2026-09-21T03:08:19Zhttps://digital.library.adelaide.edu.au/server/oai/request

oai:digital.library.adelaide.edu.au:2440/718522012-11-12T23:28:52Zcom_2440_14759col_2440_14760

Lim, Cheng-Chew Asenstorfer, John Anthony Wang, Yuexian School of Electrical and Electronic Engineering 2011 http://hdl.handle.net/2440/71852 Wireless sensor networks, when compared with other traditional wireless communication systems, possess two unique characteristics: (i) the limited battery power supply of sensor nodes, and (ii) the redundant data which are correlated among different nodes. These two are associated with energy consumption and data traffic control. The research in this thesis aims at designing an energy efficient routing scheme with data aggregation in wireless sensor networks. In this thesis, we developed an energy-efficient routing scheme consisting of the setup phase, the routing tree optimisation phase and the data gathering phase. The setup phase is to build initial routing trees by the ant colony optimisation algorithm which is executed between the base station and all sensor nodes. A key to our routing scheme is the routing tree optimisation phase. The routing tree optimisation is performed by the base station using the particle swarm optimisation algorithm. We propose a modified particle swarm optimisation algorithm that is capable of jointly exploring the data traffic and communication structure to provide the optimal strategy for data gathering. Once the routing tree optimisation has been accomplished, it comes to the data gathering phase. Data flows to the aggregator node, the aggregator node then transmits the gathering data to the base station via multi-hop in this phase of operation. The performance of our routing scheme is evaluated by comparing with three existing routing schemes using simulations. Our scheme performs as well as the shortest path tree algorithm and saves more than 45% energy over the other two algorithms in the non-aggregation scenario. If perfect aggregation occurs, our scheme obtains about 5% energy reduction at least. When varying from non to perfect aggregation, the simulation results show that our scheme can adapt to the change of data correlation condition and achieve at least 25% energy saving on average. Since our scheme can save energy and enhance transmission efficiency, it is well suited for applications where energy and data traffic are the primary considerations. Thesis (M.Eng.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2011 sensor networks; data aggregation; particle swarm optimisation; ant colony optimisation; correlation coeffieient; energy consumption Energy-efficient data gathering and aggregation for wireless sensor networks. Thesis Copyright material removed from digital thesis. See print copy in University of Adelaide Library for full text.open.access