Charging scheduling is a very important item for wireless rechargeable sensor networks. Existing researches mainly focus on scheduling charging vehicles to obtain the optimal mobile path. However, these algorithms cannot provide good performance when traffic is restricted. Considering the mobile charging vehicle scheduling problem with traffic road constraints, this paper proposes a mobility constrained charging scheduling scheme (MCCS). To better fit the actual scene, we formalize the problem as an edge coverage problem, and enhance the classical MAENS algorithm by adding a path decomposition operator and a mutation operator. The performance of MCCS is evaluated by extensive simulations. Compared with MAENS, experimental results show that MCCS achieves superior performance in terms of low average energy consumption and high charging stability.
ZHONG Ping, CHEN Yuanming, DU Zhicheng, LI Lin, GUI Lin
. A Charging Vehicle Scheduling Scheme with Traffic Road Restrictions[J]. Journal of Applied Sciences, 2021
, 39(2)
: 199
-209
.
DOI: 10.3969/j.issn.0255-8297.2021.02.002
[1] Sarker M R, Julai S, Sabri M F M, et al. Review of piezoelectric energy harvesting system and application of optimization techniques to enhance the performance of the harvesting system[J]. Sensors and Actuators A:Physical, 2019, 300:111634.
[2] Ren J, Luo Q, Hou Q Z, et al. Suppressing charge recombination and ultraviolet light degradation of perovskite solar cells using silicon oxide passivation[J]. ChemElectroChem, 2019, 6(12):3167-3174.
[3] Zhang Y D, An Y F, Wu L Y, et al. Metal-free energy storage systems:combining batteries with capacitors based on a methylene blue functionalized graphene cathode[J]. Journal of Materials Chemistry A, 2019, 7(34):19668-19675.
[4] Fu X, Fortino G, Pace P, et al. Environment-fusion multipath routing protocol for wireless sensor networks[J]. Information Fusion, 2020, 53:4-19.
[5] Ma Y, Liang W, Xu W. Charging utility maximization in wireless rechargeable sensor networks by charging multiple sensors simultaneously[J]. IEEE/ACM Transactions on Networking, 2018, 26(4):1591-1604.
[6] Chen X, Wu T. Region segmentation model for wireless sensor networks considering optimal energy conservation constraints[J]. Cluster Computing, 2019, 22(3):7507-7514.
[7] Zhou P, Wang C, Yang Y. Self-sustainable sensor networks with multi-source energy harvesting and wireless charging[C]//IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019:1828-1836.
[8] He X, Fu X, Yang Y. Energy-efficient trajectory planning algorithm based on multi-objective PSO for the mobile sink in wireless sensor networks[J]. IEEE Access, 2019, 7:176204-176217.
[9] Ko H, Lee J, Pack S. CG-E2S2:consistency-guaranteed and energy-efficient sleep scheduling algorithm with data aggregation for IoT[J]. Future Generation Computer Systems, 2019, 92:1093-1102.
[10] Wang Y, Chen H, Wu X, et al. An energy-efficient SDN based sleep scheduling algorithm for WSNs[J]. Journal of Network and Computer Applications, 2016, 59:39-45.
[11] Niyato D, Hossain E, Fallahi A. Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks:performance analysis and optimization[J]. IEEE Transactions on Mobile Computing, 2007, 6(2):221-236.
[12] Curry J, Harris N. Powering the environmental internet of things[J]. Sensors, 2019, 19(8):1940.
[13] Valera A C, Soh W S, Tan H P. Survey on wakeup scheduling for environmentally-powered wireless sensor networks[J]. Computer Communications, 2014, 52:21-36.
[14] Fu X, Fortino G, Li W, et al. WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings[J]. Future Generation Computer Systems, 2019, 91:223-237.
[15] Priyadarshani S, Tomar A, Jana P K. An efficient partial charging scheme using multiple mobile chargers in wireless rechargeable sensor networks[J]. Ad Hoc Networks, 2020, 113:102407.
[16] Rao X, Yang P, Yan Y, et al. Optimal recharging with practical considerations in wireless rechargeable sensor network[J]. IEEE Access, 2017, 5:4401-4409.
[17] Rao X, Yang P, Yan Y. Optimizing tours for mobile chargers with roadside segment coverage[C]//Proceedings of the Second International Conference on Internet-of-Things Design and Implementation. ACM, 2017:349-350.
[18] Rao X, Yang P, Yan Y. You can charge over the road:optimizing charging tour in urban area[C]//International Conference on Wireless Algorithms, Systems, and Applications. Springer, 2017:768-779.
[19] Tomar A, Muduli L, Jana P K. An efficient scheduling scheme for on-demand mobile charging in wireless rechargeable sensor networks[J]. Pervasive and Mobile Computing, 2019, 59:101074.
[20] Cao X, Xu W, Liu X, et al. A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks[J]. Ad Hoc Networks, 2021, 110:102278.
[21] Feng Y, Zhang W, Han G, et al. A newborn particle swarm optimization algorithm for charging-scheduling algorithm in industrial rechargeable sensor networks[J]. IEEE Sensors Journal, 2020, 20(18):11014-11027.
[22] Golden B L, Wong R T. Capacitated arc routing problems[J]. Networks, 1981, 11(3):305-315.
[23] Tang K, Mei Y, Yao X. Memetic algorithm with extended neighborhood search for capacitated arc routing problems[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(5):1151-1166.
[24] Eglese R W, Li L Y O. A tabu search based heuristic for arc routing with a capacity constraint and time deadline[M]. Boston:Springer, 1996.
[25] Huber P J. Robust statistics[M]. Hoboken:John Wiley & Sons, 2004.
