Optimizing Wireless Sensor Network Lifespan through Innovative CH Selection and Data Compression Algorithms

Problem Definition

The current state of wireless sensor networks reveals a critical need for an energy-efficient protocol that can optimize the performance and longevity of network nodes. Existing research has identified a number of shortcomings in the current energy-efficient protocols, including limitations in the selection of Cluster heads (CHs), slow convergence rates of algorithms, and a heavy reliance on infrastructure-based measures rather than addressing issues at the data layer. This lack of comprehensive solutions has led to inefficiencies in energy consumption and network performance, ultimately hindering the overall effectiveness of wireless sensor networks. The primary challenge lies in developing a protocol that not only reduces energy consumption but also addresses key limitations present in the current systems. By focusing on selecting CHs based on a broader range of parameters, improving convergence rates of algorithms, and exploring energy-efficient strategies at the data layer, the goal is to provide a more effective and sustainable approach to enhancing energy efficiency in wireless sensor networks.

Addressing these limitations and pain points within the existing protocol framework will be crucial in laying the foundation for a more robust and efficient system moving forward.

Objective

The objective of this project is to develop an improved clustering protocol for wireless sensor networks that focuses on reducing energy consumption of sensor nodes and increasing network lifespan. This will be achieved through a novel method that combines a chaotic mapping algorithm and Yellow Saddle Goatfish Algorithm for Cluster Head selection. Additionally, a data compression technique using Huffman algorithm at the data layer will further reduce energy consumption by compressing data before transmission. The goal is to provide a comprehensive solution to the limitations of current energy efficient protocols and improve network performance and efficiency.

Proposed Work

In order to address the research gap identified in the literature survey regarding the limitations of current energy efficient protocols in wireless sensor networks, a new approach is proposed in this project. The main objective is to develop an improved clustering protocol that focuses on reducing energy consumption of sensor nodes and increasing the network lifespan. To achieve this goal, a novel method combining chaotic mapping algorithm and Yellow Saddle Goatfish Algorithm (YSGA) is proposed for CH selection. The chaotic map algorithm was chosen for its ability to handle complex and noisy data, while YSGA was selected for its balanced exploration and exploitation phases. By combining these two algorithms, the proposed model aims to enhance global searching ability, network stability, and efficiency in CH selection.

Furthermore, in addition to the clustering approach, a data compression technique using Huffman algorithm is implemented at the data layer to further reduce energy consumption. The concept behind this technique is to compress the data collected by sensor nodes before transmitting it to the sink node. By assigning variable-length codes based on the frequency of characters, the data is compressed efficiently, reducing the energy usage of nodes during transmission. Overall, the proposed hybrid YSGA and chaotic model offers a comprehensive solution to the limitations of current energy efficient protocols, with the potential to significantly improve network performance and lifespan.

Application Area for Industry

This project can be applied in various industrial sectors such as agriculture, environmental monitoring, smart cities, healthcare, and manufacturing. In agriculture, the proposed energy-efficient protocol can help in monitoring soil conditions, crop health, and irrigation systems using wireless sensor networks. For environmental monitoring, the system can assist in tracking air quality, water quality, and weather conditions. In smart cities, the protocol can be utilized for smart parking systems, waste management, and energy-efficient street lighting. In healthcare, it can help in monitoring patients remotely and tracking vital signs.

Lastly, in the manufacturing sector, the protocol can be used for monitoring equipment health, optimizing production processes, and ensuring worker safety. The project's proposed solutions address the specific challenges faced by these industries, such as the need for energy efficiency, data transmission reliability, and network stability. By implementing the YSGA and chaotic map algorithm for CH selection, the network can achieve better energy consumption management, leading to increased network lifespan and stability. Additionally, the data compression technique using the Huffman algorithm at the data layer helps in reducing the amount of data transmitted, thus conserving energy and improving overall network efficiency. Overall, the benefits of implementing these solutions include improved energy efficiency, enhanced network lifespan, increased data transmission reliability, and optimized performance across various industrial domains.

Application Area for Academics

The proposed project can greatly enrich academic research, education, and training in the field of wireless sensor networks and energy efficiency. By addressing the current limitations in existing energy efficient protocols, the project introduces a novel clustering and CH selection method based on chaotic maps and Yellow Saddle Goatfish Algorithm (YSGA). This not only enhances the global searching ability of the algorithm but also improves network stability and lifespan. The use of non-repetitive nature of chaotic maps allows for faster convergence to optimal solutions, while the balancing between exploration and exploitation phases of YSGA ensures efficient energy consumption by sensor nodes. The implementation of a data compression technique at the data layer further reduces energy usage by compressing data before transmission using the Huffman algorithm.

Researchers, MTech students, and PhD scholars in the field of wireless sensor networks can benefit from the code and literature of this project for studying innovative research methods, simulations, and data analysis. The combination of YSGA and chaotic mapping algorithms provides a new approach for reducing energy consumption in wireless networks and can be applied to various research domains requiring efficient energy utilization. For future scope, the project could potentially be extended to include machine learning algorithms for even more sophisticated energy efficiency solutions. Additionally, the implementation of the proposed model in real-world scenarios can provide valuable insights for further advancements in energy-efficient protocols for wireless sensor networks.

Algorithms Used

The Combined Chaotic Maps based Yellow Saddle Goatfish Algorithm (YSGA) is utilized in the proposed work for clustering and cluster head (CH) selection in wireless sensor networks. This algorithm aims to reduce energy consumption of sensor nodes, thus enhancing the network lifespan. The YSGA enhances global searching ability, network stability, and lifespan, while the chaotic map algorithm helps in dealing with complex and noisy data, enabling faster search for optimal solutions. Huffman Encoding is applied for data compression at the data layer in the proposed model. This lossless compression technique assigns variable-length codes to input characters based on their frequency in the data.

By compressing data before transmitting it to the sink node, the energy usage of nodes is significantly reduced, ultimately prolonging the network lifespan.

Keywords

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SEO Tags

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