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ATR-MAC: deadline driven data collection from smartmeters in absence of aggregation

Published: 03 November 2013 Publication History

Abstract

Deployment of Smart Grid networks throughout cities introduces a realistic scenario for a large scale sensor network. Smart Meters create a wireless mesh network for receiving commands and sending metering reports back to provider company. The challenges of reliable and timely communication call for a fresh review of the well studied topic of Wireless Sensor Networks (WSN). We provide a centralized MAC solution for data collection, ATR-MAC, that relaxes requirements in general purpose WSNs, but emphasizes on difficulties a Smart Metering deployment will face. ATR-MAC considers variable reuse distances for minimizing propagation delay. It also increases efficiency of collection deadline instead of energy efficiency, a common concern of general-purpose WSN MAC protocols. We provide time and buffer size analysis of our algorithm, prove its correctness and show experimental results using implementation on a testbed.

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    cover image ACM Conferences
    MSWiM '13: Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
    November 2013
    468 pages
    ISBN:9781450323536
    DOI:10.1145/2507924
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 November 2013

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    Author Tags

    1. evaluation
    2. network infrastructure
    3. smart grid

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    MSWiM '13 Paper Acceptance Rate 42 of 184 submissions, 23%;
    Overall Acceptance Rate 398 of 1,577 submissions, 25%

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