suffer from increased discovery time and degraded Quality of Service(QoS).Moreover,the massive data generated by heterogeneous IoT devices often contain redundancy and noise,posing challenges to efficient data management.To address these issues,this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management.The architecture employs Modified-Ordered Points to Identify theClustering Structure(M-OPTICS)to cluster and eliminate redundant IoT data.The clustered data are then modelled into a lightweight ontology,enabling semantic relationship inference and rule generation through an embedded inference engine.User requests,transmitted via theConstrainedApplication Protocol(CoAP),are semantically enriched and matched to QoS parameters using Dynamic Shannon Entropy optimized with the Salp Swarm Algorithm.Semantic matching is further refined using a bidirectional recurrent neural network(Bi-RNN),while a State–Action–Reward–State–Action(SARSA)reinforcement learning model dynamically defines and updates semantic rules to retrieve themost recent and relevant data across heterogeneous devices.Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time,service delay,execution time,precision,recall,and F-score under varying CoAP request loads and communication overheads.The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.
文摘针对物联网(Internet of Things,IoT)的安全和隐私问题,以及传统的访问控制方法不适应于IoT环境的现状,提出了一种分布式的基于上下文和权能的访问控制架构.该架构的授权决策过程由嵌入到设备中的授权决策模块PDP来实现,以达到分布式的授权目标;特别是权能令牌的特殊构造,不仅可方便实现基于设备上下文的访问控制,而且利用椭圆曲线密码体制来实现端到端的认证、完整性和不可抵赖性;消息传输机制采用更适合于物联网的受限应用协议CoAP(Constrained Application Protocol).实验结果表明,该架构是可行的.
