As the rapid growth of mobile social networks,mobile peer-to-peer(P2P)communications and mobile edge computing(MEC)have been developed to reduce the traffic load and improve the computation capacity of cellular networ...As the rapid growth of mobile social networks,mobile peer-to-peer(P2P)communications and mobile edge computing(MEC)have been developed to reduce the traffic load and improve the computation capacity of cellular networks.However,the stability of social network is largely ignored in the advances of P2P and MEC,which is related to the social relations between users.It plays a vital role in improving the efficiency and reliability of traffic offloading service.In this paper,we integrate an edge node and the nearby P2P users as a mobile P2P social network and introduce the problem of adaptive anchored(k,r)-core to maintain the stability of multiple mobile P2P networks.It aims to adaptively select and retain a set of critical users for each network,whose participation is critical to overall stability of the network,and allocate certain resource for them so that the maximum number of users of all networks will remain engaged and the traffic of cellular network can be minimized.We called the retained users as anchor vertices.To address it,we devise a peer-edge-cloud framework to achieve the adaptive allocation of resources.We also develop a similarity based onion layers anchored(k,r)-core(S-OLAK)algorithm to explore the anchor vertices.Experimental results based on a real large-scale mobile P2P data set demonstrate the effectiveness of our method.展开更多
Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support th...Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support the computational demands of DTs.However,the decentralized nature of MEC systems introduces security challenges and traditional HetVNets fail to efficiently integrate diverse computing and network resources,limiting their ability to handle services for vehicles.This paper presents a novel service request offloading framework for DT-HetVNets to address these issues.In this framework,we design utility functions for vehicles and infrastructures to maximize satisfaction of their requirements through data synchronization and decision-making between DTs and entities.Furthermore,we propose a new honestly based distributed PoA(HDPoA)via scalable work.The interactions between infrastructures and vehicles are modeled as a multi-leader multi-follower(MLMF)game,and we develop a dynamic iterative algorithm to achieve the Nash equilibrium(NE)of the proposed game-theoretic model.Experimental results validate the effectiveness and accuracy of our scheme.展开更多
Smart city pollution control is fundamental to urban sustainability,which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring.Generally,monitoring data needs to be transm...Smart city pollution control is fundamental to urban sustainability,which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring.Generally,monitoring data needs to be transmitted to centralized servers for pollution control service determination.In order to achieve highly efficient service quality,edge computing is involved in the smart city pollution control system(SCPCS)as it provides computational capabilities near the monitoring devices and low-latency pollution control services.However,considering the diversity of service requests,determination of offloading destination is a crucial challenge for SCPCS.In this paper,A Deep Q-Network(DQN)-based edge offloading method,called N-DEO,is proposed.Initially,N-DEO employs neural hierarchical interpolation for time series forecasting(N-HITS)to forecast pollution control service requests.Afterwards,an epsilon-greedy policy is designed to select actions.Finally,the optimal service offloading strategy is determined by the DQN algorithm.Experimental results demonstrate that N-DEO achieves the higher performance on service latency and system load compared with the current state-of-the-art methods.展开更多
基金This work was supported by National Key Research and Development Program of China under Grant 2019YFB2101901 and 2018YFC0809803National Natural Science Foundation of China under Grant 61702364.
文摘As the rapid growth of mobile social networks,mobile peer-to-peer(P2P)communications and mobile edge computing(MEC)have been developed to reduce the traffic load and improve the computation capacity of cellular networks.However,the stability of social network is largely ignored in the advances of P2P and MEC,which is related to the social relations between users.It plays a vital role in improving the efficiency and reliability of traffic offloading service.In this paper,we integrate an edge node and the nearby P2P users as a mobile P2P social network and introduce the problem of adaptive anchored(k,r)-core to maintain the stability of multiple mobile P2P networks.It aims to adaptively select and retain a set of critical users for each network,whose participation is critical to overall stability of the network,and allocate certain resource for them so that the maximum number of users of all networks will remain engaged and the traffic of cellular network can be minimized.We called the retained users as anchor vertices.To address it,we devise a peer-edge-cloud framework to achieve the adaptive allocation of resources.We also develop a similarity based onion layers anchored(k,r)-core(S-OLAK)algorithm to explore the anchor vertices.Experimental results based on a real large-scale mobile P2P data set demonstrate the effectiveness of our method.
基金supported by the National Natural Science Foundation of China(No 62371250)the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu(No BK20212001)the Jiangsu Natural Science Foundation for Distinguished Young Scholars(No BK20220054).
文摘Recent advances in integrating Digital Twins(DTs)with Heterogeneous Vehicular Networks(HetVNets)enhance decision-making and improve network performance.Additionally,developments in Mobile Edge Computing(MEC)support the computational demands of DTs.However,the decentralized nature of MEC systems introduces security challenges and traditional HetVNets fail to efficiently integrate diverse computing and network resources,limiting their ability to handle services for vehicles.This paper presents a novel service request offloading framework for DT-HetVNets to address these issues.In this framework,we design utility functions for vehicles and infrastructures to maximize satisfaction of their requirements through data synchronization and decision-making between DTs and entities.Furthermore,we propose a new honestly based distributed PoA(HDPoA)via scalable work.The interactions between infrastructures and vehicles are modeled as a multi-leader multi-follower(MLMF)game,and we develop a dynamic iterative algorithm to achieve the Nash equilibrium(NE)of the proposed game-theoretic model.Experimental results validate the effectiveness and accuracy of our scheme.
基金supported by the National Natural Science Foundation of China(No.92267104 and 62372242)in part by the Natural Science Foundation of Jiangsu Province of China(No.BK20211284)the NUIST Students’Platform for Innovation and Entrepreneurship Training Program(No.202410300059Z).
文摘Smart city pollution control is fundamental to urban sustainability,which relies extensively on physical infrastructure such as sensors and cameras for real-time monitoring.Generally,monitoring data needs to be transmitted to centralized servers for pollution control service determination.In order to achieve highly efficient service quality,edge computing is involved in the smart city pollution control system(SCPCS)as it provides computational capabilities near the monitoring devices and low-latency pollution control services.However,considering the diversity of service requests,determination of offloading destination is a crucial challenge for SCPCS.In this paper,A Deep Q-Network(DQN)-based edge offloading method,called N-DEO,is proposed.Initially,N-DEO employs neural hierarchical interpolation for time series forecasting(N-HITS)to forecast pollution control service requests.Afterwards,an epsilon-greedy policy is designed to select actions.Finally,the optimal service offloading strategy is determined by the DQN algorithm.Experimental results demonstrate that N-DEO achieves the higher performance on service latency and system load compared with the current state-of-the-art methods.
