Abstract
In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.
Highlights
Wireless Sensor Networks (WSN) are applied in many fields such as in health-care, environmental sensing, and industrial monitoring [1,2,3,4]
The comparison is based on the metric of energy balancing and network lifetime in terms of First Node Dead (FND), 10PND, Quarter of Nodes Dead (QND), and Half of Nodes Dead (HND)
To achieve the best possible results of energy-efficient routing protocols in WSN, it is recommended to utilize every parameter having an effect on the energy efficiency of the WSN routing protocol
Summary
Wireless Sensor Networks (WSN) are applied in many fields such as in health-care, environmental sensing, and industrial monitoring [1,2,3,4]. Several energy-efficient routing protocols were proposed to address this issue They all adopt the idea of clustering or chaining sensor nodes so the transmission to the BS occurs in multi-hops. Many factors affect the clustering algorithms in WSN, for example the remaining energy in the sensor nodes and the distances from their BS. We introduce a fuzzy-based centralized clustering technique for energy-efficient routing protocols in WSN. Separation distance is calculated adaptively based on the number of remaining live nodes, the dimensions of the area covered by these nodes, and the percentage of the desired CHs. The proposed fuzzy model uses five parameters to prioritize opportunities of sensor nodes’ CH election.
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