A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle

During mobile edge computing, due to the movement of nodes and the exhaustion of node energy, link failure occurs thus reducing the network lifetime in the mobile ad-hoc network. When the route fails, because the single-path protocols need to restart the route discovery process, the delay of the network is greatly increased. Therefore, the multi-path routing protocol is proposed, saving the cost of route discovery. In this paper, we propose an ad hoc on-demand multi-path distance vector (AOMDV) routing protocol based on link lifetime and energy consumption prediction (named LLECP-AOMDV) for mobile edge computing. In the route discovery phase, the energy grading strategy is adopted. When the node energy is lower than the threshold, it no longer participates in the route discovery. In the routing selected phase, the path is selected based on the lifetime of the route link and the minimum energy consumption of the route. According to energy consumption, packet delivery rate, end-to-end delay performance indicators, we evaluate the comparison results. The result shows that under most network performance indicators and parameters, the proposed LLECP-AOMDV is superior to the other three protocols, which improves the network lifetime, reduces the node's energy consumption and the average end-to-end delay. The protocol is very useful for mobile edge computing.

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A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing

Due to the limited computing resources and energy of IoT devices, complex computing tasks are offloaded to sufficient computing servers, such as Cloud Center. However, offloading may increase the latency and congestion of the IoT network. Mobile Edge Computing (MEC) is a promising approach, which can decrease the delay and energy consumption of IoT devices significantly. In this paper, we investigate the problem of multi-user computation offloading under dynamic environment. Considering the channel interference when multiple IoT devices offload computing tasks via wireless channels at the same time, we formulate the computation offloading as an evolutionary game model. We use the replicator dynamics to analyze the evolutionary process of IoT devices and prove that multi-user computation offloading exists unique Evolutionary Stability Strategy (ESS). Finally, we design an evolutionary game algorithm based on reinforcement learning in practical application scenarios. Experiments can verify the convergence and performance of the proposed algorithm in multi-user scenarios.

Novel method of mobile edge computation offloading based on evolutionary game strategy for IoT devices

Adaptive repair algorithm for TORA routing protocol based on flood control strategy

In the application environment having dense distribution of marginal wireless sensor network (WSN), the data transmission process will generate a large number of conflicts, which will result in loss of transmission data and increase of transmission delay. The multi-path data transmission method can effectively solve the problem of large data loss and transmission delay caused by collisions. A new approach of multi-path reliable transmission for application of marginal WSN (named RCB-MRT) is proposed in this paper. It adopts redundancy mechanism to realize the reliability of data transmission, and uses concurrent woven multi-path technology to improve the transmission efficiency of data packets. Firstly, it divides the data packets that the sensor node needs to transmit into several sub-packets with data redundancy, and then forwards the sub-packets to the aggregation node through multi-path by the intermediate nodes of marginal environment. The results of our experimental tests show that the proposed multi-path reliable transmission method can effectively reduce data packet loss rate, reduce transmission delay and increase network lifetime. The method is very useful for the applications of marginal wireless sensor network.

New approach of multi-path reliable transmission for marginal wireless sensor network

In resource-constrained mobile ad hoc network, geographical routing algorithms are relatively attractive routing algorithms. Due to the movement of nodes, energy exhaustion and the presence of obstacles, routing holes may occur. The existing routing algorithm only considers bypassing routing void through right-hand or left-hand criteria, and forwards data at the boundary of the hole. However, such a routing algorithm may lead to the expansion of the routing hole and even cause the paralysis of the network. This paper proposes a new greedy forwarding improvement routing method for mobile ad hoc network. In the greedy forwarding phase, the reliable communication area is calculated, and then the quality of the link is evaluated according to the relative displacement between the nodes and the maintenance time of the link. Then, according to the quality of the link, the distance between the candidate node and the destination node, and the number of the neighbor nodes, the metric value is obtained, and the node with the large metric value is selected as the next hop. When a routing hole occurs, the waiting forwarding mode is used for a period of time. After a period of time, when the current node is still a routing hole node, we can use the right-hand rule and the left-hand criteria, and the bypass mode is performed simultaneously. By considering three factors of the deflection angle and the maintenance time of the link and the remaining energy of the node, the forwarding node as the next hop with the highest priority value is selected in each direction. The experimental results show that compared with the existing GPSR,EMGR and EDGR, the proposed algorithm reduces the energy consumption of the network, improves the delivery rate of data packets, reduces the network delay and prolongs the network lifetime.

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A New Method of Mobile Ad Hoc Network Routing Based on Greed Forwarding Improvement Strategy

Energy Efficient Hierarchical Clustering (EEHC) is a distributed random clustering algorithm for RWSNs with the goal of maximizing network lifetime. The novel energy-efficient hierarchical clustering approach based on neighbor rotation for edge computing of the Internet of Things (IOT) proposed in this paper determines whether a node can become a cluster head node by comparing the comprehensive weighting of the degree of the sensor node and the distance between the sensor node and the center of the subarea. When the cluster head is replaced, a member node in the set of neighbor nodes of the current cluster head becomes the new cluster head for a random time slice, and the merged information is transmitted to the nearest sink node in a multi-hop manner according to the routing information. Simulation results demonstrate that the proposed method is more effective in saving network energy, improving data transmission efficiency, and maximizing network lifetime.

Hao Wu

Papers

A New Method of Mobile Ad Hoc Network Routing Based on Greed Forwarding Improvement Strategy

Adaptive repair algorithm for TORA routing protocol based on flood control strategy

New approach of multi-path reliable transmission for marginal wireless sensor network

New Energy-Efficient Hierarchical Clustering Approach Based on Neighbor Rotation for Edge Computing of IoT