机会网络中基于博弈论的节能信标控制策略

Game Theory-based Control Strategy for Energy-efficient Beacons in Opportunistic Networks

摘要

机会网络中,由于大部分终端设备的可用能源有限,如何在数据传输过程中有效地节省能源成为提升网络性能的关键.本文提出一种基于博弈论的节能信标控制策略GTEEB(A Game-Theoretic Energy-Efficient Beaconing Strategy).该策略构建基于节点收益的博弈模型并利用纳什均衡理论动态调整节点的信标频率,推导出节点选择信标频率的能耗阈值,平衡节点在高、低频信标下的能量消耗与通信机会.同时引入节点自主决策机制使节点能够依据周围环境状态自适应地调节信标频率,实现不同信标频率下的能量消耗与通信概率之间的平衡,有效降低整体网络能耗.仿真实验结果表明,与ST-Prophet、EASE和TLEE等节能方案相比,GTEEB策略能够在保证网络连通性和数据传输质量的同时降低能耗,延长网络平均寿命.

Abstract

In opportunistic networks,due to the limited available energy of most terminal devices,how to effectively conserve energy during data transmission has become key to enhancing network performance.This paper proposes an energy-efficient beaconing strategy based on game theory,named GTEEB(A Game-Theoretic Energy-Efficient Beaconing Strategy).The strategy constructs a game model based on node benefits and dynamically adjusts the beacon frequency of nodes using Nash equilibrium theory,deducing an energy consumption threshold for node beacon frequency selection.This balances the energy consumption and communication opportunities of nodes under high and low beacon frequencies.It also introduces a node autonomous decision-making mechanism,enabling nodes to adaptively adjust their beacon frequency according to the surrounding environmental state.This achieves a balance between energy consumption and communication probability at different beacon frequencies,thereby effectively reducing overall network energy consumption.Simulation results show that compared with energy-saving schemes such as ST-Prophet,EASE,and TLEE,the GTEEB strategy can significantly reduce energy consumption and extend the average network lifetime while ensuring network connectivity and data transmission quality.

关键词

机会网络 / 博弈论 / 信标控制 / 节能路由

Key words

opportunistic networks / game theory / beacon control / energy-efficient routing

[1] Pozza R,Nati M,Georgoulas S,et al.Neighbor discovery for opportunistic networking in internet of things scenarios:a survey[J].IEEE Access,2015,3:1101-1131,doi:10.1109/ACCESS.2015.2457031.
[2] Loreti P,Bracciale L.Optimized Neighbor discovery for opportunistic networks of energy constrained IoT devices[J].IEEE Transactions on Mobile Computing,2020,19(6):1387-1400,doi:10.1109/TMC.2019.2908402.
[3] Chouaf W,Abbou A,Agga A.Optimal energy management for a connected microgrid using dynamic programming method[C]//8th International Conference on Optimization and Applications(ICOA),IEEE,2022:1-6.
[4] Yuan S,Li S,Guo S,et al.Research on dynamic adjustment strategy of charging power in station area based on intelligent energy control[C]//International Conference on Artificial Intelligence and Power Systems(AIPS),IEEE,2024:326-329.
[5] Nash J F.Non-cooperative games[M]//The Foundations of Price Theory Vol 4,Routledge,2024:329-340.
[6] Yan X,Huang C,Gan J,et al.Game theory-based energy-efficient clustering algorithm for wireless sensor networks[J].Sensors,2022,22(2):478,doi:10.3390/s22020478.
[7] González I,Calderón A J,Folgado F J.IoT real time system for monitoring lithiumion battery long-term operation in microgrids[J].Journal of Energy Storage,2022,51:104596,doi:10.1016/j.est.2022.104596.
[8] Basu S,Roy S,Bandyopadhyay S,et al.A utility driven post disaster emergency resource allocation system using DTN[J].IEEE Transactions on Systems,Man,and Cybernetics,2020,50(7):2338-2350,doi:10.1109/TSMC.2018.2813008.
[9] Liu Y,Wang Y,Shen Y.Demand-aware distributed link allocation in a multilayer heterogeneous satellite network:a game theory approach[J].IEEE Internet of Things Journal,2024,11(10):17629-17641,doi:10.1109/JIOT.2024.3358388.
[10] Hao J,Su Y,Zhang D,et al.Optimal allocation of microgrid power supply considering dynamic energy management control strategies[C]//5th International Conference on Clean Energy and Electric Power Engineering(ICCEPE),IEEE,2024:869-873.
[11] Shirvani S,Samanta A,Li Z,et al.Duojoule:accurate on-device deep reinforcement learning for energy and timeliness[C]//IEEE Real-Time Systems Symposium(RTSS),2024:109-122.
[12] Eshghi S,Khouzani M H R,Sarkar S,et al.Optimal energy-aware epidemic routing in DTNs[J].IEEE Transactions on Automatic Control,2015,60(6):1554-1569,doi:10.1109/TAC.2015.2396641.
[13] Das P,Nishantkar P,De T.SECA on MIA-DTN:tackling the energy issue in monitor incorporated adaptive delay tolerant network using a simplistic energy conscious approach[J].Journal of Network and Systems Management,2019,27(1):121-148,doi:10.1007/s10922-018-9461-4.
[14] Singh A K,Pamula R,Srivastava G.An adaptive energy aware DTN-based communication layer for cyber-physical systems[J].Sustainable Computing:Informatics and Systems,2022,35:100657,doi:10.1016/j.suscom.2022.100657.
[15] Zhao Q,Yang W,Zhang L.Energy-efficient opportunistic routing algorithm for post-disaster mine internet of things networks[J].Sensors,2023,23(16):7213,doi:10.3390/s23167213.
[16] Sharifi Sani M,Iranmanesh S,Salarian H,et al.Optimizing energy efficiency in opportunistic networks:a heuristic approach to adaptive cluster-based routing protocol[J].Information,2024,15(5):283,doi:10.3390/info15050283.
[17] Fu Y,Zhou B,Xu G,et al.An energy recovery routing algorithm for opportunistic networks based on connection stability and node encounter type[J].Ad Hoc Networks,2024,157:103455,doi:10.1016/j.adhoc.2024.103455.
[18] Wang Y,Karimi M,Kim H.Towards energy-efficient real-time scheduling of heterogeneous multi-GPU systems[C]//IEEE Real-Time Systems Symposium(RTSS),2022:409-421.
[19] Wang Y,Karimi M,Xiang Y,et al.Balancing energy efficiency and real-time performance in GPU scheduling[C]//IEEE Real-Time Systems Symposium,2021:110-122.
[20] Reghenzani F,Bhuiyan A,Fornaciari W,et al.A multi-level DPM approach for real-time DAG tasks in heterogeneous processors[C]//IEEE Real-Time Systems Symposium(RTSS),2021:14-26.
[21] Airlangga G,Sihombing D J C,Bata J,et al.Optimizing femtocell networks:a novel game theory based power management model for enhanced SINR and energy efficiency[J].IEEE Access,2024,12:74444-74455,doi:10.1109/ACCESS.2024.3405534.
[22] Yang Y,Liu W,Wang E,et al.Beaconing control strategy based on game theory in mobile crowdsensing[J].Future Generation Computer Systems,2018,86:222-233,doi:10.1016/j.future.2018.04.013.
[23] De Rango F,Amelio S.Geographic and energy aware epidemic strategy for mobile opportunistic DTN[C]//29th International Conference on Computer Communications and Networks(ICCCN),2020:1-8.

基金资助

国家自然科学基金面上项目(62272189,62372206)资助.