Design, Automation, and Test in Europe · 2005 · 247 citations · 12 references
EngineeringWireless Sensor SystemEnergy EfficiencyPower ControlSensor ConnectivityGreen NetworkingSmart Wireless NetworkIeee 802.15.4Sensor NetworksImprovement PerspectivesDense NetworkInternet Of ThingsMedium Access ControlElectrical EngineeringEnergy HarvestingComputer EngineeringWireless Microsensor NetworksTransceiver ArchitectureEnergy-efficient Networking
Wireless microsensor networks are increasingly used in industry, and the IEEE 802.15.4 standard provides interoperable physical and MAC layers for sensor node radios. This study assesses the suitability of an 802.15.4 radio for ultra‑low‑power sensor nodes operating in dense networks. By measuring an off‑the‑shelf radio, the authors derived effective radio‑activation and link‑adaptation policies. The policies reduce average node power to 211 µW, and the energy‑breakdown analysis identifies transceiver components that can be further optimized to enable self‑powered microsensor networks.
Wireless microsensor networks, which have been the topic of intensive research in recent years, are now emerging in industrial applications. An important milestone in this transition has been the release of the IEEE 802.15.4 standard that specifies interoperable wireless physical and medium access control layers targeted to sensor node radios. In this paper, we evaluate the potential of an 802.15.4 radio for use in an ultra low power sensor node operating in a dense network. Starting from measurements carried out on the off-the-shelf radio, effective radio activation and link adaptation policies are derived. It is shown that, in a typical sensor network scenario, the average power per node can be reduced down to 211 /spl mu/W. Next, the energy consumption breakdown between the different phases of a packet transmission is presented, indicating which part of the transceiver architecture can most effectively be optimized in order to further reduce the radio power, enabling self-powered wireless microsensor networks.
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Deborah Estrin, Ramesh Govindan, John Heidemann et al. · 1999 · 2.7K citations
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Jan M. Rabaey, M.J. Ammer, J.L. da Silva et al. · Computer · 2000 · 1K citations
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