Optimizing Wireless Sensor Network Lifespan with ANFIS: A Hybrid Approach for Enhanced Energy Efficiency and Routing

Problem Definition

Various techniques have been explored in the past to improve the lifetime and efficiency of sensor nodes, with clustering being a widely used approach. Clustering helps with power control and resource allocation by reusing bandwidth effectively. However, the selection and allocation of cluster heads (CHs) play a crucial role in the overall system performance. While numerous CH selection schemes have been proposed, many of them tend to overload the cluster head, affecting the system's efficiency. Some researchers have looked into using fuzzy logic for decision-making in sensor networks, particularly Type 1FL, Type 2FL, and LEACH schemes.

Although these approaches help manage uncertainty in the network, they often fail to consider the mobility of the base station, leading to a constant network lifetime regardless of changes in the environment. Additionally, some algorithms have been developed to address this issue by extending the network lifetime compared to LEACH, but they may not scale well for larger applications and lack detailed simulation results. An alternative protocol based on fuzzy parameters like remaining battery power, mobility, and distance to the base station was proposed to elect a super cluster head (SCH) among the CHs. However, this protocol also suffers from the same drawback of a constant network lifetime despite mobility changes and lacks thorough system analysis. These existing schemes fall short in terms of energy efficiency and cluster head selection, highlighting the need for a more robust and scalable solution.

Objective

The objective of this project is to address the limitations of existing clustering algorithms in Wireless Sensor Networks (WSNs) by introducing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the selection of Cluster Heads. The main goals are to enhance energy efficiency, increase network lifetime, and improve routing algorithms within WSNs. By leveraging the capabilities of ANFIS, which combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), a more robust and efficient system for CH selection will be developed. This approach involves deploying sensor nodes, selecting cluster heads based on various parameters, and using ANFIS for the final selection. The rationale behind choosing ANFIS is its ability to offer a more intelligent and adaptive solution for CH selection, leading to improved performance and energy savings in WSNs.

Proposed Work

Therefore, the proposed work aims to address the limitations of existing clustering algorithms in Wireless Sensor Networks (WSNs) by introducing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the selection of Cluster Heads. The main objectives of this project are to enhance energy efficiency, increase network lifetime, and improve routing algorithms within WSNs. By leveraging the hybrid capabilities of ANFIS, which combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), we aim to develop a more robust and efficient system for CH selection. The approach involves deploying sensor nodes in a specific area, initializing them, selecting cluster heads randomly based on a probability equation, calculating parameters such as node residual energy and distance to the base station, and ultimately using ANFIS for the final selection of cluster heads. This methodology allows for a more dynamic and intelligent approach to cluster head selection, leading to improved performance and energy savings in WSNs.

This project rationale behind choosing ANFIS lies in its ability to combine the strengths of neural networks and fuzzy logic, offering a more intelligent and adaptive solution for CH selection in WSNs. Unlike previous clustering algorithms that may have limitations in terms of efficiency, scalability, and energy consumption, ANFIS provides a more advanced and flexible approach. By incorporating various parameters such as energy levels, distance to the base station, and node concentration, the proposed system ensures a more comprehensive evaluation of the network dynamics before selecting cluster heads. Furthermore, the use of ANFIS allows for more precise decision-making and better adaptability to changing network conditions, ultimately leading to a more energy-efficient and sustainable WSN solution.

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 project can help in monitoring soil moisture levels, crop health, and weather conditions through sensor networks. For environmental monitoring, the system can be used to track air and water quality, as well as detect natural disasters. In smart cities, the project can aid in monitoring traffic flow, energy consumption, and waste management. In healthcare, the system can assist in tracking patient vitals, medication adherence, and hospital equipment maintenance.

Lastly, in manufacturing, the project can be used to monitor machinery health, inventory levels, and production efficiency. The proposed solutions offered by this project address the challenge of efficient cluster head selection, energy conservation, extended network lifetime, and improved routing algorithms in wireless sensor networks. By utilizing the ANFIS hybrid model of artificial neural networks and fuzzy logic, the project can optimize cluster head selection based on parameters such as node residual energy, distance to the base station, and packet transmission delay. Implementing these solutions can result in improved network performance, increased energy efficiency, and enhanced system scalability across various industrial domains. By leveraging advanced algorithms and innovative approaches, the project can bring significant benefits to industries looking to optimize their sensor network operations.

Application Area for Academics

The proposed project aims to enrich academic research, education, and training in the field of wireless sensor networks by addressing the limitations of existing clustering techniques and improving energy efficiency, network lifetime, and routing algorithms. By incorporating fuzzy logic and artificial neural network-based systems like ANFIS, the project offers a novel approach to cluster head selection, energy savings, and improved performance in WSNs. Researchers in the field of wireless sensor networks, as well as MTech students and PhD scholars, can benefit from the code and literature of this project by gaining insights into advanced clustering techniques, fuzzy logic, and neural network models. By studying the proposed ANFIS-based system, researchers can explore new methods for optimizing CH selection, energy efficiency, and network performance in WSNs. They can also leverage the project's data analysis capabilities for simulating and evaluating the impact of different parameters on network operations.

The relevance of this project extends to various technology domains within wireless sensor networks, particularly in the areas of cluster head selection, energy optimization, and routing protocols. Researchers and students can apply the insights gained from this project to develop innovative research methods, simulations, and data analysis techniques in their academic pursuits. The project opens up new avenues for exploring the potential applications of fuzzy logic and neural networks in enhancing the efficiency and performance of wireless sensor networks. In terms of future scope, the proposed project could lead to further advancements in clustering algorithms, energy-efficient protocols, and network optimization strategies for WSNs. By continuing to refine the ANFIS-based system and exploring new research directions, researchers can contribute to the development of cutting-edge solutions for improving the reliability, scalability, and overall performance of wireless sensor networks.

Algorithms Used

ANFIS is used in this work because it is the hybrid model of the two schemes namely ANN and FL, thus consist of benefits of both. In first step, nodes are deployed in the specific area and initialization of nodes is done. Once the nodes are initialized, next step is to select the node cluster heads in the network. For the cluster head selection, nodes in the network are selected randomly and the probability equation is used for the probability calculation of the cluster heads in nodes. In case the equation is satisfied, the nodes are designated as the CHs.

After random cluster head formation, the distance of respective nodes from the network is calculated and the nodes are assigned to the clusters. Next step is to calculate the various parameters named as node residual energy, distance to base station, concentration of nodes in the network and delay of the packet transmission. After evaluation of the various parameters of nodes and clusters formed initially, next phase is actual cluster head selection. For this purpose ANFIS, i.e.

artificial neural network based fuzzy logic proposed system is used for selection of the CHs. Communication from source node to destination takes place and energy dissipation is calculated.

Keywords

Adaptive Neuro-Fuzzy Inference System (ANFIS), Cluster Head Selection, Wireless Sensor Networks (WSNs), Energy Efficiency, Energy-Efficient Protocols, Remaining Power Battery, Distance to Base Station (BS), Concentration, Delay, Network Performance, Cluster Head Optimization, WSNs Energy Optimization, ANFIS Model for WSNs

SEO Tags

Adaptive Neuro-Fuzzy Inference System, ANFIS, Cluster Head Selection, Wireless Sensor Networks, WSNs, Energy Efficiency, Energy-Efficient Protocols, Remaining Power Battery, Distance to Base Station, Concentration, Delay, Network Performance, Cluster Head Optimization, WSNs Energy Optimization, ANFIS Model for WSNs, Sensor Node Lifetime Improvement, Power Control in Sensor Networks, Bandwidth Resource Allocation, Fuzzy Logic Decision Making, Type 1FL, Type 2FL, LEACH Protocol, Super Cluster Head Selection, Routing Algorithms, Hybrid ANN and FL Model, Node Initialization, Node Residual Energy, Packet Transmission Delay, Energy Dissipation Analysis.