Combining sensor selection with routing and scheduling in wireless sensor networks

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Title: Combining sensor selection with routing and scheduling in wireless sensor networks
Authors: Muldoon, Conor
Trigoni, Niki
O'Hare, G. M. P. (Greg M. P.)
Permanent link: http://hdl.handle.net/10197/3510
Date: 29-Aug-2011
Abstract: In wireless sensor networks, determining the set of locations to activate sensors, such that the amount of information received is maximised, is an important task. With the model driven approach to sensor networks, a considerable amount of research has been conducted into the sensor selection problem, whereby informative locations to activate sensors are determined through the use of Gaussian Processes. Current approaches to sensor selection, however, do not take bandwidth constraints into consideration. When bandwidth is limited, the amount of time required to realise a given set of communication requests, in terms of rout- ing and scheduling, must be accounted for. Traditionally, in wireless sensor networks, sensor selection, routing, and scheduling are considered separately. These three processes, however, are highly coupled and the overall utility of the data collection process, in terms of the amount of information it produces, can be improved by integrating them. In this paper, we present two approaches to combining routing, scheduling, and sensor selection such that they inform each other so as to improve the performance of the network in the bandwidth limited case.
Funding Details: Science Foundation Ireland
Irish Research Council for Science, Engineering and Technology
European Research Council
Type of material: Conference Publication
Copyright (published version): 2011 ACM
Keywords: Sensor selection;Gaussian processes
Subject LCSH: Wireless sensor networks
Gaussian processes
Language: en
Status of Item: Peer reviewed
Conference Details: Paper presented at the 8th International Workshop on Data Management for Sensor Networks (DMSN’11)‚ held in conjunction with the International Conference on Very Large Data Bases (VLDB)‚ Seattle, USA, 29 August 2011
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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