A Fuzzy Inference System Model with STSA Optimization for Energy-Efficient WSN

Problem Definition

The current state of wireless sensor networks (WSNs) is facing challenges in terms of clustering and cluster head (CH) selection, ultimately impacting the network's lifespan. Existing literature reveals that while numerous approaches have been proposed to enhance WSN lifespan, the high energy consumption in CHs is a major concern as they are responsible for collecting data from nodes and transmitting it to the sink node. This inefficiency leads to a shortened network lifespan. Moreover, researchers have predominantly focused on limited quality of service parameters when selecting CHs, neglecting other crucial parameters that could optimize CH selection in the network. Additionally, the lack of determination of the sink node's location in traditional WSN models further contributes to network instability.

As a result, traditional methods exhibit limitations in clustering and CH selection, resulting in increased energy consumption and decreased network lifetime. These shortcomings underscore the urgent need for the development of a new and improved method that effectively selects CHs to enhance the lifespan of wireless sensor networks.

Objective

To develop an efficient clustering and routing protocol using a fuzzy inference system (FIS) to address the challenges faced by traditional wireless sensor network (WSN) approaches in clustering and cluster head (CH) selection. The objective is to reduce energy consumption in CH nodes, improve network stability, and increase network lifespan by considering important quality of service parameters for CH selection. The proposed method incorporates fuzzy logic and nature-inspired optimization algorithms to enhance decision-making and maximize network performance.

Proposed Work

In this research, an improved and highly efficient clustering and routing protocol is proposed for tackling the limitations of the traditional approaches and prolonging the stability and lifespan of the network. The proposed model is based on fuzzy inference system (FIS) in which four important parameters are taken into consideration for determining the CH in the network. The main motive of the current research is to reduce the energy usage in CH nodes which in turn leads to enhanced and stable network with increased lifespan. To accomplish this, initially, an FCM (fuzzy c-means) technique is used in the proposed work for forming the grids in the network and then the CH is selected by using the fitness value of FCM approach. After that, a nature inspired optimization algorithm named as, STSA (sine tree seed algorithm) is used in order to form clusters in the current WSN network.

Furthermore, as described earlier that the majority of the traditional models utilized only few parameters for determining the CH in the network. However, there are number of QoS parameters that should be considered before selecting the CH in the network. Keeping this in mind, a fuzzy based approach is proposed in the proposed work in which some important QoS parameters like the residual energy of the nodes, required energy, communication area and location of base node or sink node serve as the inputs to the proposed fuzzy system which are processed as per the defined rules to generate a single output that determines whether that node is capable for being the CH in the network or not. One of the main motivations for employing fuzzy logic in the proposed study is that it improves the model's decision-making capabilities while consuming less power. Fuzzy set theory has been utilized in WSNs in order to enhance the decision-making, lower resource usage and improve results of models.

Application Area for Industry

This project can be highly beneficial in various industrial sectors such as agriculture, environmental monitoring, smart cities, healthcare, and manufacturing. In agriculture, for example, the proposed solutions can help in optimizing irrigation systems by efficiently monitoring soil moisture levels and weather conditions, leading to water conservation and increased crop yield. In environmental monitoring, the project can aid in detecting pollution levels and managing natural resources effectively. Healthcare facilities can use the solutions to monitor patient health and automate processes for better patient care. Additionally, in manufacturing, the project can assist in improving efficiency by monitoring production processes and reducing downtime.

The challenges that industries face, such as high energy consumption, limited quality of service parameters, and lack of stability in network systems, can be effectively addressed by implementing the proposed clustering and CH selection solutions. By using FIS and fuzzy set theory, the project aims to optimize energy usage in wireless sensor networks, enhance decision-making capabilities, and improve the overall performance and lifespan of the network. The application of STSA for cluster formation and consideration of important QoS parameters for CH selection will result in a more stable and efficient network, benefiting various industrial domains by reducing energy consumption, improving resource management, and ensuring reliable and long-lasting network operations.

Application Area for Academics

The proposed research project on clustering and CH selection in wireless sensor networks has the potential to enrich academic research in the field of networking and communication systems. By introducing a new and improved method based on fuzzy logic and optimization algorithms, the project addresses the limitations of traditional approaches and aims to enhance the stability and lifespan of wireless networks. The relevance of this project lies in its focus on reducing energy consumption in CH nodes, which ultimately leads to a more stable network with a longer lifespan. This can benefit academic research by providing a novel solution to a pressing issue in the field of wireless sensor networks. In terms of education and training, the proposed project can serve as a valuable resource for students pursuing degrees in networking, communication systems, or related fields.

By studying and implementing the algorithms and methodologies proposed in this research, students can gain hands-on experience in developing innovative solutions for real-world problems in wireless networks. The potential applications of this project in pursuing innovative research methods, simulations, and data analysis within educational settings are vast. Researchers, MTech students, and PhD scholars can leverage the code and literature of this project to further explore the impact of clustering and CH selection on network lifespan and stability. The use of fuzzy logic and optimization algorithms opens up new avenues for research in the field, allowing for more sophisticated and efficient approaches to network management and optimization. The specific technology and research domain covered in this project include wireless sensor networks, clustering algorithms, fuzzy logic, and optimization techniques.

By delving into these areas, researchers and students can gain insights into the complexities of network management and explore novel strategies for improving network performance and energy efficiency. In conclusion, the proposed project on clustering and CH selection in wireless sensor networks has the potential to significantly contribute to academic research, education, and training in the field of networking and communication systems. By addressing the limitations of traditional approaches and introducing new methodologies based on fuzzy logic and optimization algorithms, this research opens up new opportunities for innovation and advancement in the field. Reference: Future Scope: The proposed research can be extended by incorporating machine learning techniques to further enhance the decision-making capabilities of the model. Additionally, conducting real-world experiments to validate the effectiveness of the proposed approach in practical scenarios can provide valuable insights for deployment in actual wireless sensor networks.

Further research can also explore the integration of multiple optimization algorithms to optimize the clustering and CH selection process in a dynamic and adaptive manner.

Algorithms Used

STSA algorithm is used in this research work for forming clusters in the wireless sensor network. Fuzzy Logic is employed for determining cluster heads based on QoS parameters like residual energy, required energy, communication area, and location of base node. FCM technique is used to form grids in the network and select the global head based on fitness value. The proposed model aims to reduce energy consumption in CH nodes, enhancing network stability and lifespan. By combining these algorithms, the research aims to improve efficiency and accuracy in clustering and routing protocols in WSNs.

Keywords

clustering, CH selection, wireless network lifespan, energy consumption, quality of service parameters, sink node location, traditional WSN models, network stability, network lifetime, clustering and routing protocol, fuzzy inference system, FCM technique, GH selection, STSA algorithm, nature inspired optimization algorithm, QoS parameters, fuzzy system, residual energy, required energy, communication area, base node, sink node location, fuzzy logic, decision-making capabilities, power consumption, fuzzy set theory, WSNs, decision-making enhancement, resource usage, model results.

SEO Tags

sensor networks, communication optimization, CH selection, data filtering, Fuzzy S-Tree, optimization algorithms, seed optimization, data aggregation, distributed systems, network performance, resource allocation, quality of service, energy efficiency, sensor node coordination, network optimization, WSN, wireless sensor networks, clustering, routing protocol, fuzzy inference system, FCM, fuzzy c-means, STSA, sine tree seed algorithm, QoS parameters, residual energy, communication area, location of base node, sink node, fuzzy logic, decision-making capabilities, fuzzy set theory, decision-making, resource usage.