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Conference Paper: Minimumlatency aggregation scheduling in wireless sensor networks under physical interference model
Title  Minimumlatency aggregation scheduling in wireless sensor networks under physical interference model 

Authors  
Keywords  data aggregation minimum latency physical interference model wireless sensor networks 
Issue Date  2010 
Publisher  Association for Computing Machinery 
Citation  The 13th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2010), Bodrum, Turkey, 1721 October 2010. In Proceedings of 13th MSWiM, 2010, p. 360367 How to Cite? 
Abstract  MinimumLatency Aggregation Scheduling (MLAS) is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for globalscale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log3n) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the nonlinear power assignment strategy in [9]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results. © 2010 ACM. 
Persistent Identifier  http://hdl.handle.net/10722/125682 
ISBN  
References 
DC Field  Value  Language 

dc.contributor.author  Li, H  en_HK 
dc.contributor.author  Hua, QS  en_HK 
dc.contributor.author  Wu, C  en_HK 
dc.contributor.author  Lau, FCM  en_HK 
dc.date.accessioned  20101031T11:45:48Z   
dc.date.available  20101031T11:45:48Z   
dc.date.issued  2010  en_HK 
dc.identifier.citation  The 13th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2010), Bodrum, Turkey, 1721 October 2010. In Proceedings of 13th MSWiM, 2010, p. 360367  en_HK 
dc.identifier.isbn  9781450302746   
dc.identifier.uri  http://hdl.handle.net/10722/125682   
dc.description.abstract  MinimumLatency Aggregation Scheduling (MLAS) is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for globalscale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log3n) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the nonlinear power assignment strategy in [9]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results. © 2010 ACM.  en_HK 
dc.language  eng  en_HK 
dc.publisher  Association for Computing Machinery   
dc.relation.ispartof  Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems, MSWiM 2010  en_HK 
dc.subject  data aggregation  en_HK 
dc.subject  minimum latency  en_HK 
dc.subject  physical interference model  en_HK 
dc.subject  wireless sensor networks  en_HK 
dc.title  Minimumlatency aggregation scheduling in wireless sensor networks under physical interference model  en_HK 
dc.type  Conference_Paper  en_HK 
dc.identifier.openurl  http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=9781450302746&volume=&spage=360&epage=367&date=2010&atitle=Minimumlatency+aggregation+scheduling+in+wireless+sensor+networks+under+physical+interference+model   
dc.identifier.email  Wu, C:cwu@cs.hku.hk  en_HK 
dc.identifier.email  Lau, FCM:fcmlau@cs.hku.hk  en_HK 
dc.identifier.authority  Wu, C=rp01397  en_HK 
dc.identifier.authority  Lau, FCM=rp00221  en_HK 
dc.description.nature  link_to_OA_fulltext   
dc.identifier.doi  10.1145/1868521.1868581  en_HK 
dc.identifier.scopus  eid_2s2.078650222013  en_HK 
dc.identifier.hkuros  175395  en_HK 
dc.relation.references  http://www.scopus.com/mlt/select.url?eid=2s2.078650222013&selection=ref&src=s&origin=recordpage  en_HK 
dc.identifier.spage  360  en_HK 
dc.identifier.epage  367  en_HK 
dc.publisher.place  United States   
dc.description.other  The 13th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2010), Bodrum, Turkey, 1721 October 2010. In Proceedings of 13th MSWiM, 2010, p. 360367   
dc.identifier.scopusauthorid  Li, H=35292662600  en_HK 
dc.identifier.scopusauthorid  Hua, QS=15060090400  en_HK 
dc.identifier.scopusauthorid  Wu, C=15836048100  en_HK 
dc.identifier.scopusauthorid  Lau, FCM=7102749723  en_HK 