Keyword(s):k-MaxRS, Maximizing Range Sum, Distributed Query Processing, Wireless Sensor Networks

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Sensor Networks

Abstract: We address the problem of in-network processing of k-Maximizing Range Sum (k-MaxRS) queries in Wireless Sensor Networks (WSN). The traditional, Computational Geometry version of the MaxRS problem considers the setting in which, given a set of (possibly weighted) 2D points, the goal is to determine the optimal location for a given (axes-parallel) rectangle R to be placed so that the sum of the weights (or, a simple count) of the input points in R’s interior is maximized. In WSN, this corresponds to finding the location of region R such that the sum of the sensors’ readings inside R is maximized. The k-MaxRS problem deals with maximizing the overall sum over k such rectangular regions. Since centralized processing – i.e., transmitting the raw readings and subsequently determining the k-MaxRS in a dedicated sink – incur communication overheads, we devised an efficient distributed algorithm for in-network computation of k-MaxRS. Our experimental observations show that the novel algorithm provides significant energy/communication savings when compared to the centralized approach.(More)

We address the problem of in-network processing of k-Maximizing Range Sum (k-MaxRS) queries in Wireless Sensor Networks (WSN). The traditional, Computational Geometry version of the MaxRS problem considers the setting in which, given a set of (possibly weighted) 2D points, the goal is to determine the optimal location for a given (axes-parallel) rectangle R to be placed so that the sum of the weights (or, a simple count) of the input points in R’s interior is maximized. In WSN, this corresponds to finding the location of region R such that the sum of the sensors’ readings inside R is maximized. The k-MaxRS problem deals with maximizing the overall sum over k such rectangular regions. Since centralized processing – i.e., transmitting the raw readings and subsequently determining the k-MaxRS in a dedicated sink – incur communication overheads, we devised an efficient distributed algorithm for in-network computation of k-MaxRS. Our experimental observations show that the novel algorithm provides significant energy/communication savings when compared to the centralized approach.

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Wonge-ammat, P.; Mas-ud Hussain, M.; Trajcevski, G.; Avci, B. and Khokhar, A. (2017). Distributed In-Network Processing of k-MaxRS in Wireless Sensor Networks.In Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-211-0, pages 108-117. DOI: 10.5220/0006210701080117

@conference{sensornets17, author={Panitan Wonge{-}ammat. and Muhammed Mas{-}ud Hussain. and Goce Trajcevski. and Besim Avci. and Ashfaq Khokhar.}, title={Distributed In-Network Processing of k-MaxRS in Wireless Sensor Networks}, booktitle={Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,}, year={2017}, pages={108-117}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0006210701080117}, isbn={978-989-758-211-0}, }

TY - CONF

JO - Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, TI - Distributed In-Network Processing of k-MaxRS in Wireless Sensor Networks SN - 978-989-758-211-0 AU - Wonge-ammat, P. AU - Mas-ud Hussain, M. AU - Trajcevski, G. AU - Avci, B. AU - Khokhar, A. PY - 2017 SP - 108 EP - 117 DO - 10.5220/0006210701080117