Journal ArticleDOI
A new algorithm of clustering AODV based on edge computing strategy in IOV
TLDR
In this paper, a new algorithm of clustering AODV based on edge computing strategy is proposed, considering the vehicle node energy and speed, the routing protocol based on the minimum hop number is optimized, which divided the communication mode into vehicle to vehicle and vehicle to road (V2R) mode.Abstract:
In the vehicular ad hoc network (VANET), due to the particularity of high-speed movement of vehicle nodes, there are higher challenges in link stability and network topology control overhead. In this paper, a new algorithm of clustering AODV based on edge computing strategy is proposed. Considering the vehicle node energy and speed, the AODV routing protocol based on the minimum hop number is optimized, which divided the communication mode into vehicle to vehicle (V2V) and vehicle to road (V2R) mode. Adding edge server in the road side unit (RSU) and using the idea of clustering, that is, the nodes in the cluster use V2V communication mode, and the nodes between clusters use V2V and V2R combined communication mode to select routes. The algorithm improves the routing efficiency in the high-speed mobile. Experiments show that the algorithm is feasible, reducing the network topology control overhead, lowering the end-to-end delay and improving the packet delivery rate comparing with others in different environment.read more
Citations
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Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning
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A novel offloading scheduling method for mobile application in mobile edge computing
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A Novel Edge Computing Architecture Based on Adaptive Stratified Sampling
TL;DR: In this article , a self-adjusting stratified sampling algorithm is proposed for real-time data stream processing of the Internet of Things (IoT), which adjusts the size of the sample stratums according to the variance of each stratum while maintaining the given memory budget.
Journal ArticleDOI
A new Edge Computing architecture based on adaptive stratified sampling: ApproxECIoT
TL;DR: In this paper, a self-adjusting stratified sampling algorithm is proposed for real-time data stream processing of the Internet of Things, which adjusts the size of the sample stratums according to the variance of each stratum while maintaining the given memory budget.
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An Approach of Flow Compensation Incentive based on Q-Learning Strategy for IoT User Privacy Protection
TL;DR: In this paper , an incentive approach of flow offset based on Q-learning algorithm for perception user privacy protection is proposed to reduce network overhead, protect IoT user privacy and increase the participation enthusiasm of perception task.
References
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An Energy-Balanced Routing Method Based on Forward-Aware Factor for Wireless Sensor Networks
TL;DR: Experimental results show that FAF-EBRM outperforms LEACH and EEUC, which balances the energy consumption, prolongs the function lifetime and guarantees high QoS of WSN.
Journal ArticleDOI
Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks
TL;DR: An algorithm is devised that enables the placement of the cloudlets at user dense regions of the WMAN, and assigns mobile users to the placed cloudlets while balancing their workload, which indicates that the performance of the proposed algorithm is very promising.
Journal ArticleDOI
Cooperative data scheduling in hybrid vehicular ad hoc networks: VANET as a software defined network
TL;DR: The proposed model and solution represent the first known vehicular ad hoc network (VANET) implementation of software defined network (SDN) concept and prove that CDS is NP-hard by constructing a polynomial-time reduction from the Maximum Weighted Independent Set (MWIS) problem.