The availability of conventional mobile edge computing (MEC) for vehicles is often hindered by signal interference and attenuation, limiting its efficiency in supporting computationally intensive and latency-sensitive applications. To address these challenges, we propose a novel blockchainenabled vehicular mobile edge computing (VMEC) system that enhances resource sharing and energy efficiency in electric vehicle (EV)-centric services. The system employs an improved RAFT-based consensus mechanism (mRAFT), which dynamically evaluates the reputation of access point (AP) nodes based on their available resources, ensuring fair leader election and enhancing consensus reliability and efficiency. Furthermore, a probabilistic model is introduced to describe AP behaviors, improving the security of the consensus process. To minimize overall energy consumption, we develop a decentralized optimization framework using the Alternating Direction Method of Multipliers (ADMM). This framework jointly optimizes AP clustering, computation resource allocation, and bandwidth scheduling to achieve energy-efficient task offloading and consensus. Simulation results demonstrate that the proposed VMEC system reduces latency by 29.53 and energy consumption by 43.43 schemes, showcasing its effectiveness in delivering low-latency, energy-efficient services for advanced vehicular applications.

The increasing popularity of electric passenger vehicles (EPVs) has significant implications for transportation networks and power grids. We aim to tackle the routing and charging dispatching problem for EPVs while considering charging station (CS) power limits. We formulate the problem using a clustered rolling framework and introduce an energy criterion to determine EPV availability for shuttle services. The EPV charging dispatching is modeled as a constraint programming problem under CS power limits, with a fixed charging rate assumed at the start of charging. The RCLBD algorithm, based on logic-based benders decomposition, effectively handles binary and continuous variables. The EPV routing model serves as the master problem, while the charging model acts as the sub-problem. Simulation experiments demonstrate the RCLBD algorithm’s performance and efficiency. The algorithm successfully provides efficient pickup and delivery services, minimizing waiting times for customers. It ensures successful routing and charging solutions for all arriving EPVs. The electricity cost of our proposed RCLBD algorithm is 2.13%; 10.68% lower than that of MIP and MIPC method when the number of EPVs is 750. Our proposed routing and charging algorithm shows good performance and efficiency, addressing the challenges posed by the increasing popularity of EPVs.

As the electric vehicles (EVs) become prevalent, the demand for smart charging rises. The disordered charging problem of EVs, the high cost, and the location problem of charging stations bring a great challenge to the power grids and transport networks. The Internet of Things (IoT) technology enables the IoT-based EV (IoEV) to plan the route and process the information with smart wireless charging. However, how to schedule the optimal routing and wireless charging is challenging. In this article, we consider a joint routing and wireless charging scheduling problem with a microwave power transfer system to minimize the travel distance, the charging cost, and battery degradation cost when IoEVs provide shuttle services. To solve this mixed linear programming problem for the joint routing and charging schedule of IoEVs with the integer routing variables and continuous charging variables, we propose a routing and charging customized benders decomposition (RCBD) algorithm. To increase the time efficiency of the RCBD algorithm, we propose an improved RCBD (IRCBD) algorithm with the trajectory similarity measurement method. Extensive simulation results show the effectiveness and correctness of the proposed scheduling algorithms. We compare the IRCBD algorithm with the actor–critic algorithm and the RCBD algorithm. The charging cost of the IRCBD algorithm with the threshold 0.9 of trajectory similarity is 5.56% more than that of the RCBD algorithm. The running time of the IRCBD algorithm is 50.01% less than that of the RCBD algorithm when there are 300 pickups and deliveries. The running time of the IRCBD algorithm is less than that of the RCBD algorithm and the actor–critic algorithm.

With the advances in the Internet-of-Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This article investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor–critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor–critic learning charging (CALC) algorithm by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs’ expected cost by 24.03%, 21.49%, 13.80%, compared with the eagerly charging algorithm, online charging algorithm, reinforcement learning (RL)-based adaptive energy management algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.

With the increasing use of electric vehicles (EVs) and the development of emerging transportation network services, autonomous EVs (AEVs) may play an important role in the future of transportation. AEVs can automatically plan their route, park in the charging station, and support the vehicle-to-grid (V2G) services. However, V2G services may influence user dissatisfaction due to the task delays. There is a tradeoff between the optimization of electricity cost and user dissatisfaction. In this article, we formulate the problem to minimize the electricity cost of AEVs and the degradation cost of the charging station batteries with the constraint of V2G services and user dissatisfaction, which is a nonconvex problem and is difficult to solve. To solve the nonconvex optimization problem, we design a suboptimal charging algorithm with some constraints (SCAC) based on the Lyapunov optimization technique to find a tradeoff between the total cost and user dissatisfaction. This algorithm cannot find the optimal solution but can give a selection criterion. Furthermore, in order to get a global charging schedule, we use the criterion from the SCAC algorithm as a priori knowledge to design the charging scheduling reinforcement-learning-based (CSRL) algorithm, which is more efficient than the reinforcement learning (RL) method without any particular criterion. We do simulations by using day-ahead price and practical profiles of AEVs to evaluate the proposed algorithms. The numerical results show that the CSRL algorithm has a better performance 5.12% than the SCAC algorithm and both algorithms are 12.66% and 17.14% better than the benchmark algorithm which is the shortest path (SP)-based algorithm. The CSRL algorithm has more efficiency ε(1 - Pr(Λ(t) = 0)) than the SCAC algorithm, where Pr(Λ(t) = 0) is a selection criterion calculated from the SCAC algorithm.

With the development of renewable energy technology and communication technology in recent years, many residents utilize renewable energy devices in their residences with energy storage systems. However, it is a great challenge to share residents' energy with others in the smart community for minimizing the total cost of all residents. In this paper, we investigate the problem of energy management and task scheduling for a smart community with residential combined heat and power system (resCHP) and renewable energy to pay the least bill. We take heterogeneous task arrival into consideration, which widely exists in the community. We formulate the minimum cost problem of a non-cooperative community as a random non-convex optimization problem with physical constraints. Our objective is to minimize the community time-average cost, including the cost of the external grid and natural gas. We adopt the Lyapunov optimization theory to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on Sarsa Algorithm. Finally, we present extensive simulations to validate the proposed algorithms by using real trace data.

We present the performance comparison of 40, 80 and 112 Gb/s polarization division multiplexing (PDM)-differential quadrature phase shift keying (DQPSK) optical label switching system with frequency swept coherent detected spectral amplitude code (SAC) labels in simulation. Direct detection is selected to demodulate the PDM payload in the receiver. The SAC label is frequency-swept coherently detected. The label and payload signal performances are assessed by the eye diagram opening factor and bit error rate (BER) as function of received optical power (ROP) and optical signal to noise ratio (OSNR). For back-to-back (BTB) system and 156, 138 and 120 km transmission for 40, 80 and 112 Gb/s respectively, label eye opening factors are 0.93 and 0.85 for 40 Gb/s, 0.94 and 0.86 for 80 Gb/s and 0.93 and 0.90 for 112Gb/s respectively, while payload optical signal-to-noise ratio are 29.9, 25.6, 23.4 dB and the payload received optical power are -11.5, -12.6 and -13.3 dBm for a bit error rate of 10-9after 156, 138 and 120 km for 40, 80 and 112 Gb/s respectively. The payload could well be demodulated after 1,800, 1,260 and 900 km transmission for 40, 80 and 112 Gb/s respectively at a BER of 10-3using forward error correction (FEC).