Trust-based Cluster Head Selection with k-means Algorithm for Energy-efficient Wireless Sensor Networks

Problem Definition

In Wireless Sensor Networks (WSN), the selection of cluster heads plays a crucial role in optimizing network performance and efficiency. The traditional method of electing cluster heads, such as the LEACH protocol, has proven to be effective but comes with several limitations. One of the main drawbacks of the LEACH protocol is its random selection of cluster heads, leading to the possibility of the same node being repeatedly chosen as a cluster head. This can result in uneven energy distribution among nodes and potential premature depletion of energy resources, ultimately affecting the overall network lifetime. Moreover, the random selection method may also lead to the selection of cluster heads based on factors like distance or energy levels, rather than considering other important criteria.

As a result, there is a clear need for a novel approach that can address these limitations by effectively selecting cluster heads based on multiple factors to optimize network performance and prolong network lifetime.

Objective

The objective is to address the limitations of traditional cluster head selection methods in Wireless Sensor Networks by proposing a novel approach that considers multiple factors for electing cluster heads. The proposed method involves using a trust factor based on parameters like residual energy and distance between nodes, along with employing the k-means clustering algorithm. Additionally, the network architecture is optimized by dividing it into equal grids to distribute nodes evenly, reducing the load on cluster heads and extending network lifetime. Introducing a trust factor in the selection criteria also enhances network security by minimizing the involvement of malicious nodes in communication processes.

Proposed Work

In WSN, the concept of cluster head is added to reduce the communication complexity of the network. The CH is elected in order to represent the respective clusters. The role of the CH is to transmit the data from cluster nodes to the base station. But the election of the CH is a tedious task in itself. In traditional work, the LEACH performance was quite effective, but it does have various drawbacks.

The major drawback of using the LEACH protocol is that it elects the CH on a random basis. Thus, there is a possibility that the same node becomes CH again and again. Along with this, there is a feasibility that the node located at the farthest distance or the node with less energy becomes the CH. Electing the CH in this way can affect the network lifetime. Thus, there is a need to develop a novel approach that could elect the CH effectively by considering various important factors.

To propose a cluster head selection method based on a trust factor that ensures all nodes are trustworthy and authentic during communication. To calculate direct trust of nodes using parameters such as the residual energy and the distance between the nodes, along with the use of the k-means clustering algorithm. As in the traditional way, nodes are deployed in the environment without defining any particular areas. LEACH bears the responsibility of increasing the lifetime of network by reducing the energy consumption. Previously the nodes were arbitrarily distributed in the entire network and A cluster head possibly gets an uneven number of nodes that results in the high load over the cluster head and high usage of energy which in turn decreases the network lifetime, but in the proposed work, the network is separated into eight equal grids in which equal number of nodes are distributed.

It assists the network in creating the cluster heads according to the number of grids and the load on the cluster heads is also reduced as in each grid, equal number of nodes are present. Lesser load will consume less energy and thus, the network can live longer. We considered the first-order radio energy method for energy dissipation calculation in the proposed model for data communication operations such as transmission and reception process. Through this network architecture, the load on the CHs decreases which also reduces the energy consumption during communication. Thus, unlike the traditional approach, it increases the network lifetime.

Further, in proposed model trust factor have introduced which ensures the better performance of the network. Generally, the nodes in the network are selected as cluster heads on the basis of quality of service parameters such as distance from sink, energy consumption etc. These factors are computed for all nodes in network and according to the different approaches, the CHs are elected. Although, in conventional approach, security and trust factor is not taken into consideration in traditional techniques and this may lead to the selection of malicious nodes as the cluster head in the network. The malicious nodes eventually affects the performance of the network in terms of transmitting data from source to destination or to the sink.

However, introducing trust factor as another parameter for the selection criteria of the CHs, reduces the number of malicious nodes to be get involved in communication phase and provide immunity from attackers to the network.

Application Area for Industry

This project can be used across various industrial sectors such as smart manufacturing, agriculture, environmental monitoring, healthcare, and transportation. In smart manufacturing, the proposed solution can help in creating efficient communication networks for connecting different sensors and devices on the factory floor. By reducing the energy consumption and increasing network lifetime through the equitable distribution of nodes, the project can address the challenge of maintaining a reliable and sustainable communication infrastructure in the manufacturing sector. Similarly, in agriculture, the project can assist in optimizing irrigation systems, soil monitoring, and crop management by establishing robust WSN networks with reliable cluster heads. By incorporating trust factors into the selection criteria for cluster heads, the network can mitigate the risk of malicious nodes disrupting data transmission and ensure the integrity and security of the agricultural data.

Overall, the implementation of this project's proposed solutions can lead to enhanced operational efficiency, improved data reliability, and increased network longevity in various industrial domains.

Application Area for Academics

The proposed project on enhancing WSN by improving the election process of cluster heads can significantly enrich academic research, education, and training in the field of wireless sensor networks and data communication. This project introduces a novel approach to elect cluster heads effectively by considering important factors such as energy consumption, network load distribution, and trust factor. In academic research, this project opens up avenues for exploring innovative methods in WSN optimization and data transmission, particularly in enhancing network lifetime and security. Researchers can utilize the code and literature of this project to further investigate the impact of various factors on network performance and develop advanced algorithms for cluster head election. For education and training purposes, this project can be used to demonstrate the application of Kmean algorithm in WSN optimization and data communication.

MTech students and PhD scholars can utilize the project to deepen their understanding of network protocols and data transmission strategies in WSN environments. Future scope of this project includes the exploration of additional optimization techniques and the integration of machine learning algorithms for further enhancing network performance and security. This project has the potential to contribute significantly to the advancement of WSN research and educational applications.

Algorithms Used

Kmean is employed to distribute nodes in the network evenly among grids, reducing the load on cluster heads and increasing network lifetime. The introduction of the first-order radio energy method aids in calculating energy dissipation during data communication operations. Trust factor is incorporated to improve network performance by mitigating the risk of malicious nodes being elected as cluster heads, ensuring data transmission efficiency and security.

Keywords

SEO-optimized keywords: WSN, cluster head, communication complexity, network lifetime, energy consumption, LEACH protocol, CH election, data transmission, base station, network architecture, energy dissipation, data communication, trust factor, quality of service, security, malicious nodes, network performance, data aggregation, routing protocols, sensor node coordination, energy efficiency, grid-based clustering, network optimization, grid-based deployment, grid-based communication, resource allocation, network coverage, distributed systems, energy conservation.

SEO Tags

wireless sensor networks, clustering protocol, grid-based clustering, network coverage, energy efficiency, network optimization, data aggregation, routing protocols, sensor node coordination, distributed systems, grid-based deployment, grid-based communication, network performance, resource allocation, quality of service, energy conservation, CH election, LEACH protocol, network lifetime, trust factor, security measures, data transmission, energy dissipation, communication complexity, base station communication, malicious nodes, network security, research methodology.