GWO-Fuzzy Optimization for Energy-Efficient Communication in IoT-Enabled WSNs

Problem Definition

The current literature on energy-efficient routing protocols for networked sensors highlights several key limitations and problems that need to be addressed. One major challenge is the need for high energy consumption when passing sensitive data packets to the base station, due to the limited power resources of small sensors. Existing algorithms for routing protocols have focused on the grouping of nodes, cluster head (CH) selection, and data transfer to the base station through nodes. However, clustering algorithms such as K-means and Fuzzy C-Means may not always perform optimally when the number of clusters is not known beforehand, leading to potential performance issues. Additionally, existing CH selection models often prioritize factors such as residual energy and node distance, but fail to consider other key factors that can impact network performance.

As a result, there is a clear need for an efficient solution for energy-efficient clustering in wireless sensor networks to improve overall system performance and prolong the lifespan of networked sensors.

Objective

The objective is to design an efficient solution for energy-efficient clustering in wireless sensor networks to address the limitations of current routing protocols. This includes improving the grid formation process using the Grey Wolf Optimization (GWO) algorithm, developing an intelligent Fuzzy based decision model for cluster head (CH) selection, and introducing new factors such as distance of CH nodes with Sink, number of connected nodes, and Hamming distance between CHs. Additionally, the concept of IoT is incorporated into the proposed protocol by generating random data and utilizing the Thingspeak open IoT platform for data storage and retrieval. The overall goal is to optimize energy utilization in the network and improve system performance while prolonging the lifespan of networked sensors.

Proposed Work

As discussed in the problem formulation and gaps that the strategy of clustering and CH election in the traditional system has a scope of improvement. Therefore, in the proposed work, the grid formation is done by using the FCM clustering approach as the count of grids will be limited for the whole network and as the number of clusters will be more. Traditional FCM is replaced by a nature-inspired algorithm that is Grey Wolf Optimization (GWO) algorithm and once the clusters are formed an intelligent Fuzzy based decision model is designed and evaluated to decide which nodes will be CHs. This phase is dependent not only on residual energy and distance between nodes but new factors are also introduced in the selection criteria, the factors that are added to the proposed model along with residual energy are the distance of CH nodes with Sink, the number of connected nodes and Hamming distance between CHs. Along with this, the concept of IoT is also introduced in the proposed WSN protocol.

To demonstrate the concept of IoT based communication, random data is generated which is considered as the sensed data, after this the sensed data is sent to Thingspeak open IoT platform provided by Mathworks to store and retrieve data from things over the Internet. The proposed scheme is working into 4 phases as grid formation, cluster formation, CH selection, and data communication within the network to optimize the utilization of energy.

Application Area for Industry

This project can be applied in various industrial sectors where wireless sensor networks are used for monitoring and data collection, such as agriculture, healthcare, manufacturing, and environmental monitoring. The proposed solutions address challenges related to energy efficiency in networked sensors by introducing a more optimized grid formation using the Grey Wolf Optimization (GWO) algorithm, intelligent fuzzy-based decision models for cluster head selection, and incorporating new factors for better performance. By improving the efficiency of clustering and CH selection, industries can benefit from extended network lifetime, enhanced data transmission reliability, and overall cost savings in maintaining and managing sensor networks. Additionally, the integration of IoT concepts in the proposed WSN protocol allows for seamless communication and data storage using open IoT platforms, enabling industries to leverage the power of the Internet for data analysis and decision-making.

Application Area for Academics

The proposed project can enrich academic research, education, and training by providing a novel approach to energy-efficient clustering in wireless sensor networks. By addressing the limitations of existing clustering and cluster head selection methods, the project opens up new avenues for research in the field of IoT-based communication and optimization of energy utilization. Researchers, MTech students, and PHD scholars in the domain of wireless sensor networks can benefit from the code and literature of this project to explore innovative research methods, simulations, and data analysis within educational settings. The utilization of algorithms such as Grey Wolf Optimization, Fuzzy C-Means, and Fuzzy logic can offer a deeper understanding of the network dynamics and help in developing more efficient clustering protocols. The relevance of this project lies in its potential applications for improving the performance of networked sensors with limited power resources.

By incorporating nature-inspired algorithms and advanced decision models, the project demonstrates an interdisciplinary approach that can be leveraged by researchers across various fields. In the future, the scope of this project could be expanded to explore the integration of other optimization techniques, machine learning algorithms, or communication protocols to further enhance the efficiency and scalability of wireless sensor networks. The findings of this research can pave the way for developing more robust and reliable systems in the era of IoT and smart technologies.

Algorithms Used

GWO algorithm: The Grey Wolf Optimization (GWO) algorithm is used to optimize the cluster formation process in the proposed work. It helps in finding the optimal number of clusters for the network by mimicking the social behavior of grey wolves. FCM algorithm: The Fuzzy C-Means (FCM) algorithm is utilized for grid formation in the proposed work. It helps in assigning network nodes to clusters based on their similarity, taking into account factors such as residual energy and distance between nodes. Fuzzy logic: A Fuzzy Logic decision model is employed for CH selection in the proposed work.

It considers additional factors such as distance of CH nodes with the sink, the number of connected nodes, and Hamming distance between CHs to intelligently determine the CH nodes in the network. Overall, the combination of these algorithms plays a crucial role in improving the energy efficiency and performance of the wireless sensor network by optimizing cluster formation, CH selection, and data communication processes.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, Clustering Protocol, Energy Efficiency, Grey Wolf Optimization (GWO), Fuzzy Inference System, Cluster Head (CH) Selection, Grid Formation, Grid Head Selection, Network Setup, Residual Energy, Distance to the Sink, Connection to Nodes, Hamming Distance, Nature-Inspired Algorithms, Energy Optimization, Sensor Nodes, Network Performance, Wireless Communication, Sensor Network Management, Optimization Techniques, CH Selection Algorithms, Energy-Aware Routing, Wireless Communication Systems, Network Efficiency, Network Optimization, Energy Consumption Optimization

SEO Tags

Wireless Sensor Networks, Clustering Protocol, Energy Efficiency, Grey Wolf Optimization, Fuzzy Inference System, Cluster Head Selection, Grid Formation, Nature-Inspired Algorithms, Sensor Nodes, Energy Optimization, Network Performance, Wireless Communication, Optimization Techniques, Energy-Aware Routing, Sensor Network Management, CH Selection Algorithms, Network Efficiency, Network Optimization, Energy Consumption Optimization