Panda: Neighbor Discovery on a Power Harvesting Budget

TL;DR

This paper introduces Panda, an energy-efficient neighbor discovery protocol tailored for energy harvesting low-power nodes, demonstrating significant improvements in discovery rate and adaptability to non-homogeneous power conditions through extensive real-world testing.

Contribution

The paper presents Panda, a novel neighbor discovery protocol optimized for energy harvesting devices, including a generalized model, parameter analysis, and a variant for non-homogeneous nodes, validated by testbed experiments.

Findings

Panda achieves up to 3x higher neighbor discovery rate compared to existing protocols.

Experimental results closely match analytical models with less than 2% difference.

Panda-D effectively operates under non-homogeneous energy harvesting conditions.

Abstract

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 tailored for 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 Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze \name 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 then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.