Energy-Efficient Smart Routing Based on Link Correlation Mining for Wireless Edge Computing in IoT

Modern Internet-of-Things (IoT) applications are heavily data driven and often require reliable data streams to achieve high-quality data mining. The concept of edge computing is introduced to reduce data latency and communication bandwidth between the cloud server and IoT edge devices. However, inefficient routing that may cause transmission failure or unnecessary data (re)transmission is still a key obstacle to obtain good and reliable data mining results. In this article, network coding combined with opportunistic routing is used to improve energy efficiency in wireless IoT infrastructure, considering the existence of link correlation. Studies have shown that packet receptions on wireless links are correlated, which is completely contrary to the assumption of link independence used in existing routing mechanisms. This assumption causes estimation errors in the calculation of expected number of transmissions for forwarders, which further affects the selection of forwarder set, and ultimately affects the performance of the protocol. We propose an intrasession network coding mechanism based on the mining of link correlation. A novel smart routing method is proposed to accurately estimate the number of transmissions required by forwarders, together with an algorithm for selecting a forwarder set with more optimal number of transmissions. Simulation results demonstrate that the proposed mechanism can achieve fewer transmissions and offer more energy-efficient communications for wireless edge IoT applications.

Automated summary

This article introduces an intelligent edge computing method based on link correlation, which improves the energy efficiency of wireless IoT infrastructure through network coding and opportunistic routing. The method reduces unnecessary data transmission and achieves more energy-efficient communications.