Enhanced ANFIS-GWO Methodology for Prolonging WSN Lifespan Using Cluster-Based Network Division and Intelligent Node Deployment

Problem Definition

From the literature review, it is evident that the existing traditional models in the field of IoT face several limitations and problems in the selection of Cluster Heads (CHs) among nodes. The random distribution of nodes in the traditional system leads to issues such as load imbalance, uneven energy consumption, and limited coverage area. Moreover, the reliance on node energy alone for CH selection overlooks important factors like communication distance and node coverage area, which are crucial for enhancing the network's lifespan. The use of a threshold matching technique in the traditional protocol further restricts flexibility in CH selection, leaving room for improvement. These identified limitations and pain points within the current IoT network models highlight the need for a more adaptive and efficient approach to selecting CHs.

By addressing the shortcomings of traditional techniques, a proposed solution could lead to improved energy efficiency, network performance, and overall longevity. The development of a technique that can dynamically adjust its selection strategy based on changing network conditions is essential to overcome the inadequacies of the existing protocols and maximize the benefits of IoT technology.

Objective

The objective is to develop a new approach for selecting Cluster Heads (CHs) in IoT networks that addresses the limitations of traditional models. This approach involves grid-based network division to improve balance and coverage, as well as the use of an ANFIS neuro-fuzzy system with the GWO algorithm for optimal CH selection. By dynamically adjusting the selection strategy based on changing network conditions, the goal is to enhance energy efficiency, network performance, and overall longevity in IoT environments.

Proposed Work

The proposed work aims to address the limitations of traditional cluster head selection techniques in IoT networks by introducing a new approach based on clustering. By deploying a grid-based network division, the proposed model will distribute nodes uniformly across different grids, improving network balance and coverage. This novel scheme will also help manage energy dissipation by employing a neuro-fuzzy system known as ANFIS in conjunction with the GWO algorithm for optimal CH selection. The ANFIS model will process various inputs such as residual energy, communication area, and distance from the base station to determine the best cluster heads for each grid. By combining these advanced techniques, the proposed work seeks to optimize energy consumption, increase network lifespan, and improve overall network performance in IoT environments.

Application Area for Industry

This project can find applications in various industrial sectors such as smart manufacturing, smart agriculture, smart cities, and healthcare. In the context of smart manufacturing, the proposed scheme can help in optimizing energy consumption and improving network lifespan within the factory environment. By efficiently selecting cluster heads based on residual energy, communication distance, and average node coverage area, the system can enhance the overall network performance and reduce energy wastage. In smart agriculture, the proposed model can assist in creating a more balanced distribution of network nodes, leading to improved monitoring and control of agricultural activities. Similarly, in smart cities and healthcare domains, the implementation of this solution can address challenges related to unbalanced energy consumption, network coverage, and CH selection, resulting in enhanced efficiency and reliability of IoT networks in these sectors.

Overall, the benefits of implementing these solutions include increased network lifespan, optimized energy usage, improved coverage area, and better adaptability to changing situations.

Application Area for Academics

The proposed project has the potential to significantly enrich academic research, education, and training in the field of IoT. By addressing the limitations of traditional clustering techniques, the proposed scheme offers a novel approach to improving energy consumption and network lifespan in IoT networks. This innovation can open up new avenues for research in network optimization, artificial intelligence algorithms, and data analysis within educational settings. Researchers studying IoT networks can benefit from the code and literature of this project to explore innovative methods for optimizing network clustering and improving energy efficiency. MTech students and PHD scholars can use the proposed algorithms, GWO and ANFIS, to enhance their research in network design and optimization.

The proposed scheme's emphasis on grid-based network division and intelligent CH selection can provide valuable insights for scholars working in the field of IoT network management. In the future, this project could be further developed to incorporate real-world datasets and conduct extensive simulations to validate its effectiveness. Additionally, exploring the applicability of the proposed scheme in different IoT applications such as smart cities, healthcare monitoring, and environmental monitoring could broaden its scope and impact. Overall, the proposed project holds great potential to advance academic research and education in IoT networks through innovative research methods, simulations, and data analysis.

Algorithms Used

The proposed technique in this project utilizes a combination of the Gray Wolf Optimization (GWO) algorithm and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The GWO algorithm is employed to select cluster heads in the network by optimizing the distribution of network nodes in separate grids to prevent congested deployment and manage excessive energy dissipation. On the other hand, the ANFIS algorithm processes multiple factors such as residual energy, communication area, and distance from the base station to generate optimal cluster head outcomes based on 27 predefined rules and membership functions. By utilizing these algorithms together, the project aims to improve the efficiency and accuracy of network deployment in unbalanced environments.

Keywords

SEO-optimized keywords: IoT energy consumption, network lifespan, CH selection techniques, traditional protocol, unbalanced energy consumption, network coverage area, communication distance, adaptive selection strategy, clustering scheme, grid-based network, network division, cluster heads, Neuro-fuzzy algorithm, ANFIS, Gray wolf optimization, GWO, residual energy, communication area, base station, wireless sensor networks, energy efficiency, node count, network lifetime, sensor nodes, network optimization, energy-aware routing, sensor network management, CH selection algorithms, optimization techniques, wireless communication systems, network efficiency, energy consumption optimization, network performance.

SEO Tags

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