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Energy-efficient low power listening for wireless sensor networks in noisy environments

Published: 08 April 2013 Publication History

Abstract

Low Power Listening (LPL) is a common MAC-layer technique for reducing energy consumption in wireless sensor networks, where nodes periodically wakeup to sample the wireless channel to detect activity. However, LPL is highly susceptible to false wakeups caused by environmental noise being detected as activity on the channel, causing nodes to spuriously wakeup in order to receive nonexistent transmissions. In empirical studies in residential environments, we observe that the false wakeup problem can significantly increase a node's duty cycle, compromising the benefit of LPL. We also find that the energy-level threshold used by the Clear Channel Assessment (CCA) mechanism to detect channel activity has a significant impact on the false wakeup rate. We then design AEDP, an adaptive energy detection protocol for LPL, which dynamically adjusts a node's CCA threshold to improve network reliability and duty cycle based on application-specified bounds. Empirical experiments in both controlled tests and real-world environments showed AEDP can effectively mitigate the impact of noise on radio duty cycles, while maintaining satisfactory link reliability.

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    cover image ACM Conferences
    IPSN '13: Proceedings of the 12th international conference on Information processing in sensor networks
    April 2013
    372 pages
    ISBN:9781450319591
    DOI:10.1145/2461381
    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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    Publication History

    Published: 08 April 2013

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    Author Tags

    1. CCA control
    2. low power listening
    3. wireless sensor networks

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    IPSN '13 Paper Acceptance Rate 24 of 115 submissions, 21%;
    Overall Acceptance Rate 143 of 593 submissions, 24%

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    • (2024)Secure opportunistic routing in 2-hop IEEE 802.15.4 networks with SMORComputer Communications10.1016/j.comcom.2024.01.024217:C(57-69)Online publication date: 25-Jun-2024
    • (2023)Towards Sustainable Distributed Sensor Networks: An Approach for Addressing Power Limitation Issues in WSNsSensors10.3390/s2302097523:2(975)Online publication date: 14-Jan-2023
    • (2023)Optimizing Energy Efficiencies of IoT-based Wireless Sensor Network Components for Metaverse Sustainable Development using Carry Resist Adder based Booth Recoder (CRABRA)2023 20th Learning and Technology Conference (L&T)10.1109/LT58159.2023.10092334(91-96)Online publication date: 26-Jan-2023
    • (2023)Transparent and Tamper-Proof Event Ordering in the Internet of Things PlatformsIEEE Internet of Things Journal10.1109/JIOT.2022.322245010:6(5335-5348)Online publication date: 15-Mar-2023
    • (2023)A Survey on Designing Efficient WSN Using Duty Cycle OptimizationAdvanced Communication and Intelligent Systems10.1007/978-3-031-45124-9_17(219-230)Online publication date: 11-Oct-2023
    • (2022)Challenges for Convergence of Cloud and IoT in Applications and Edge ComputingResearch Anthology on Edge Computing Protocols, Applications, and Integration10.4018/978-1-6684-5700-9.ch032(644-662)Online publication date: 1-Apr-2022
    • (2022)SMAC-Based WSN Protocol-Current State of the Art, Challenges, and Future DirectionsJournal of Computer Networks and Communications10.1155/2022/67074132022Online publication date: 1-Jan-2022
    • (2022)Collision-Free Dynamic Convergecast in Low-Duty-Cycle Wireless Sensor NetworksIEEE Transactions on Wireless Communications10.1109/TWC.2021.310598321:3(1665-1680)Online publication date: Mar-2022
    • (2021)Challenges for Convergence of Cloud and IoT in Applications and Edge ComputingChallenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing10.4018/978-1-7998-3111-2.ch002(17-36)Online publication date: 2021
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