Balancing Energy Loads in Wireless Sensor Networks through Uniformly Quantized Energy Levels-Based Clustering

Abstract

Clustering is considered a common and an effective method to prolong the lifetime of a wireless sensor network. This paper provides a new insight into the cluster formation process based on uniformly quantizing the residual energy of the sensor nodes. The unified simulation framework provided herein, not only aids to reveal an optimum number of clusters but also the required number of quantization levels to maximize the network's lifetime by improving energy load balancing for both homogeneous and heterogeneous sensor networks. The provided simulation results clearly show that the uniformly quantized energy level-based clustering provides improved load balancing and hence, a longer network lifetime than existing methods.Keywords-Clustering, energy load balancing, network lifetime, uniform quantization, wireless sensor network.

Energy Load Balancing for Fixed Clustering in Wireless Sensor Networks

“…Through extensive simulations we found that the optimal number of fixed clusters is between 3−6 which is somewhat similar to the case of dynamic clustering approach [11]. It should be noted that a few number of clusters increase intra-cluster communication overhead due to an increased distance between the sensor nodes and can result in poor performance, whereas, a large number of clusters increase the distance between the BS and the far away cluster heads which again can degrade the performance of the system.…”

“…4 exhibits simulated lifetime curves for the proposed scheme with five clusters for various quantization levels (8,16, 32 and 64) and packet sizes (64, 128 and 256 bytes). The results are compared with dynamic cluster formation method as proposed by authors in [11] for the same packet sizes with 64 quantization levels. Since both methods use uniformly quantized energy levels for cluster head selection, the provided plots can be compared fairly to highlight the difference between fixed and dynamic clustering methods [11].…”

“…The results are compared with dynamic cluster formation method as proposed by authors in [11] for the same packet sizes with 64 quantization levels. Since both methods use uniformly quantized energy levels for cluster head selection, the provided plots can be compared fairly to highlight the difference between fixed and dynamic clustering methods [11]. As expected, it is evident from the plots that the dynamic clustering outperforms the proposed fixed clustering method by around 14% (64 byte packet).…”

“…Moreover, this work also addresses the issues related not only to maximize the network lifetime but also to achieve energy load balancing. The novelty of this work is that it introduces a new centralized two-phased protocol to first obtain energy balanced clusters and then, to enable the sensor nodes within each fixed cluster share their residual energy information as part of their transmitted packets to the cluster head in the form of a uniformly quantized energy level as suggested in [11]. This sharing of quantized energy information enables us to reach near optimum energy load balancing which leads to a prolonged system lifetime.…”

Situation Awareness Measurement Enhanced for Efficient Monitoring in the Internet of Things

“…The remaining power was used because it is a popular metric in extensions to LEACH and other cluster selection algorithms to increase the lifetime of a network. Rotating the clusterhead role amongst the high energy nodes has the effect of balancing the load [4].…”