Distributed Routing and Charging Scheduling Optimization for Internet of Electric Vehicles

Tang, Wanrong; Bi, Suzhi; Zhang, Ying Jun

doi:10.1109/jiot.2018.2876004

Cited by 79 publications

(32 citation statements)

References 28 publications

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“…The authors in [20] proposed a predictive algorithm for the low-complexity scheduling of EVs charging in which non-causal information about the future arrivals of EVs is not available but only the estimated statistical information. A distributed algorithm to jointly optimize the routing selection and charging schedule of IoV-based EV networks is proposed in [21]. This joint optimization problem is formulated as mixed-integer nonlinear programming, and this NP-had problem is tackled by using an approximate distributed algorithm, which enables to calculate a routing and charging solution in a distributed way by the EV users and a system operator.…”

Section: B Scheduling Optimization Of Evs Chargingmentioning

confidence: 99%

IoV-Based Deployment and Scheduling of Charging Infrastructure in Intelligent Transportation Systems

et al. 2021

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Internet of vehicles (IoV) is an emerging paradigm to exchange and analyze information collected from sensors using wireless technologies between vehicles and people, vehicles and infrastructure, and vehicles-to-vehicles. With the recent increase in the number of electric vehicles (EVs), the seamless integration of IoV in EVs and charging infrastructure can offer environmentally sustainable and budget-friendly transportation. In this paper, we propose an IoV-based framework that consists of deployment and scheduling of a mobile charging infrastructure. For the deployment, we formulate an optimization problem to minimize the total cost of mobile charging infrastructure placement while considering constraints on the number of EVs that can be charged simultaneously. The formulated problem is mixedinteger programming and solved by using the branch and bound algorithm. We then propose an IoV-based scheduling scheme for EVs charging to minimize travel distance and charging costs while satisfying the constraints of charging time requirement of EVs and resources of a charging station. We consider passive road sensors and traffic sensors in the proposed IoV-based scheduling scheme to enable EV users for finding a charging station that can fulfill their requirements, as well as to enable service providers to know about the demand in the area. Simulation results illustrate the significant impact of the optimal deployment of charging infrastructure and scheduling optimization on the efficiency of EVs charging.

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Section: B Scheduling Optimization Of Evs Chargingmentioning

confidence: 99%

IoV-Based Deployment and Scheduling of Charging Infrastructure in Intelligent Transportation Systems

et al. 2021

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“…Based on the existing LTE network architecture, a layered MEC network architecture needs to be considered in order to be closer to the actual situation. Taking advantage of the short distance between edge servers and vehicles, a reasonable task offloading decision is made to improve the efficiency of system's task execution according to the amount of data uploaded by computing tasks and computing resources required to perform the task [29,30]. In this system, vehicles can choose to access either micro base stations or macro base stations.…”

Section: Related Workmentioning

confidence: 99%

A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

2020

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This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi area multi-user multi MEC server system is designed, in which a MEC server is deployed in each area, and multiple vehicle user equipment in an area can offload computing tasks to MEC servers in different areas by a wireless channel. Then, considering the mobility of users in Internet of vehicles, a vehicle distance prediction based on Kalman filter is proposed to improve the accuracy of vehicle-to-vehicle distance. However, when the vehicle performs the task, it needs to submit the real location, which causes the problem of the location privacy disclosure of vehicle users. Finally, the total cost of communication delay, location privacy of vehicles and energy consumption of all users is formulated as the optimization goal, which take into account the system state, action strategy, reward and punishment function and other factors. Moreover, Double DQN algorithm is used to solve the optimal scheduling strategy for minimizing the total consumption cost of system. Simulation results show that proposed algorithm has the highest computing task completion rate and converges to about 80% after 8000 iterations, and its performance is more ideal compared with other algorithms in terms of system energy cost and task completion rate, which demonstrates the effectiveness of our proposed scheduling strategy.

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“…Reed et al [31] applied the ant colony system to solve CVRP associated with the waste collection. Also, location-routing issues [32], inventory routing problem [33], [34], urban bicycles renting issue [35], blood supply in emergency situations [36], unmanned air vehicle routing [37], data collection in Internet of Things [38], routing and charging scheduling for electric vehicles [39] were also formalized as CVRP. These applications indicate that CVRP is very practical in Internet of Thing, so the research on CVRP is significant, especially on effective algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things

et al. 2019

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The Internet of Things integrates lots of capacitated vehicles in smart logistics. The routing for capacitated vehicles is a combinatorial optimization problem which has been widely studied in recent years. This paper proposes an effective order-aware hybrid genetic algorithm for the capacitated vehicle routing problem in the Internet of Things. The method is characterized by an improved initialization strategy and a problem-specific crossover operator. The former combines the sweep algorithm with randomness to harmonize the contradiction between diversity and convergence, while the latter integrates neighborhood search heuristics to find the offspring with the best fitness and check constraints simultaneously. A large number of simulations have been carried out, and the results validated the effectiveness of our algorithm. INDEX TERMS Internet of Things, capacitated vehicle routing problem, order-aware hybrid genetic algorithm, sweep algorithm, neighborhood search heuristics.

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Distributed Routing and Charging Scheduling Optimization for Internet of Electric Vehicles

Cited by 79 publications

References 28 publications

IoV-Based Deployment and Scheduling of Charging Infrastructure in Intelligent Transportation Systems

IoV-Based Deployment and Scheduling of Charging Infrastructure in Intelligent Transportation Systems

A collaborative scheduling strategy for IoV computing resources considering location privacy protection in mobile edge computing environment

An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things

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