14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

Less is More: Learning More with Concurrent Transmissions for Energy-Efficient Flooding

  • @INPROCEEDINGS{10.4108/eai.7-11-2017.2273530,
        author={Peilin Zhang and Alex Yuan Gao and Oliver Theel},
        title={Less is More: Learning More with Concurrent Transmissions for Energy-Efficient Flooding},
        proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ACM},
        proceedings_a={MOBIQUITOUS},
        year={2018},
        month={4},
        keywords={flooding data dissemination machine learning exp3 wireless sensor networks},
        doi={10.4108/eai.7-11-2017.2273530}
    }
    
  • Peilin Zhang
    Alex Yuan Gao
    Oliver Theel
    Year: 2018
    Less is More: Learning More with Concurrent Transmissions for Energy-Efficient Flooding
    MOBIQUITOUS
    ACM
    DOI: 10.4108/eai.7-11-2017.2273530
Peilin Zhang1,*, Alex Yuan Gao2, Oliver Theel1
  • 1: Carl von Ossietzky University of Oldenburg
  • 2: Uppsala University
*Contact email: peilin.zhang@informatik.uni-oldenburg.de

Abstract

Concurrent transmissions, a novel communication paradigm, has been shown to effectively accomplish a reliable and energy-efficient flooding in 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 paper, we propose LiM, an energy-efficient flooding protocol for wireless sensor networks. LiM builds on concurrent transmissions, exploiting constructive interference and the capture effect to achieve high reliability and low latency. Moreover, LiM equips itself 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 ms in all experimental scenarios.