Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks

Abstract

Since nodes in a sensor network have limited energy, prolonging the network lifetime and improving scalability become important. In this paper, we propose a distributed weight-based energy-efficient hierarchical clustering protocol (DWEHC). Each node first locates its neighbors (in its enclosure region), then calculates its weight which is based on its residual energy and distance to its neighbors. The largest weight node in a neighborhood may become a clusterhead. Neighboring nodes will then join the clusterhead hierarchy. The clustering process terminates in O(1) iterations, and does not depend on network topology or size. Simulations show that DWEHC clusters have good performance characteristics.

Comparative Analysis of Clustering Protocols for Wireless Sensor Networks

“…DWEHC, given by Ding et al [5], is a distributed clustering algorithm that provide improvement to HEED by building balanced cluster sizes and optimize the intra-cluster topology using location awareness of the nodes. DWEHC include no assumption about network size and density, and consider residual energy in the act of CH election as in HEED.…”

WSN Performance Issues and Various Clustering Methods

“…Paper [27] mentions DWEHC method to overcome drawbacks of HEED algorithm. This method creates an equilibrium cluster size for best intra clustering formation.…”

CDEEC: A connectivity Degree-based energy efficient clustering protocol for wireless sensor networks

“…Here, we review some of the protocols proposed in the literature for each class: PEGASIS [18], DWEHC [21], and TASC [22] in the deterministic class, and EEHC [20], EECS [19], and HEED [8] in the probabilistic class.…”

“…Distributed Weight-Based Energy-Efficient Hierarchical Clustering (DWEHC) [21] is another deterministic clustering protocol, which aims at high energy efficiency by generating balanced cluster sizes and optimizing the intra-cluster topology. To become a CH, in DWEHC, each sensor node calculates its weight after exchanging its geographic location with its neighbors.…”

“…Similar to the idea in [21], Topology Adaptive Spatial Clustering (TASC) [22] decomposes large non-uniform networks into smaller locally-uniform clusters. TASC assumes nodes are aware of 2-hop neighborhood information and also that they know the distance to their neighborhood.…”