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Energy-efficient multihop reprogramming for sensor networks

Published:03 April 2009Publication History
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Abstract

Reprogramming of sensor networks is an important and challenging problem, as it is often necessary to reprogram the sensors in place. In this article, we propose MNP, a multihop reprogramming service designed for sensor networks. One of the problems in reprogramming is the issue of message collision. To reduce the problem of collision, we propose a sender selection algorithm that attempts to guarantee that in a given neighborhood there is at most one source transmitting the program at a time. Furthermore, our sender selection is greedy in that it tries to select the sender that is expected to have the most impact. We use pipelining to enable fast data propagation. MNP is energy efficient because it reduces the active radio time of a sensor node by putting the node into “sleep” state when its neighbors are transmitting a segment that is not of interest. We call this type of sleep contention sleep. To further reduce the energy consumption, we add noreq sleep, where sensor node goes to sleep if none of its neighbors is interested in receiving the segment it is advertising. We also introduce an optional init sleep to reduce the energy consumption in the initial phase of reprogramming. Finally, we investigate the performance of MNP in different network settings.

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  1. Energy-efficient multihop reprogramming for sensor networks

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        John W. Fendrich

        Because of acquired understanding of the sensor network environment and continual technological advancement, the software running on sensor nodes needs to be changed; that is, the sensor network needs to be reprogrammed. This paper discusses the issues involved: 100 percent delivery, relatively high bandwidth requirements, message collisions and congestion, concurrent senders, and the importance of energy efficiency for low-powered sensor nodes. It presents a code dissemination protocol, multihop network reprogramming protocol (MNP), to provide "a reliable and energy-efficient service to propagate new program code to all sensor nodes in the network, over wireless radio." Contributions include a sensor selection algorithm, "pipelining to enable fast data propagation," and three innovative sleep strategies for energy conservation: contention sleep, noreq sleep, and init sleep. Another contribution is implementation and performance evaluations using the TinyOS platform and "TOSSIM, a discrete event simulator for TinyOS wireless sensor networks." This includes the identification of optimal values of the three sleep parameters in different network densities, network sizes, and base station locations, and the use of these optimal values to observe the performance of MNP. This report is on experiments done on a preliminary version of MNP, not the latest version. This analysis goes on to identify and discuss more MNP-related issues. Kulkarni and Wang present a perspective on the related work of others, enabling readers to see how this work fits in with other sensor network reprogramming research. The authors also determine that investigation is needed on the use of MNP in the dissemination of any data. Online Computing Reviews Service

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        • Published in

          cover image ACM Transactions on Sensor Networks
          ACM Transactions on Sensor Networks  Volume 5, Issue 2
          March 2009
          284 pages
          ISSN:1550-4859
          EISSN:1550-4867
          DOI:10.1145/1498915
          Issue’s Table of Contents

          Copyright © 2009 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 3 April 2009
          • Accepted: 1 May 2008
          • Revised: 1 March 2008
          • Received: 1 August 2006
          Published in tosn Volume 5, Issue 2

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