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SenseSwarm: a perimeter-based data acquisition framework for mobile sensor networks

Published: 24 September 2007 Publication History

Abstract

This paper assumes a set of n mobile sensors that move in the Euclidean plane as a swarm. Our objectives are to explore a given geographic region by detecting and aggregating spatio-temporal events of interest and to store these events in the network until the user requests them. Such a setting finds applications in environments where the user (i.e., the sink) is infrequently within communication range from the field deployment. Our framework, coined SenseSwarm, dynamically partitions the sensing devices into perimeter and core nodes. Data acquisition is scheduled at the perimeter in order to minimize energy consumption while storage and replication takes place at the core nodes which are physically and logically shielded to threats and obstacles. To efficiently identify the perimeter of the swarm we devise the Perimeter Algorithm (PA), an efficient distributed algorithm with a message complexity of O(p + n), where p denotes the number of nodes on the perimeter and n the overall number of nodes. For storage and replication we devise a spatio-temporal in-network aggregation scheme based on minimum bounding rectangles and minimum bounding cuboids. Our trace-driven experimentation shows that our framework can offer significant energy reductions while maintaining high data availability rates.

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cover image ACM Other conferences
DMSN '07: Proceedings of the 4th workshop on Data management for sensor networks: in conjunction with 33rd International Conference on Very Large Data Bases
September 2007
46 pages
ISBN:9781595939111
DOI:10.1145/1286380
  • General Chairs:
  • Amol Deshpande,
  • Qiong Luo
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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  • Intel: Intel

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New York, NY, United States

Publication History

Published: 24 September 2007

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Overall Acceptance Rate 6 of 16 submissions, 38%

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  • (2018)A New Metric for the Analysis of Swarms Using Potential FieldsIEEE Access10.1109/ACCESS.2018.28774216(63258-63267)Online publication date: 2018
  • (2018)Mobile Sensor Network Data ManagementEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_221(2283-2288)Online publication date: 7-Dec-2018
  • (2017)Mobile Sensor Network Data ManagementEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_221-3(1-6)Online publication date: 14-Feb-2017
  • (2016)Mobile Sensor Network Data ManagementEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_221-2(1-6)Online publication date: 7-Dec-2016
  • (2010)Sensor relocation for emergent data acquisition in sparse mobile sensor networksMobile Information Systems10.1155/2010/6515896:2(155-176)Online publication date: 1-Apr-2010
  • (2010)Adaptive monitoring of marine disasters with intelligent mobile sensor networks2010 IEEE Workshop on Environmental Energy and Structural Monitoring Systems10.1109/EESMS.2010.5634179(38-45)Online publication date: Sep-2010
  • (2009)Perimeter-Based Data Replication in Mobile Sensor NetworksProceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware10.1109/MDM.2009.36(244-251)Online publication date: 18-May-2009
  • (2009)Perimeter discovery in wireless sensor networksJournal of Parallel and Distributed Computing10.1016/j.jpdc.2009.08.00269:11(922-929)Online publication date: 1-Nov-2009
  • (2009)Mobile Sensor Network Data ManagementEncyclopedia of Database Systems10.1007/978-0-387-39940-9_221(1755-1759)Online publication date: 2009
  • (2008)Energy consumption vs. latency in a new boundary identification method for WSNs with a mobile sinkProceedings of the 6th ACM international symposium on Mobility management and wireless access10.1145/1454659.1454681(125-132)Online publication date: 30-Oct-2008
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