An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud

An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud

Blog Article

The high latency and high energy consumption of wireless body areas networks (WBANs) for computing-intensive tasks is intolerable, especially for remote interventional surgery.In this paper, a multi-mobile edge server collaborative computation offloading scheme is proposed, which enables tasks to choose a server and offload a certain proportion of computation to efficiently handle computing-intensive services for massive users.More specifically, we formulate the problem of minimizing system latency and energy consumption, and Car Seat then model the task offloading and resource allocation process as a Markov decision process (MDP).

We have developed a scheme called m4m-PDQN to optimize offloading decisions, aiming to minimize the weighted sum of latency and energy consumption.Compared to existing single-server offloading schemes, it is more effective in utilizing computing resources and reducing waiting time and energy consumption for computing tasks in the multiple-server collaborative computing scenarios.The experimental results show Food Service:Tabletop Concession Machines:Concession Displays that it outperforms other algorithms in terms of performance and efficiency, significantly improving the quality of service (QoS) for wearable wireless body area networks for medical applications.