Enhancing Energy Efficiency in Clustering Protocols with Gray Wolf Optimization Algorithm

Problem Definition

The energy consumption of wireless IoT sensor systems is a significant challenge that needs to be addressed in order to improve efficiency and sustainability. The current use of Clustering Protocols for data transmission is leading to excessive energy usage, which can have detrimental effects on the overall system performance. Additionally, the outdated optimization algorithms like ESU and PSO are exacerbating the problem by getting stuck in local optima and increasing system complexity, ultimately resulting in suboptimal results. This highlights the urgent need for a more efficient and effective approach to managing energy consumption in wireless IoT sensor systems, as well as the utilization of modern and optimized algorithms to improve overall system performance. By addressing these key limitations and pain points, we can strive towards creating more energy-efficient and sustainable wireless IoT sensor systems for better functionality and performance.

Objective

The objective of this project is to address the energy consumption challenges in wireless IoT sensor systems by enhancing the efficiency of Clustering Protocols. This will be achieved by incorporating communication distance and energy considerations in selecting cluster heads. Furthermore, the use of the Gray Wolf Optimization (GWO) algorithm will replace outdated optimization algorithms like ESU and PSO to improve system performance. By focusing on energy efficiency and optimized algorithm selection, the goal is to achieve higher throughput, packet delivery ratio, and reduced energy usage in wireless IoT systems. The use of MATLAB software will facilitate the implementation of these advanced techniques and analysis of system data, ultimately aiming to create a more sustainable and high-performance wireless IoT system.

Proposed Work

The proposed work aims to tackle the energy consumption challenges in wireless IoT sensor systems by focusing on enhancing the efficiency of Plustering Protocols. By introducing the concept of communication distance along with energy in selecting cluster heads, the research seeks to improve energy efficiency in the network. Furthermore, to address the limitations of existing optimization algorithms like ESU and PSO, the Gray Wolf Optimization (GWO) algorithm will be utilized. By evaluating the cost function and selecting the best cluster head within the cluster, the system aims to achieve higher performance in terms of throughput, packet delivery ratio, and energy usage. Implementing a systematic approach to address these issues will lead to optimized results and a more sustainable wireless IoT system.

The rationale behind choosing the specific techniques and algorithms lies in their ability to address the identified gaps in the current wireless IoT systems. By focusing on energy efficiency through communication distance and cluster head selection, the proposed approach aims to directly target the main problem of high energy consumption. Furthermore, by replacing outdated optimization algorithms with GWO, the research aims to overcome the limitations of getting stuck in local optima and increasing system complexity. MATLAB has been chosen as the software for this project due to its robust capabilities in implementing complex algorithms and analyzing data. By combining these elements, the proposed work sets out to achieve a more sustainable and high-performance wireless IoT system.

Application Area for Industry

This project can be applied across various industrial sectors that rely on wireless IoT sensor systems for data collection and transmission, such as manufacturing, agriculture, healthcare, and transportation. By introducing the concept of communication distance in addition to energy in the selection of cluster heads, the proposed solutions aim to significantly improve energy efficiency in these systems. The use of the Gray Wolf Optimization (GWO) algorithm, instead of outdated methods like ESU and PSO, addresses the challenge of local optima and system complexity, leading to more optimal results. Implementing these solutions can result in reduced energy consumption, improved system performance, and overall cost savings for industries utilizing wireless IoT sensor systems.

Application Area for Academics

The proposed project has the potential to significantly enrich academic research, education, and training in the field of Wireless IoT sensor systems. By addressing the energy consumption challenges associated with current systems, the research opens up new avenues for exploration and development. The introduction of the Gray Wolf Optimization (GWO) algorithm as a more efficient alternative to ESU and PSO algorithms offers researchers, MTech students, and PHD scholars the opportunity to explore innovative research methods and data analysis techniques within educational settings. The relevance of this project lies in its application areas, where energy efficiency and optimization play a crucial role in the performance of wireless IoT sensor systems. By incorporating the concept of communication distance in addition to energy considerations for selecting cluster heads, the system aims to achieve improved efficiency and performance.

The use of MATLAB software for implementing the GWO algorithm also provides a practical platform for researchers and students to experiment with simulations and data analysis in real-world scenarios. Researchers and students in the field of Wireless IoT sensor systems can leverage the code and literature generated by this project to enhance their own research endeavors. The GWO algorithm's ability to optimize cost functions based on energy and communication distance factors can be applied to various research domains within the field. By incorporating this algorithm into their work, researchers can strive to achieve better energy efficiency and performance in wireless sensor systems. Moving forward, the future scope of this project includes the potential for further optimization and refinement of the GWO algorithm, as well as the exploration of additional applications and use cases within the Wireless IoT sensor systems domain.

By continuing to innovate and develop new methodologies, researchers and students can contribute to advancements in the field and drive progress in academic research, education, and training.

Algorithms Used

The Gray Wolf Optimization (GWO) algorithm plays a crucial role in the proposed work for optimizing the Wireless IoT, WSM IoT system. By considering factors such as energy and communication distance, the GWO algorithm helps in selecting the best cluster head within the network to improve energy efficiency and overall system performance. Using MATLAB software, this algorithm enhances accuracy and efficiency by identifying optimal application areas and evaluating the cost function to make data-driven decisions for cluster head selection.

Keywords

energy consumption, wireless IoT sensor systems, Clustering Protocols, optimal application areas, WSM IoT system, communication distance, cluster heads, Gray Wolf Optimization, GWO algorithm, node selection, cost function, MATLAB, Smart Agriculture, Smart Buildings, Intelligent Transportation, Smart Medical Healthcare Systems, Sensor Deployment

SEO Tags

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