Improving the lifetime of two-tiered sensor networks using genetic algorithm

Date of Award


Publication Type

Master Thesis


Computer Science



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


Wireless sensor networks have been envisioned to have a wide range of applications which consist of many inexpensive and low-powered wireless nodes which are used to sense, gather, and transmit the data towards the base station. In Two-Tiered wireless sensor networks, nodes are grouped into clusters, with a minimum of one cluster-head to distribute the work load among the member nodes. In the recent years, higher-powered relay nodes have been proposed to act as cluster heads and these relay nodes form a network among themselves in order to improve the lifetime of the sensor networks. Since the nodes are generally energy constrained, efficient management of the network data communication scheme can maximize the lifetime of the networks. A Genetic Algorithm is the technique for randomized search and optimization which is based on Darwin's Principal of Natural Selection. In this paper, we have proposed a Genetic Algorithm based solution for scheduling the data gathering of relay nodes that can significantly extend the lifetime of the relay node network. We have simulated our method on 15 different sizes of networks and measured the lifetime of the network as the number of rounds, until the first relay node runs out of battery power. For smaller networks, where the global optimum can be determined, our genetic algorithm based approach is always able to find the optimal solution with a lesser program run-time. For larger networks, we have compared our approach with traditional routing schemes and shown that our method leads to significant improvements.