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Reducing Energy Consumption in Wireless Sensor Networks
Carlo Fischione

Citation
Carlo Fischione. "Reducing Energy Consumption in Wireless Sensor Networks". Talk or presentation, 11, December, 2007.

Abstract
Wireless Sensor Networks (WSNs) are ad hoc networks of tiny, autonomous devices equipped with wireless communication and sensing capabilities for networking, control and monitoring purposes. WSNs are characterized by limited resources in terms of communication, computation and energy supply. The need of achieving maximum efficiency from these systems motivates the challenge of a careful design that integrates functionalities traditionally considered to be separated, as communication, computation, and control. Indeed, despite major recent advances in each of these areas, several open questions have to be explored for the overall design of WSNs. In particular, the limited amount of processing resources of wireless nodes has a significant impact on many aspects of the system. It is expected that significant performance improvement can be achieved by exploiting the advantages offered by joint design of communication, control, and computation in WSNs, using the Platform Based Design approach. In this talk, we illustrate such a design methodology for the problem of energy consumption minimization of WSNs. Specifically, we present some relevant aspects of two coding schemes available in the literature: the Minimum Energy (ME) coding, and the Modified Minimum Energy (MME) coding. We start from a detailed and general model of the energy consumption of the nodes, which is a function of the hardware characteristics of the transceivers, the coding technique, the radio transmit powers, and the dynamics of the wireless channel. Then, we propose a new method to minimize the energy by optimizing all the variables of the model. In particular, we suggest an optimal way to design the detection thresholds of the receivers. Exploiting the limited feed-back of the channel state, and resorting to the theory of the contractive functions, a distributed radio power minimization algorithm is derived. The algorithm is shown to be stable and it requires low computational processing, making it amenable for implementation on limited hardware platforms. Next, an analysis of the bit error probability is addressed. Finally, the theoretical framework is applied to real WSNs to illustrate the tradeoffs between ME and MME coding.

Electronic downloads

Citation formats  
  • HTML
    Carlo Fischione. <a
    href="http://chess.eecs.berkeley.edu/pubs/381.html"
    ><i>Reducing Energy Consumption in Wireless Sensor
    Networks</i></a>, Talk or presentation,  11,
    December, 2007.
  • Plain text
    Carlo Fischione. "Reducing Energy Consumption in
    Wireless Sensor Networks". Talk or presentation,  11,
    December, 2007.
  • BibTeX
    @presentation{Fischione07_ReducingEnergyConsumptionInWirelessSensorNetworks,
        author = {Carlo Fischione},
        title = {Reducing Energy Consumption in Wireless Sensor
                  Networks},
        day = {11},
        month = {December},
        year = {2007},
        abstract = {Wireless Sensor Networks (WSNs) are ad hoc
                  networks of tiny, autonomous devices equipped with
                  wireless communication and sensing capabilities
                  for networking, control and monitoring purposes.
                  WSNs are characterized by limited resources in
                  terms of communication, computation and energy
                  supply. The need of achieving maximum efficiency
                  from these systems motivates the challenge of a
                  careful design that integrates functionalities
                  traditionally considered to be separated, as
                  communication, computation, and control. Indeed,
                  despite major recent advances in each of these
                  areas, several open questions have to be explored
                  for the overall design of WSNs. In particular, the
                  limited amount of processing resources of wireless
                  nodes has a significant impact on many aspects of
                  the system. It is expected that significant
                  performance improvement can be achieved by
                  exploiting the advantages offered by joint design
                  of communication, control, and computation in
                  WSNs, using the Platform Based Design approach. In
                  this talk, we illustrate such a design methodology
                  for the problem of energy consumption minimization
                  of WSNs. Specifically, we present some relevant
                  aspects of two coding schemes available in the
                  literature: the Minimum Energy (ME) coding, and
                  the Modified Minimum Energy (MME) coding. We start
                  from a detailed and general model of the energy
                  consumption of the nodes, which is a function of
                  the hardware characteristics of the transceivers,
                  the coding technique, the radio transmit powers,
                  and the dynamics of the wireless channel. Then, we
                  propose a new method to minimize the energy by
                  optimizing all the variables of the model. In
                  particular, we suggest an optimal way to design
                  the detection thresholds of the receivers.
                  Exploiting the limited feed-back of the channel
                  state, and resorting to the theory of the
                  contractive functions, a distributed radio power
                  minimization algorithm is derived. The algorithm
                  is shown to be stable and it requires low
                  computational processing, making it amenable for
                  implementation on limited hardware platforms.
                  Next, an analysis of the bit error probability is
                  addressed. Finally, the theoretical framework is
                  applied to real WSNs to illustrate the tradeoffs
                  between ME and MME coding.},
        URL = {http://chess.eecs.berkeley.edu/pubs/381.html}
    }
    

Posted by Douglas Densmore on 11 Dec 2007.
Groups: chess
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