An intelligent charging scheme maximizing the utility for rechargeable network in smart city

Abstract The mobile charging scheme is a promising solution to extending the lifetime of the network by replenishing the energy for the sensing nodes, which has attracted more and more attention from the researchers. However, due to the limitation of energy storage both for sensing nodes and mobile chargers, not all the sensing nodes can be recharged in time by mobile chargers. Therefore, how to select appropriate sensing nodes and design the path for the mobile charger are the key to improve the system utility. This paper proposes an Intelligent Charging scheme Maximizing the Quality Utility (ICMQU) to design the charging path for the mobile charger. Comparing to the previous studies, we consider not only the utility of the data collected from the environment, but also the impact of sensing nodes with different quality. Quality Utility is proposed to optimize the charging path design. Besides, ICMQU designs the charging scheme for a single mobile charger and multiple mobile chargers simultaneously. For the charging scheme with multiple mobile chargers, the workload balance among different mobile chargers is also considered as well as the utility of the system. Extensive simulation results are provided, which demonstrates the proposed ICMQU scheme can significantly improve the utility of the system.

[1]  Anzar Mahmood,et al.  Prosumer based energy management and sharing in smart grid , 2018 .

[2]  Zhu Han,et al.  Mobile Charging in Wireless-Powered Sensor Networks: Optimal Scheduling and Experimental Implementation , 2017, IEEE Transactions on Vehicular Technology.

[3]  Yilmaz Sozer,et al.  Design and Implementation of a 75-kW Mobile Charging System for Electric Vehicles , 2016 .

[4]  Ngoc-Tu Nguyen,et al.  Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks , 2018, IEEE Systems Journal.

[5]  Aylin Yener,et al.  Cooperative energy harvesting communications with relaying and energy sharing , 2013, 2013 IEEE Information Theory Workshop (ITW).

[6]  R. Hollands.  Critical interventions into the corporate smart city , 2015 .

[7]  Jinhuan Zhang,et al.  An intelligent big data collection technology based on micro mobile data centers for crowdsensing vehicular sensor network , 2020, Personal and Ubiquitous Computing.

[8]  Maria Dolores Gil Montoya,et al.  Electric vehicles in Spain: An overview of charging systems , 2017 .

[9]  Sotiris E. Nikoletseas,et al.  Interactive Wireless Charging for Energy Balance , 2016, Wireless Power Transfer Algorithms, Technologies and Applications in Ad Hoc Communication Networks.

[10]  Fufang Li,et al.  Adaptive Contention Window MAC Protocol in a Global View for Emerging Trends Networks , 2021, IEEE Access.

[11]  Guihai Chen,et al.  Radiation Constrained Scheduling of Wireless Charging Tasks , 2018, IEEE/ACM Transactions on Networking.

[12]  Jianping Pan,et al.  An Active Mobile Charging and Data Collection Scheme for Clustered Sensor Networks , 2019, IEEE Transactions on Vehicular Technology.

[13]  Guangjie Han,et al.  A Joint Energy Replenishment and Data Collection Algorithm in Wireless Rechargeable Sensor Networks , 2018, IEEE Internet of Things Journal.

[14]  Patrick Maillé,et al.  Charging Electric Vehicles in the Smart City: A Survey of Economy-Driven Approaches , 2016, IEEE Transactions on Intelligent Transportation Systems.

[15]  Zhiwen Zeng,et al.  A verifiable trust evaluation mechanism for ultra-reliable applications in 5G and beyond networks , 2021, Comput. Stand. Interfaces.

[16]  Xi Zheng,et al.  Crowdsourcing Mechanism for Trust Evaluation in CPCS Based on Intelligent Mobile Edge Computing , 2019, ACM Trans. Intell. Syst. Technol..

[17]  Sotiris E. Nikoletseas,et al.  Wireless charging for weighted energy balance in populations of mobile peers , 2017, Ad Hoc Networks.

[18]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[19]  Guihai Chen,et al.  SCAPE: Safe Charging with Adjustable Power , 2014, 2014 IEEE 34th International Conference on Distributed Computing Systems.

[20]  Zhiwen Zeng,et al.  A trust-based minimum cost and quality aware data collection scheme in P2P network , 2020, Peer-to-Peer Netw. Appl..

[21]  Shaojie Tang,et al.  CHASE: Charging and Scheduling Scheme for Stochastic Event Capture in Wireless Rechargeable Sensor Networks , 2020, IEEE Transactions on Mobile Computing.

[22]  Abhinav Tomar,et al.  An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks , 2018, J. Netw. Comput. Appl..

[23]  Naixue Xiong,et al.  STMTO: A smart and trust multi-UAV task offloading system , 2021, Inf. Sci..

[24]  Daniel Fodorean,et al.  New Mobile Charging Station for Urban and Resort Areas , 2019, 2019 Electric Vehicles International Conference (EV).

[25]  Guihai Chen,et al.  Wireless Charger Placement for Directional Charging , 2018, IEEE/ACM Transactions on Networking.

[26]  Taban Habibu,et al.  Design of Mobile Phone Charging Power Source Using Microwave Harvesting , 2020 .

[27]  Changqin Huang,et al.  A parallel joint optimized relay selection protocol for wake-up radio enabled WSNs , 2021, Phys. Commun..

[28]  Dong-Soo Har,et al.  Distributed Sensor Nodes Charged by Mobile Charger with Directional Antenna and by Energy Trading for Balancing , 2017, Sensors.

[29]  Smail Tedjini,et al.  Europe and the future for WPT , 2017 .

[30]  Weifa Liang,et al.  Charging utility maximization in wireless rechargeable sensor networks , 2016, Wireless Networks.

[31]  Anfeng Liu,et al.  Deep reinforcement learning for computation offloading in mobile edge computing environment , 2021, Comput. Commun..

[32]  Xiaohong Huang,et al.  An optimal scheduling algorithm for hybrid EV charging scenario using consortium blockchains , 2019, Future Gener. Comput. Syst..

[33]  Hongyi Wu,et al.  Low-Cost Collaborative Mobile Charging for Large-Scale Wireless Sensor Networks , 2017, IEEE Transactions on Mobile Computing.

[34]  Sotiris E. Nikoletseas,et al.  Interactive Wireless Charging for Weighted Energy Balance , 2016, 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS).

[35]  Weifa Liang,et al.  Approximation Algorithms for Charging Reward Maximization in Rechargeable Sensor Networks via a Mobile Charger , 2017, IEEE/ACM Transactions on Networking.

[36]  Panlong Yang,et al.  Collaborated Tasks-driven Mobile Charging and Scheduling: A Near Optimal Result , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[37]  Anfeng Liu,et al.  An Intelligent Game-Based Offloading Scheme for Maximizing Benefits of IoT-Edge-Cloud Ecosystems , 2022, IEEE Internet of Things Journal.

[38]  Zhiwen Zeng,et al.  An Intelligent Collaboration Trust Interconnections System for Mobile Information Control in Ubiquitous 5G Networks , 2021, IEEE Transactions on Network Science and Engineering.

[39]  Jigang Wu,et al.  Joint Charging Tour Planning and Depot Positioning for Wireless Sensor Networks Using Mobile Chargers , 2017, IEEE/ACM Transactions on Networking.

[40]  Arun Kumar Sangaiah,et al.  Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities , 2021, IEEE Transactions on Intelligent Transportation Systems.