inis 18(13): e4

Research Article

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

Download134 downloads
  • @ARTICLE{10.4108/eai.20-3-2018.154369,
        author={Peilin Zhang and Alex Yuan Gao and Oliver Theel},
        title={Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={4},
        number={13},
        publisher={EAI},
        journal_a={INIS},
        year={2018},
        month={3},
        keywords={Wireless Sensor Networks, Data Dissemination, Flooding, Multi-armed Bandit Problem, Machine Learning},
        doi={10.4108/eai.20-3-2018.154369}
    }
    
  • Peilin Zhang
    Alex Yuan Gao
    Oliver Theel
    Year: 2018
    Bandit Learning with Concurrent Transmissions for Energy-Efficient Flooding in Sensor Networks
    INIS
    EAI
    DOI: 10.4108/eai.20-3-2018.154369
Peilin Zhang1,*, Alex Yuan Gao2, Oliver Theel1
  • 1: Department of Computer Science, Carl von Ossietzky University of Oldenburg, Germany
  • 2: Department of Information Technology, Uppsala University, Sweden
*Contact email: peilin.zhang@informatik.uni-oldenburg.de

Abstract

Concurrent transmissions, a novel communication paradigm, has been shown to e ectively 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 e ect 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.