Using data mules for sensor network resiliency

Jon Crowcroft, Liron Levin, Michael Segal

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Scopus citations

    Abstract

    In this paper, we study the problem of efficient data recovery using the data mules approach, where a set of mobile sensors with advanced mobility capabilities re-acquire lost data by visiting the neighbors of failed sensors, thereby improving network resiliency. Our approach involves defining the optimal communication graph and mules' placements such that the overall traveling time and distance is minimized regardless to which sensors crashed. We explore this problem under different practical network topologies such as general graphs, grids and random linear networks and provide approximation algorithms based on multiple combinatorial techniques. Simulation experiments demonstrate that our algorithms outperform various competitive solutions for different network models, and that they are applicable for practical scenarios.

    Original languageEnglish
    Title of host publication2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2015
    PublisherInstitute of Electrical and Electronics Engineers
    Pages427-434
    Number of pages8
    ISBN (Electronic)9783901882746
    DOIs
    StatePublished - 6 Jul 2015
    Event2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2015 - Mumbai, India
    Duration: 25 May 201529 May 2015

    Publication series

    Name2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2015

    Conference

    Conference2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2015
    Country/TerritoryIndia
    CityMumbai
    Period25/05/1529/05/15

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Control and Optimization
    • Modeling and Simulation

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