The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ap...The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.展开更多
在LEACH协议的基础上进行改进提出了一种高能效无线传感器网络协议——LEACH-M。LEACH协议中,簇首节点与基站之间直接传送数据,离基站较远区域的簇首能耗较大,这影响了系统寿命。LEACH-M协议在簇首形成阶段采用CSMA/CA(carrier sense mu...在LEACH协议的基础上进行改进提出了一种高能效无线传感器网络协议——LEACH-M。LEACH协议中,簇首节点与基站之间直接传送数据,离基站较远区域的簇首能耗较大,这影响了系统寿命。LEACH-M协议在簇首形成阶段采用CSMA/CA(carrier sense multi-access with collision avoidance)作为MAC协议,并在簇首节点与基站之间引入了改进的多跳路由算法,使网络中各簇的能耗更加均匀。仿真结果表明,与LEACH相比,LEACH-M协议具有更好的能量有效性,并且提高了无线传感器网络的寿命。展开更多
Multi-access interference(MAI)is the main source limiting the capacity and quality of the multiple-input multipleoutput orthogonal frequency division multiplexing(MIMO-OFDM)system which fulfills the demand of high-spe...Multi-access interference(MAI)is the main source limiting the capacity and quality of the multiple-input multipleoutput orthogonal frequency division multiplexing(MIMO-OFDM)system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic(UWA)communication.Therefore,multi-user detection(MUD)is needed at the receiver of the MIMO-OFDM system to suppress the effect of MAI.In this research,MUD is achieved by using a criterion based adaptive recursive successive interference cancellation(RSIC)scheme at the receiver of a MIMO-OFDM system whose transceiver model in underwater communication is implemented by using the Bellhop simulation system.The proposed scheme estimates and eliminates the MAI through user signal detection and subtraction from received signals at the receiver of the MIMO-OFDM system in underwater environment.The bit error rate(BER)performance of the proposed scheme is analyzed by using weight filtering and weight selection criteria.By Matlab simulation,it is shown that the BER performance of the proposed scheme outperforms the conventional matched filter(MF)detector,the adaptive successive interference cancellation(SIC)scheme,and the adaptive RSIC scheme in the UWA network.展开更多
文摘The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
文摘在LEACH协议的基础上进行改进提出了一种高能效无线传感器网络协议——LEACH-M。LEACH协议中,簇首节点与基站之间直接传送数据,离基站较远区域的簇首能耗较大,这影响了系统寿命。LEACH-M协议在簇首形成阶段采用CSMA/CA(carrier sense multi-access with collision avoidance)作为MAC协议,并在簇首节点与基站之间引入了改进的多跳路由算法,使网络中各簇的能耗更加均匀。仿真结果表明,与LEACH相比,LEACH-M协议具有更好的能量有效性,并且提高了无线传感器网络的寿命。
文摘Multi-access interference(MAI)is the main source limiting the capacity and quality of the multiple-input multipleoutput orthogonal frequency division multiplexing(MIMO-OFDM)system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic(UWA)communication.Therefore,multi-user detection(MUD)is needed at the receiver of the MIMO-OFDM system to suppress the effect of MAI.In this research,MUD is achieved by using a criterion based adaptive recursive successive interference cancellation(RSIC)scheme at the receiver of a MIMO-OFDM system whose transceiver model in underwater communication is implemented by using the Bellhop simulation system.The proposed scheme estimates and eliminates the MAI through user signal detection and subtraction from received signals at the receiver of the MIMO-OFDM system in underwater environment.The bit error rate(BER)performance of the proposed scheme is analyzed by using weight filtering and weight selection criteria.By Matlab simulation,it is shown that the BER performance of the proposed scheme outperforms the conventional matched filter(MF)detector,the adaptive successive interference cancellation(SIC)scheme,and the adaptive RSIC scheme in the UWA network.
