Clustering Wireless Sensor Network

Clustering Wireless Sensor Network: A Framework-based PSO

There are three essential aspects of ad hoc mobile wireless networks in general and sensor networks in particular; location management, energy management, and topology management. Energy saving in WSN is a critical problem for the diversity of applications. Data aggregation between sensor nodes is massive unless a suitable sensor data flow management is adopted. Clustering the sensor nodes is considered a practical solution to this problem. Each cluster should have a controller denoted as a Cluster Head (CH) and many nodes within its supervision area. Clustering demonstrated a practical result in forming the network into a linked hierarchy. Thus, balancing the load distribution in WSN to make efficient use of the available energy sources and reducing traffic transmission can be achieved. On solving this problem, we need to find the optimal distribution of sensors and CHs; thus, we can increase the network lifetime while minimizing the energy consumption. Currently, we are exploring various search algorithms to solve this challenging problem. We published many research articles in this promising area of research-based Particle Swarm Optimization and Differential Evolution [1-3]. Recently, we released a book chapter in the Springer Encyclopedia of Wireless Networks [4].

Publications

1. B. Solaiman and A. Sheta, “Computational intelligence for wireless sensor networks: Applications and clustering algorithms,” International Journal of Computer Applications, vol. 73, no. 15, pp. 1–8, 2013.

2. B. Solaiman and A. Sheta, “Evolving a hybrid k-means clustering algorithm for wireless sensor network using PSO and GA,” International Journal of Computer Science Issues, vol. 12, no. 1, pp. 23–32, 2015.

3. B. Solaiman and A. Sheta, “Energy optimization in wireless sensor networks using a hybrid K-Means PSO clustering algorithm," Turkish Journal of Electric Engineering and Computer Science, 2016.

4. A. Sheta and B. Soliman, “Extending WSN lifetime based on evolutionary clustering algorithm,” in Encyclopedia of Wireless Networks(X. L. Sherman Shen and K. Zhang, eds.), pp. 1–9, Springer International Publishing, 2018