Distributed collaboration and anti-interference optimization in edge computing for IoT

Abstract

The edge computing (EC) systems for Internet of Things (IoT) can bring out low latency, high reliability, distributed intelligence, and network bandwidth savings to industrial real-time applications. However, limited computing and processing capabilities of edge devices remains to be difficult to meet complex data processing and artificial intelligence (AI) analysis requirements for diverse services. Besides, the scarcity of wireless spectrum resources in harsh industrial environment makes the interference between devices more serious. To address these challenges, this paper proposes an adaptive distributed collaborative anti-interference optimization scheme for IoT-edge system. Firstly, the EC system model is established, and the model of link failure probability is derived theoretically. Then, an Occupy-Interference Mitigation (O-IM) algorithm based on full-frequency multiplexing is proposed. The algorithm combines adaptive full-frequency multiplexing and interference mitigation to reduce the dependence of the reliable collaboration on bandwidth and signal-to-noise ratio (SNR). In addition, an anti-interference algorithm based on Interference Mitigation and inter-cellfrequency multiplexing Interference Avoidance (IM-IA) is proposed to balance bandwidth and SNR. This algorithm adopts interference cancellation scheme in the broadcasting phase, and adopts orthogonal frequency division in the collaboration phase. Extensive simulation results on Mininet platform verify that the proposals can obtain lower failure probability, and are less than traditional solutions in transmitting power, bandwidth, and SNR requirements. Moreover, the O-IM is suitable for low power scenarios, while the IM-IA is more suitable for high power case.