Research On Multi-dimensional Resource Collaboration And Efficient Computional Offloading Strategy In Heterogeneous Iot

The rapid development of new heterogeneous Internet of Things applications puts higher demands on the elaborative management of the spectrum resources,power resources and computational resources of wireless networks.In order to solve the above problems,the introduction of NOMA technology in the Internet of Things can improve the spectrum efficiency and energy efficiency in the wireless network,reduce the communication delay,and support more access devices.In addition,offloading the locally executed computional tasks to the remote cloud or surrounding collaborative users can efficiently exploit computional resources,thereby reducing computational latency and energy consumption.This paper first clarifies the background and significance of the topic,and discusses the current research status of multi-dimensional resource coordination and efficient computational offloading strategy in heterogeneous Internet of Things.Firstly,this paper proposes a social networking and caching-assisted collaborative computing system framework for heterogeneous Internet of Things scenarios,which effectively reduces system delay and energy consumption by combining social factors between users and collaborative caching and collaborative computing based on NOMA cluster,which also further demonstrates the importance of social factors in large-scale device access scenarios.In a multi-user IoT communication scenario with a single data distributor,the full-duplex relay cooperative NOMA communication mode is put forward to maximize the ergodic sum rate by optimizating the resource allocation scheme.To reduce computational complexity,the upper bound of the ergodic sum rate is derived.Then the original problem is divided into NOMA user pairing problem and resource allocation problem including spectrum resource and power resource optimization,and is further solved by a continuous iterative resource allocation algorithm.Simulation shows that compared with other schemes,the proposed algorithm can further achieve a balance between complexity and optimality.In addition,in the multi-user computional offloading scenario,this paper puts forward an optimized selection scheme of collaborative users in the multi-offloading mode,including NOMA pair computational offloading mode,single user computational offloading mode and base station computational offloading mode,to minimize the system computional offloading delay.Considering the channel state and user social behaviors between the user with computational tasks to be offloaded and the collaborative user,the optimal computional offloading mode and the collaborative user are selected for the offloading user.When the NOMA pair offloading mode is adopted,the computioal task partition scheme within the NOMA pair is further optimized.The simulation results show that the proposed social behavior based NOMA cooperative scheme can effectively improve the system data rate and reduce the system delay and energy consumption.