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Mrs.P.Lavanya, Brinda Reddy Kuncharam, Madugula Sai Krishna Reddy, Yuva Ram Potu
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Page No: 1 - 16
Abstract : Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols.
Keyword Wireless Sensor Networks (WSNs), Artificial Bee Colony (ABC) Algorithm, Intra-Cluster Communication
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Abstract

Clustering sensor nodes is a smart way to design energy-efficient routing for Wireless Sensor Networks (WSNs). It helps improve network lifespan and reduces power consumption. However, cluster heads perform extra tasks, which drains their energy faster. Choosing the right cluster heads is vital.

This study introduces a clustering algorithm based on an improved Artificial Bee Colony (ABC) method. It considers energy, location, and density to select optimal cluster heads. During the setup phase, all nodes begin with the same energy. The improved ABC algorithm works with Fuzzy C-Means (FCM) to find the best clusters.

For communication between clusters, we use a modified Ant Colony Optimization (ACO) routing algorithm. To save even more energy, a polling control system manages communication inside each cluster. It activates only idle nodes to avoid wasting energy.

Simulations show that our method outperforms current protocols. It uses energy better and increases overall performance.

Keywords: Wireless Sensor Networks, Cluster Head Selection, ABC Algorithm, ACO Routing, Energy Efficiency, Intra-Cluster Communication.

Introduction

Wireless Sensor Networks (WSNs) are used in many areas like environment monitoring, healthcare, military, and industry. A WSN is made of small, low-power sensors that connect wirelessly. These sensors collect data and send it to a base station.

WSNs face a major issue—limited energy. Replacing batteries in large networks is not practical. When sensors run out of power, the network weakens or even fails.

To fix this, clustering is used. Sensors are grouped into clusters. Each cluster has a cluster head that collects and forwards data. But these cluster heads use more energy. That’s why choosing them wisely is key.

We propose a new method that combines two advanced techniques:

  1. An improved ABC algorithm to pick energy-efficient cluster heads.
  2. A better ACO algorithm for routing between them.

We also add a polling system that checks which nodes are idle or busy. It only wakes up nodes when needed, saving more power.

This setup improves data transmission, saves energy, and makes the network last longer.

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