Energy efficient data gathering using spanning trees for wireless sensor networks
LE3 .A278 2008
Master of Science
With the advancement of micro-sensor and radio technology, wireless sensor networks (WSNs) are deployed in various applications. In a continuous monitoring application, sensors gather information and transmit the sensed data to the base station in a periodic manner. In each data gathering round, a node generates a data packet and transmits it to the base station, or to another node; the data packets received from neighboring nodes can be aggregated. The lifetime of the WSN is defined as the time (rounds) until the base station receives data from all sensors in the network. In a data gathering round, the single best routing tree consumes lowest energy from all nodes but assigns more load to some sensors. As a result, the energy resources of the heavily loaded nodes are depleted earlier than others, which reduces network lifetime. This thesis proposes two multi-hop routing algorithms for a homogeneous network to maximize the network lifetime. The first proposed algorithm uses a greedy approach (Energy Efficient Spanning Tree, EESR) while the other uses a genetic algorithm (GA). The proposed algorithms generate balanced and energy efficient data aggregation trees for WSNs. The simulation results show that the proposed algorithms outperform the traditional data aggregation algorithms in terms of extending network lifetime. Moreover, the results show that the greedy approach (EESR) performs better when the base station is placed outside and genetic approach performs better when the base station is placed inside the network field.
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