Panda: Neighbor Discovery on a Power Harvesting Budget

Object tracking applications are gaining popularity and will soon utilize energy harvesting (EH) low-power nodes that will consume power mostly for neighbor discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges p...

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Bibliographic Details
Published inIEEE journal on selected areas in communications Vol. 34; no. 12; pp. 3606 - 3619
Main Authors Margolies, Robert, Grebla, Guy, Tingjun Chen, Rubenstein, Dan, Zussman, Gil
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Capacitors
Energy efficiency
Energy harvesting
Green communications
Neighbor discovery
Peer-to-peer computing
Protocols
Prototypes
Transceivers
wireless
Wireless communication
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Summary:Object tracking applications are gaining popularity and will soon utilize energy harvesting (EH) low-power nodes that will consume power mostly for neighbor discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailoredfor the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware ND Asynchronously (Panda) protocol, in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less than 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3× compared with related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2016.2611984