Abstract
A charge scheduling strategy is a robust approach to schedule the charging strategies in electric vehicles (EVs) from a broad perspective with the aim of evading the overloading of charging stations and enhancing energy efficiency. However, devising an effective charging scheduling schemefor attaining optimal energy consumption still prevails as a complicated problem, particularly while considering the synchronized behavior of both charging stations as well as EVs. Here, a robust QoS-based charge scheduling approach was developed, which exploits the vehicular Adhoc networks (VANETs) with the improved functionalities for enabling communication between the vehicle-traffic server, road-side units (RSUs), and various EVs on roads. An optimal routing is performed by the Fractional-social sky driver (Fractional SSD), which is devised by the incorporation of the Fractional calculus (FC) and social sky driver (SSD) optimization. Here, the multi-objectives, namely, distance, battery power, and predicted traffic density are considered where the traffic density is effectively predicted using deep recurrent neural network (Deep RNN). Then, the charge scheduling process is executed by the utilization of the developed optimization technique called Fractional-social water cycle algorithm (Fractional SWCA)-based scheduling algorithm by taking into account the QoS-based fitness objective, likepriority, response time, and latency. Moreover, the proposed Fractional SWCA is developed by the integration of fractional SSD and water cycle algorithm (WCA). The performance of the devisedscheme is evaluated withmeasures, like metrics, delay, traffic density, fitness, total trip time, percentage of successful allocation, and power with the values of 8.429 min, 4.8 per lane, 24.571, 49.421 min, 94.494%, and 14,135.72 J.
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References
Ammous M, Belakaria S, Sorour S, Abdel-Rahim A (2018) Optimal cloud-based routing with in-route charging of mobility-on-dem & electric vehicles. IEEE Trans Intell Transp Syst 20(7):2510–2522
Bhaladhare PR, Jinwala DC (2014) A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Adv Comput Eng. https://doi.org/10.1155/2014/396529
Bharathi M, Geetha K, Mani PK, Vijayakumar GN, Srinivasan K, & Kumar KR (2022) AI and IoT-based electric vehicle monitoring system. In: the proceeding of sixth international conference on I-SMAC (IoT in Social, Mobile, Analytics & Cloud) (I-SMAC), IEEE, Dharan, Nepal
Cao Y, Zhang X, Wang R, Peng L, Aslam N, & Chen X (2017) Applying DTN routing for reservation-driven EV charging management in smart cities. In: IEEE 13th international wireless communications & mobile computing conference (IWCMC), 1471–1476
Céspedes S, Taha S, Shen X (2023) A multihop-authenticated proxy mobile IP scheme for asymmetric VANETs. IEEE Trans Veh Technol 62(7):3271–3286
Cheng H, Shojafar M, Alazab M, Tafazolli R, Liu Y (2022) PPVF: privacy-preserving protocol for vehicle feedback in cloud-assisted VANET. IEEE Trans Intell Transp Syst 23(7):9391–9403
Cheng W, Cheng X, Song M, Chen B, Zhao WW (2011) On the design & deployment of RFID assisted navigation systems for VANETs. IEEE Trans Parallel Distrib Syst 23(7):1267–1274
Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm: a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166
Inoue M, Inoue S, Nishida T (2018) Deep recurrent neural network for mobile human activity recognition with high throughput. Artif Life & Robot 23(2):173–185
Kancharla SR, Ramadurai G (2018) An adaptive large neighborhood search approach for electric vehicle routing with load-dependent energy consumption. Transp Dev Econ 4(2):10
Kisacikoglu MC, Erden F, Erdogan N (2017) Distributed control of PEV charging based on energy dem & forecast. IEEE Trans Industr Inf 14(1):332–341
Kumar AG, Anmol M, Akhil VS (2015) A strategy to enhance electric vehicle penetration level in India. Procedia Technol 21:552–559
Leontiadis I, Marfia G, Mack D, Pau G, Mascolo C, Gerla M (2011) On the effectiveness of an opportunistic traffic management system for vehicular networks. IEEE Trans Intell Transp Syst 12(4):1537–1548
Liang H, Zhuang W (2012a) Double-loop receiver-initiated MAC for cooperative data dissemination via roadside WLANs. IEEE Trans Commun 60(9):2644–2656
Liang H, Zhuang W (2012b) Efficient on-dem & data service delivery to high-speed trains in cellular/info station integrated networks. IEEE J Sel Areas Commun 30(4):780–791
Liu A, Li C, Xia B, Yue W, & Miao Z (2018) G-MACO: a multi-objective route planning algorithm on green wave effect for electric vehicles. In: 2018 IEEE 87th vehicular technology conference (VTC Spring), 1–5.
Liu R, Dow L, Liu E (2017) A survey of PEV impacts on electric utilities. ISGT 2011. https://doi.org/10.1109/ISGT.2011.5759171
Luan TH, Shen XS, & Bai F (2013) Integrity-oriented content transmission in highway vehicular ad hoc networks. In: 2013 proceedings IEEE INFOCOM, 2562–2570
Malhotra A, Binetti G, Davoudi A, Schizas ID (2016) Distributed power profile tracking for heterogeneous charging of electric vehicles. IEEE Trans Smart Grid 8(5):2090–2099
Wagh MB, Gomathi N (2019) Improved GWO-CS Algorithm-Based Optimal Routing Strategy in VANET. J Netw Commun Syst 2(1):34–42
Nimalsiri N, Smith D, Ratnam E, Mediwaththe C, & Halgamuge S (2020) A decentralized electric vehicle charge scheduling scheme for tracking power profiles. In: 2020 IEEE power & energy society innovative smart grid technologies conference (ISGT), 1–5
Pourazarm S, Cassandra CG, & Malikopoulos A (2014) Optimal routing of electric vehicles in networks with charging nodes: a dynamic programming approach. In: 2014 IEEE international electric vehicle conference (IEVC), 1–7
Qian LP, Zhou X, Yu N, & Wu Y (2020) Electric vehicles charging scheduling optimization for total elapsed time minimization. In: 2020 IEEE 91st vehicular technology conference (VTC2020-Spring), 1–5
Qianwen Z (2021) Integrating renewable energy sources in electric vehicles via optimization assisted model. J Comput Mech Power Syst Control 4(1):35–41
Rewadkar D, Doye D (2018) Traffic-aware routing protocol in VANET using adaptive autoregressive crow search algorithm. J Net Commun Syst 1(1):36–42
Sampigethaya K, Li M, Huang L, Poovendran R (2007) AMOEBA: robust location privacy scheme for VANET. IEEE J Sel Areas Commun 25(8):1569–1589
Smiai O, Bellotti F, Berta R, De Gloria A (2017) Exploring particle swarm optimization to build a dynamic charging electric vehicle routing algorithm. International conference on applications in electronics pervading industry. Environment & Society, Springer, pp 127–134
Tan H, Kim P, Chung I (2020) Practical homomorphic authentication in cloud-assisted VANETs with blockchain-based healthcare monitoring for pandemic control. Electronics 9(10):1683
Tharwat A, Gabel T (2019) Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput Appl 32:1–14
Vanitha V, Resmi R, Reddy KN (2020) Machine learning-based charge scheduling of electric vehicles with minimum waiting time. Comput Intell 37(3):1047–1055
Wang M, Liang H, Deng R, Zhang R, & Shen XS (2013) VANET based online charging strategy for electric vehicles. In: 2013 IEEE global communications conference (GLOBECOM), 4804–4809.
Wang Z, Xu A, Zhang Y, Wang Q, Xu X, Jiang Y, Wen H (2020) Research on electric vehicle charging scheduling strategy based on graph model. In J Phys: Conf Ser 1673(1):012063
Yang H, Yang S, Xu Y, Cao E, Lai M, Dong Z (2015) Electric vehicle route optimization considering time-of-use electricity price by learnable partheno-genetic algorithm. IEEE Trans Smart Grid 6(2):657–666
Yudovina E, Michailidis G (2014) Socially optimal charging strategies for electric vehicles. IEEE Trans Autom Control 60(3):837–842
Zhang Y, You P, Cai L (2018) Optimal charging scheduling by pricing for EV charging station with dual charging modes. IEEE Trans Intell Trans Syst 20(9):3386–3396
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Hiremath, S.C., Mallapur, J.D. QoS based scheduling mechanism for electrical vehicles in cloud-assisted VANET using deep RNN. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02277-z
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DOI: https://doi.org/10.1007/s13198-024-02277-z