An adaptive routing strategy in P2P-based Edge Cloud

Abstract

P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered to adapt to the dynamic requirements of user request services and network topology. In order to address the challenges produced by these factors, an adaptive routing in P2P-based Edge Cloud is proposed, which is named ARPEC. In our approach, a target edge node selection scheme based on message activity and network topology is proposed, aiming to minimize the load on edge node and user access latency. Furthermore, to minimize system overhead, sensor data routing is mapped to minimum cost maximum flow (MCMF) graph. On this basis, a target edge node selection algorithm based on a grey linear regression combination prediction model is designed, and an incremental MCMF algorithm based on belief propagation (BP) is proposed. The evaluation results show that our approach can effectively improve PEC transmission performance and user experience.