Low-complexity algorithms for event detection in wireless sensor networks
To ensure that a multi-hop cluster of batterypowered, wireless sensor motes can complete all of its tasks, each task must minimize its use of communication and processing resources. For event detection tasks that are subject to both measurement errors by sensors and communication errors in the wirel...
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| Published in: | IEEE journal on selected areas in communications Vol. 28; no. 7; pp. 1138 - 1148 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.09.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0733-8716, 1558-0008 |
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| Abstract | To ensure that a multi-hop cluster of batterypowered, wireless sensor motes can complete all of its tasks, each task must minimize its use of communication and processing resources. For event detection tasks that are subject to both measurement errors by sensors and communication errors in the wireless channel, this implies that: (i) the Cluster-Head (CH) must optimally fuse the decisions received from its cluster in order to reduce the effect of measurement errors; (ii) the CH and all motes that relay other motes' decisions must adopt lowcomplexity processing and coding algorithms that minimize the effects of communication errors. This paper combines a Maximum a Posteriori (MAP) approach for local and global decisions in multi-hop sensor networks with low-complexity repetition codes and processing algorithms. It is shown by analysis and confirmed by simulation that there exists an odd integer M and an integer K M such the decision error probability at the CH is reduced when: (1) nodes in rings k ≤ K M hops from the CH directly relay their decisions to the CH; (2) nodes in rings k > K M locally fuse groups of M decisions and then use a repetition code to forward these fused decisions to the CH; and (3) K M is a nondecreasing function of M. This algorithm - and hybrid, hierarchical, and compression approaches based on it - enable tradeoffs amongst the probability of error, energy usage, compression ratio, complexity, and time to decision. |
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| AbstractList | To ensure that a multi-hop cluster of batterypowered, wireless sensor motes can complete all of its tasks, each task must minimize its use of communication and processing resources. For event detection tasks that are subject to both measurement errors by sensors and communication errors in the wireless channel, this implies that: (i) the Cluster-Head (CH) must optimally fuse the decisions received from its cluster in order to reduce the effect of measurement errors; (ii) the CH and all motes that relay other motes' decisions must adopt lowcomplexity processing and coding algorithms that minimize the effects of communication errors. This paper combines a Maximum a Posteriori (MAP) approach for local and global decisions in multi-hop sensor networks with low-complexity repetition codes and processing algorithms. It is shown by analysis and confirmed by simulation that there exists an odd integer M and an integer K sub(M) such the decision error probability at the CH is reduced when: (1) nodes in rings k [els] K sub(M) hops from the CH directly relay their decisions to the CH; (2) nodes in rings k >K sub(M) locally fuse groups of M decisions and then use a repetition code to forward these fused decisions to the CH; and (3) K sub(M) is a nondecreasing function of M. This algorithm - and hybrid, hierarchical, and compression approaches based on it - enable tradeoffs amongst the probability of error, energy usage, compression ratio, complexity, and time to decision. To ensure that a multi-hop cluster of batterypowered, wireless sensor motes can complete all of its tasks, each task must minimize its use of communication and processing resources. For event detection tasks that are subject to both measurement errors by sensors and communication errors in the wireless channel, this implies that: (i) the Cluster-Head (CH) must optimally fuse the decisions received from its cluster in order to reduce the effect of measurement errors; (ii) the CH and all motes that relay other motes' decisions must adopt lowcomplexity processing and coding algorithms that minimize the effects of communication errors. This paper combines a Maximum a Posteriori (MAP) approach for local and global decisions in multi-hop sensor networks with low-complexity repetition codes and processing algorithms. It is shown by analysis and confirmed by simulation that there exists an odd integer M and an integer K M such the decision error probability at the CH is reduced when: (1) nodes in rings k ≤ K M hops from the CH directly relay their decisions to the CH; (2) nodes in rings k > K M locally fuse groups of M decisions and then use a repetition code to forward these fused decisions to the CH; and (3) K M is a nondecreasing function of M. This algorithm - and hybrid, hierarchical, and compression approaches based on it - enable tradeoffs amongst the probability of error, energy usage, compression ratio, complexity, and time to decision. To ensure that a multi-hop cluster of batterypowered, wireless sensor motes can complete all of its tasks, each task must minimize its use of communication and processing resources. For event detection tasks that are subject to both measurement errors by sensors and communication errors in the wireless channel, this implies that: (i) the Cluster-Head (CH) must optimally fuse the decisions received from its cluster in order to reduce the effect of measurement errors; (ii) the CH and all motes that relay other motes' decisions must adopt lowcomplexity processing and coding algorithms that minimize the effects of communication errors. This paper combines a Maximum a Posteriori (MAP) approach for local and global decisions in multi-hop sensor networks with low-complexity repetition codes and processing algorithms. It is shown by analysis and confirmed by simulation that there exists an odd integer M and an integer KM such the decision error probability at the CH is reduced when: (1) nodes in rings k [= or <, slanted] KM hops from the CH directly relay their decisions to the CH; (2) nodes in rings k >KM locally fuse groups of M decisions and then use a repetition code to forward these fused decisions to the CH; and (3) KM is a nondecreasing function of M. This algorithm - and hybrid, hierarchical, and compression approaches based on it - enable tradeoffs amongst the probability of error, energy usage, compression ratio, complexity, and time to decision. |
| Author | Coyle, E J Xusheng Sun |
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| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Sep 2010 |
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| References_xml | – ident: ref16 doi: 10.1109/TSP.2006.888888 – start-page: 2525 year: 2008 ident: ref22 article-title: Group-ordered SPRT for distributed detection publication-title: Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP) – ident: ref24 doi: 10.1109/LSP.2008.916720 – ident: ref19 doi: 10.1109/TSP.2007.909355 – ident: ref9 doi: 10.1109/TSP.2007.894286 – ident: ref11 doi: 10.1109/TSP.2005.850334 – year: 0 ident: ref7 article-title: Adaptive Quantization for Distributed Characterization of Interferers in Wireless Networks publication-title: SenSIP 2008 special issue of Digital Signal Processing – ident: ref6 doi: 10.1016/S1389-1286(03)00320-7 – ident: ref21 doi: 10.1109/TSP.2007.898773 – year: 2009 ident: ref25 article-title: Quantization, Channel Compensation, and Energy Allocation for Estimation in Wireless Sensor Networks publication-title: 7'th Intl Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks (WiOpt 2009) – ident: ref18 doi: 10.1109/TWC.2007.05769 – year: 0 ident: ref2 publication-title: Wireless Sensor Network Technology Trends Report Summer 2008 – ident: ref3 doi: 10.1109/TRIDNT.2006.1649167 – ident: ref20 doi: 10.1109/TIT.2008.920217 – ident: ref5 doi: 10.1109/ICIW.2008.57 – year: 2010 ident: ref1 article-title: Local Decisions and Optimal Distributed Detection in Mobile Wireless Sensor Networks publication-title: 6'th Intl Workshop on Wireless Networks-Communication Cooperation and Competition (WNC3 2010) – ident: ref26 doi: 10.1109/TNET.2005.860111 – ident: ref14 doi: 10.1109/TSP.2007.906770 – ident: ref23 doi: 10.1109/TSP.2007.894410 – ident: ref13 doi: 10.1109/MSP.2006.1657814 – ident: ref8 doi: 10.1109/JSAC.2004.830894 – ident: ref4 doi: 10.1109/CHINACOM.2006.344683 – ident: ref17 doi: 10.1109/TSP.2006.890914 – start-page: 1651 year: 2004 ident: ref10 article-title: Optimal distributed detection strategies for wireless sensor networks publication-title: Proc 42nd Annu Allerton Conf Communication Control and Computing – ident: ref15 doi: 10.1109/TSP.2006.887563 – ident: ref12 doi: 10.1109/TIT.2004.840879 |
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| SubjectTerms | Algorithms Clustering algorithms Clusters Decisions Detectors distributed detection energy Error analysis Error detection Error probability Fuses Relays Sensor networks Sensors Spread spectrum communication Studies Tasks Wireless communication Wireless sensor networks |
| Title | Low-complexity algorithms for event detection in wireless sensor networks |
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