The University of Southampton

A Learning-Based Approach for Efficient Long-term Information Collection in Wireless Sensor Networks

Algorithms for Wireless Sensor Networks, Decentralised Information Systems, Agent Based Computing, Intelligent Systems and Machine Learning, Decentralised Architectures

We are entering a new age in the evolution of computer systems, in which pervasive computing technologies seamlessly interact with human users [Satyanarayanan, 2001;Weiser, 1991]. These technologies serve people in their everyday lives at home and work by functioning invisibly in the background. They free them from tedious routine tasks and create a smart environment around them [Cook and Das, 2004]. In the influential article “The Computer for the 21st Century”, Mark Weiser described smart environments as a “physical world that is richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives, and connected through a continuous network” [Weiser, 1991]. For example, this would be an intelligent building, or a smart traffic control system. Now, since such smart environments need information about their surroundings, they rely first and foremost on sensory data from the real world. More accurately, this data is provided by wireless sensor networks, which are responsible for sensing as well as for information collecting [Lewis, 2004]. Thus, improving the efficiency of these tasks in the networks of wireless sensors is of necessity. Given this, we will focus on efficient long-term (e.g. lifetime-long) information collection of these networks, using learning-theory to tackle this challenge.

Primary investigators

Associated research group

  • Agents, Interaction and Complexity
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