Adaptive optimal power trade-off in underwater sensor networks

Devesh K. Jha, Thomas A. Wettergren, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    In general, sensor networks have two competing objectives: (i) maximization of network performance with respect to the probability of successful search with a specified false alarm rate for a given coverage area, and (ii) maximization of the network's operational life. In this context, battery-powered sensing systems are operable as long as they can communicate sensed data to the processing nodes. Since both operations of sensing and communication consume energy, judicious use of these operations could effectively improve the sensor network's lifetime. From these perspectives, the paper presents an adaptive energy management policy that will optimally allocate the available energy between sensing and communication operations at each node to maximize the network performance under specified constraints. With the assumption of fixed total energy for a sensor network operating over a time period, the problem is reduced to identification of a network topology that maximizes the probability of successful detection of targets over a surveillance region. In a two-stage optimization, a genetic algorithm-based meta-heuristic search is first used to efficiently explore the global design space, and then a local pattern search algorithm is used for convergence to an optimal solution. The results of performance evaluation are presented to validate the proposed concept.

    Original languageEnglish (US)
    Title of host publicationControl, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;
    PublisherAmerican Society of Mechanical Engineers (ASME)
    ISBN (Print)9780791856130
    StatePublished - Jan 1 2013
    EventASME 2013 Dynamic Systems and Control Conference, DSCC 2013 - Palo Alto, CA, United States
    Duration: Oct 21 2013Oct 23 2013


    OtherASME 2013 Dynamic Systems and Control Conference, DSCC 2013
    Country/TerritoryUnited States
    CityPalo Alto, CA

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering


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