Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Peilin Zhang¹  Oliver Theel¹  Alex Yuan Gao² 

[1] Department of Computer Science, Carl von Ossietzky University of Oldenburg, Germany;Department of Information Technology, Uppsala University, Sweden;

关键词: wireless sensor networks;    data dissemination;    flooding;    multi-armed bandit problem;    machine learning;   

DOI  :  10.4108/eai.20-3-2018.154369

来源: DOAJ

【 摘 要 】

Concurrent transmissions, a novel communication paradigm, has been shown to eectively accomplish a reliable and energy-eÿcient flooding in low-power wireless networks. With multiple nodes exploiting a receive-and-forward scheme in the network, this technique inevitably introduces communication redundancy and consequently raises the energy consumption of the nodes. In this article, we propose Less is More (LiM), an energy-eÿcient flooding protocol for wireless sensor networks. LiM builds on concurrent transmissions, exploiting constructive interference and the capture eect to achieve high reliability and low latency. Moreover, LiM is equipped with a machine learning capability to progressively reduce redundancy while maintaining high reliability. As a result, LiM is able to significantly reduce the radio-on time and therefore the energy consumption. We compare LiM with our baseline protocol Glossy by extensive experiments in the 30-node testbed FlockLab. Experimental results show that LiM highly reduces the broadcast redundancy in flooding. It outperforms the baseline protocol in terms of radio-on time, while attaining a high reliability of over 99.50% and an average end-to-end latency around 2 milliseconds in all experimental scenarios.

【 授权许可】

Unknown