Authors:
Panitan Wonge-ammat
1
;
Muhammed Mas-ud Hussain
1
;
Goce Trajcevski
1
;
Besim Avci
1
and
Ashfaq Khokhar
2
Affiliations:
1
Northwestern University, United States
;
2
Illinois Institute of Technology, United States
Keyword(s):
k-MaxRS, Maximizing Range Sum, Distributed Query Processing, Wireless Sensor Networks
Related
Ontology
Subjects/Areas/Topics:
Aggregation, Classification and Tracking
;
Applications and Uses
;
Data Manipulation
;
Energy Efficiency
;
Energy Efficiency and Green Manufacturing
;
Environment Monitoring
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Obstacles
;
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.
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