As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources' allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms. INDEX TERMS Edge computing, multiple business, power Internet of Things, service delay, workload allocation.
As a mode of processing task request, edge computing paradigm can reduce task delay and effectively alleviate network congestion caused by the proliferation of Internet of things(IoT) devices compared with cloud computing. However, in the actual construction of the network, there are various edge autonomous subnets in the adjacent areas, which leads to the possibility of unbalance of server load among autonomous subnets during the peak period of task request. In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing. The problem is solved based on the real-time state of the network and the attributes of the task, which adopts Actor Critic and Policy Gradient's Deep Deterministic Policy Gradient(DDPG) to make optimized decisions of computation offloading. Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize the task delay.The simulation results show that the proposed DDPG-based algorithm is competitive compared with the Deep Q Network(DQN) algorithm and Asynchronous Advantage Actor-Critic(A3C) algorithm. Moreover, the optimal solutions are leveraged to analyze the influence of edge network parameters on task delay. INDEX TERMS Edge computing, computation offload, collaborative computing, reinforcement learning, DDPG.
This paper presents a novel approach for providing a mobile battery swapping service for electric vehicles (EVs) that is provided by a mobile battery swapping van. This battery swapping van can carry many fully charged batteries and drive up to an EV to swap a battery within a few minutes. First, a reasonable EV battery swapping architecture based on a battery swapping van is established in this paper. The function and role of each participant and the relationships between each participant are determined, especially their changes compared with the battery charging service. Second, the battery swapping service is described, including the service request priority and service request queuing model. To provide the battery swapping service efficiently and effectively, the battery swapping service request scheduling is analyzed well, and a minimum waiting time based on priority and satisfaction scheduling strategy (MWT-PS) is proposed. Finally, the battery swapping service is simulated, and the performance of MWT-PS is evaluated in simulation scenarios. The simulation results show that this novel approach can be used as a reference for a future system that provides reasonable and satisfying battery swapping service for EVs.
With the development of wireless local area networks and intelligent transportation technologies, the Internet of Vehicles is considered to be an effective method to alleviate the severe situation of the current transportation system. The vehicles in the Internet of Vehicles system build the Vehicular Ad Hoc Networks through wireless communication technology and dynamically provide different services through the real-time driving information broadcast by the vehicles. Vehicle drivers can control the distance, planning the driving route, between vehicles according to the current traffic environment, which improves the overall safety and efficiency of the traffic system. Due to the particularity of the Internet of Vehicles system service, vehicles need to broadcast their location information frequently. Attackers can collect and analyze vehicle broadcast information to steal privacy and even directionally track the owner through the driving trajectory, bringing serious security risks. This paper proposes a blockchain-based privacy protection system for the Internet of Vehicles. The system combines the blockchain with the Internet of Vehicles system to design a safe and efficient two-way authentication and key agreement algorithm through encryption and signature algorithm, which also solves the central dependency problem of the traditional Internet of Vehicles system.
As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT). However, the uneven spatial and temporal distribution of business requests in edge network leads to a significant difference in business busyness between edge nodes. Due to the natural lightweight and portability, container migration has become a critical technology for load balancing, thereby optimizing the resource utilization of edge nodes. To this end, this paper proposes a container migration-based decision-making (CMDM) mechanism in power IoT. First, a load differentiation matrix model between edge nodes is constructed to determine the timing of container migration. Then, a container migration model of load balancing joint migration cost (LBJC) is established to minimize the impact of container migration while balancing the load of edge network. Finally, the migration priority of containers is determined from the perspective of resource correlation and business relevance, and by introducing a pseudo-random ratio rule and combining the local pheromone evaporation with global pheromone update at the same time, a migration algorithm based on modified Ant Colony System (MACS) is designed to utilize the LBJC model and thus guiding the choice of possible migration actions. The simulation results show that compared with genetic algorithm (GA) and Space Aware Best Fit Decreasing (SABFD) algorithm, the comprehensive performance of CMDM in load balancing joint migration cost is improved by 7.3% and 12.5% respectively. INDEX TERMS container migration, load balancing, migration cost, Edge computing, power Internet of things.
Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.
Due to the advantages of muti-hop communication, self-organizing, self-healing and reliability, wireless muti-hop mesh network becomes an ideal solution for smart grid meter data collection. However, wireless muti-hop meter data collection network faces limitation and challenge on communication performance of network caused by application layer data traffic and the network topology limitation. When a large number of data occur in emergence, some mesh nodes (smart meters) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion. This paper utilizes a three-layer network to form the wireless muti-hop meter data collection communication network, next proposes a new random switching traffic scheduling algorithm based on meter data collection tree with the idea of load balancing, and then gives an optimization for random switching traffic scheduling. Simulation show that the new traffic scheduling mechanism can create a balanced meter data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.
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