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LiMoSense: live monitoring in dynamic sensor networks

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Abstract

We present LiMoSense, a fault-tolerant live monitoring algorithm for dynamic sensor networks. This is the first asynchronous robust average aggregation algorithm that performs live monitoring, i.e., it constantly obtains a timely and accurate picture of dynamically changing data. LiMoSense uses gossip to dynamically track and aggregate a large collection of ever-changing sensor reads. It overcomes message loss, node failures and recoveries, and dynamic network topology changes. The algorithm uses a novel technique to bound variable size. We present the algorithm and formally prove its correctness. We use simulations to illustrate its ability to quickly react to changes of both the network topology and the sensor reads, and to provide accurate information.

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Notes

  1. For any variable, the node it belongs to is written in subscript and, when relevant, the time is written in superscript.

  2. There is a rich literature dealing with the means of detecting failures, usually with timeouts. This subject is outside the scope of this work.

  3. Note that the weight at a node never drops below \(q\), so the expression is valid.

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Acknowledgments

The authors thank an anonymous reviewer for important comments on an earlier version of this work.

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Authors and Affiliations

  1. Department of Computer Science, Cornell University, Ithaca, NY, USA

    Ittay Eyal

  2. Department of Electrical Engineering, Technion, Haifa, Israel

    Idit Keidar & Raphael Rom

Authors
  1. Ittay Eyal
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  2. Idit Keidar
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  3. Raphael Rom
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Correspondence to Ittay Eyal.

Additional information

A preliminary version of this paper appears in the proceedings of the 7th International Symposium on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities (ALGOSENSOR) [7].

This work was partially supported by the Israeli Science Foundation (ISF), Technion Funds for Security Research, the Technion Autonomous Systems Program (TASP), the Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI), and the Hasso-Plattner Institute for Software Systems Engineering (HPI).

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Eyal, I., Keidar, I. & Rom, R. LiMoSense: live monitoring in dynamic sensor networks. Distrib. Comput. 27, 313–328 (2014). https://doi.org/10.1007/s00446-014-0213-8

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  • DOI: https://doi.org/10.1007/s00446-014-0213-8

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