Energy-efficient clustering algorithm for structured wireless sensor networks

• Published in: IET networks 2018-07, Vol.7 (4), p.265-272
• Main Authors: Padmanaban, Yuvaraj, Muthukumarasamy, Manimozhi
• Format: Article
• Language: English
• Subjects: average communication distance; CH role; CH‐to‐normal ratio; cluster heads; complete useful data percentage; confined clustered information; connectivity; effective clustering algorithm; effective data gathering; energy conservation; energy‐efficient structured clustering algorithm; environmental factors; environmental monitoring fields; information collection centres; lingering energy; load balancing; low‐energy adaptive clustering hierarchy; Research Article; scalable energy‐efficient CH; speedy communication; structured wireless sensor networks; wireless communication; wireless sensor networks
• ISSN: 2047-4954; 2047-4962; 2047-4962
• DOI: 10.1049/iet-net.2017.0112

Wireless communication is preferred in numerous sensing applications due to its convenience, cost-effectiveness, and flexibility. Modern sensors are versatile to sense the environmental factors and send them wirelessly. The information collection centres prefer to collect confined clustered information from a group of sensors rather than collecting them from individual sensors. Good connectivity, speedy communication, and effective data gathering can be ensured in the network when a good clustering algorithm is utilized. In this paper, a simple and effective clustering algorithm called energy efficient structured clustering algorithm (EESCA) is proposed for the environmental monitoring fields. Cluster heads (CHs) are elected based on average communication distance and lingering energy. Further, a new parameter called cluster head to normal ratio (CTNR) is introduced to rotate the cluster head role among the nodes. The performance evaluation is carried out in terms of first node die (FND), simulation time, scalability, load balancing, and a new parameter called complete useful data percentage (CUDP). Simulations are conducted for three different network scenarios. Results are compared with the renowned existing algorithms low energy adaptive clustering hierarchy (LEACH) and scalable energy efficient clustering hierarchy (SEECH) and it is proved that the proposed technique is beneficial for WSNs.