Optimization of Mobile Edge Computing Offloading Model for Distributed Wireless Sensor Devices

Abstract

The development and popularization of mobile Internet and wireless communication technology have spawned a large number of computation-intensive and delay-intensive applications. Limited computing resources and existing technologies cannot meet the performance requirements of new applications. Mobile edge computing technology can use wireless communication technology to offload data to be stored and computing tasks to the nearby assistant or edge server with idle resources. Based on the data offloading of distributed wireless sensor device to device communication, the architecture is designed and the basic framework of distributed mobile edge computing is constructed. To solve the problem of high mobile cloud computing technology, the offloading model of optimized mobile edge computing was proposed, and the stability and convergence of the proposed algorithm were proved. Finally, the system performance of the proposed algorithm is verified by simulation. The results show that the proposed algorithm can converge within a finite number of steps. Compared with other benchmark schemes, the proposed algorithm has better performance in reducing system energy consumption, reducing moving edge response delay and system total delay.

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

“…Flock‐CC is inspired by swarm intelligence and uses bird flocks' collective behavior to effectively manage congestion in WSNs. For multimedia WSNs, Han et al 12 suggested a priority rate‐based routing protocol (PRRP). For congestion control, PRRP employs a hop‐by‐hop technique, prioritizing packets depending on their rate requirements.…”

An Efficient Algorithm for Resource Allocation in Mobile Edge Computing Based on Convex Optimization and Karush–Kuhn–Tucker Method

“…P4 is a 0-1 discrete variable convex optimization problem after relaxation, and its algorithm complexity is O(n 3 ). In addition, the complexity of the number of alternate iterations is O(n) [79]. Terefore, the overall complexity of the proposed algorithm is the product of interior point method and that of the number of alternate iterations, i.e., O(n 4 ).…”