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Development of a Modified Energy-Efficient Clustering with Splitting and Merging for Wireless Sensor Networks Using Cluster-Head Handover Mechanism

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Development of a Modified Energy-Efficient Clustering with Splitting and Merging for Wireless Sensor Networks Using Cluster-Head Handover Mechanism.


Energy efficiency is one of the most important challenges for Wireless Sensor Networks (WSNs). This is due to the fact that sensor nodes have limited energy capacity. Therefore, the energy of sensor nodes has to be efficiently managed to provide longer lifetime for the network. To reduce energy consumption in WSNs, a Modified Energy Efficient Clustering with Splitting and Merging (𝑚𝐸𝐸𝐶𝑆𝑀) for WSNs using Cluster-Head Handover Mechanism was implemented in this research.

This model used information of the residual energy of sensor nodes and a suitable Cluster Head (CH) handover threshold to minimize energy consumption in the network. A backup CH was incorporated into the model to take over the responsibilities of the CH once the CH handover threshold is reached. The energy consumed by node’s amplifier was varied with its transmission distance.

Average improvements of 7.5% and 50.7% were achieved for the network lifetime and residual energy ratio respectively which indicate a significant reduction in energy consumption of the network nodes. Also, scalability and robustness test were carried out with respect to network lifetime by randomly adding and removing a number of nodes from the network. Average improvements of 7.8%, and 10.3% were achieved for scalability and robustness test respectively.


Wireless sensor networks have recently emerged as an important means to study and interact with the physical world. The recent technological advances have made it possible to deploy small, low power, low-bandwidth, and multi-functional wireless sensor nodes to monitor and report conditions and events in their local environments (Liu & Ning, 2007; Sabet & Naji, 2015).

A large collection of these sensor nodes can thus form a wireless sensor network in an ad-hoc manner, creating new types of information systems (Liu & Ning, 2007). A sensor network is a network of large number of tiny sensor nodes and a few powerful control nodes (also called base stations). Sensor nodes are usually deployed randomly in the field and form a sensor network in an ad-hoc manner to fulfill certain tasks.

There is usually no infrastructure support for sensor networks (Liu & Ning, 2007; Razaque et al., 2016). Every sensor node in a sensor network has one or a few sensing components to sense conditions (such as temperature, humidity, pressure, etc.) from its immediate surroundings, a processing component to carry out simple computation on the raw data, and a communication component to communicate with its neighbor nodes (Akyildiz et al., 2002; Tahir et al., 2013).

Instead of sending raw data to the control nodes responsible for data fusion, sensor nodes are fitted with an on-board processor with processing abilities. This enables sensor nodes to locally carry out simple computations and transmit only the required partially processed data to the control nodes. The control nodes may further process the data collected from the sensor nodes, disseminate control commands to the sensor nodes, and connect the network to a traditional wired network.


Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393-422.

Ari, A., Abba, A., Gueroui, A., Labraoui, N., & Yenke, B. O. (2015). Concepts and evolution of research in the field of wireless sensor networks. International Journal of Computer Networks & Communications (IJCNC), 7(1), 81 – 98. doi: 10.5121/ijcnc.2015.7106

Camilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. Paper presented at the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence,. 49-59

Castagnetti, A., Pegatoquet, A., Le, T. N., & Auguin, M. (2014). A joint duty-cycle and transmission power management for energy harvesting WSN. Ieee Transactions On Industrial Informatics, 10(2), 928-936.

Dahnil, D. P., Singh, Y. P., & Ho, C. K. (2012). Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks. IET Wireless Sensor Systems, 2(4), 318-327.

Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(1), 1-38.

CSN Team.

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