Energy Efficient Optimization of WSN using Chaotic-ABC Algorithm

Problem Definition

The wireless sensor network (WSN) technology is facing challenges regarding energy utilization and network lifespan. Current approaches are not yielding effective results, particularly in terms of energy consumption by cluster head (CH) nodes during data collection and transmission to the sink node. Existing models for CH selection in WSNs are limited in their parameters and fail to consider various factors that influence this process. Additionally, optimization methods used to improve energy efficiency often get stuck in local minima, hindering the search for global fitness values. These limitations underscore the necessity for a new and improved energy protocol for WSNs that addresses the inefficiencies and shortcomings of current technologies.

By addressing these issues, researchers can work towards enhancing the overall performance and longevity of wireless networks.

Objective

The objective of this study is to develop a new energy protocol for wireless sensor networks (WSN) that addresses the challenges of energy utilization and network lifespan. By incorporating chaotic map and Artificial Bee Colony (ABC) optimization algorithm, the proposed model aims to reduce energy consumption by cluster head (CH) nodes during data collection and transmission to the sink node. The model also aims to optimize CH selection based on parameters such as residual energy, node density, distance, and throughput, leading to more efficient energy utilization. Additionally, the introduction of relay nodes in the network is proposed to optimize communication distance and make the communication process more reliable and energy-efficient. Through these enhancements, the objective is to improve the overall performance and longevity of wireless networks.

Proposed Work

To address the issue of energy utilization and network lifespan in wireless sensor networks (WSN), a new approach is proposed in this paper. By incorporating chaotic map and Artificial Bee Colony (ABC) optimization algorithm, the proposed model aims to reduce the energy consumption of nodes and enhance the overall lifespan of the network. The use of chaotic map along with ABC optimization algorithm helps in improving the convergence rate and avoiding getting trapped in local minima. The proposed chaotic map-ABC model selects cluster heads (CH) based on four essential parameters: residual energy, node density, distance, and throughput for each node. The node with the best fitness value calculated from these parameters is chosen as the CH in the network, leading to more efficient energy utilization.

Furthermore, the proposed model introduces the concept of relay nodes to optimize communication distance between cluster heads and the sink node. By adding relay nodes as intermediates between CH nodes and the base station, the communication process becomes more reliable and energy-efficient. This modification not only reduces energy consumption during data transmission but also prolongs the network lifespan. By addressing the limitations of current WSN technologies and improving CH selection and communication methods, the proposed model offers a more effective and energy-efficient solution for enhancing the performance of wireless networks.

Application Area for Industry

This project can be applied across various industrial sectors that rely on wireless sensor networks for data collection and communication. Industries such as agriculture, environmental monitoring, smart cities, healthcare, and manufacturing can benefit from the proposed energy-efficient approach. The challenges faced by these industries include limited network lifespan due to high energy consumption, inefficient CH selection methods, and communication reliability issues. By incorporating chaotic map and ABC optimization algorithm, the proposed solution aims to address these challenges by reducing energy consumption, improving CH selection process, and enhancing communication reliability through the use of relay nodes. Implementing these solutions would result in increased network lifespan, improved data collection efficiency, and overall cost savings for industries utilizing wireless sensor networks.

Application Area for Academics

The proposed project offers significant contributions to academic research, education, and training in the field of wireless sensor networks. By incorporating chaotic map and Artificial Bee Colony (ABC) optimization algorithm, the project aims to address the limitations of existing WSN technologies in terms of energy efficiency and network lifespan. Academically, this project enriches research by introducing a novel energy-efficient approach that combines chaotic map and ABC optimization algorithm for CH selection in WSN. This not only enhances the convergence rate but also mitigates the issue of local minima traps faced by traditional optimization methods. Researchers, MTech students, and PhD scholars can benefit from the code and literature provided in this project to explore innovative research methods, simulations, and data analysis techniques within educational settings.

The relevance of this project lies in its potential applications in exploring new avenues for energy-efficient protocols in wireless networks. The integration of chaotic map and ABC optimization algorithm offers a unique perspective on improving the performance of WSNs by addressing energy consumption issues and prolonging network lifespan. Researchers from the specific domain of wireless sensor networks can leverage the findings of this project to enhance their own research methodologies and develop cutting-edge solutions. Future scope of this project includes further exploration of the impact of chaotic map and ABC optimization algorithm on other aspects of WSNs, such as data routing and security. Additionally, the proposed relay nodes could be further optimized for enhanced communication reliability and energy efficiency.

By extending the application of chaotic map and ABC optimization algorithm to other research domains, this project has the potential to drive innovation and advance knowledge in the field of wireless sensor networks.

Algorithms Used

The proposed work in this project uses chaotic map and Artificial Bee Colony (ABC) optimization algorithm to optimize energy consumption in wireless sensor networks. The chaotic map is utilized to enhance the convergence rate of the ABC optimization algorithm and prevent it from getting trapped in local minima. By analyzing key parameters such as residual energy, node density, distance, and throughput, the proposed model selects cluster heads effectively in the network based on fitness value calculations. Additionally, the introduction of relay nodes improves the communication process by acting as intermediaries between cluster heads and the base station, reducing energy consumption and prolonging the network lifespan.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, WSN, Clustering protocol, CM-ABC, Cuckoo Search, Artificial Bee Colony, Network lifetime, Energy efficiency, Data aggregation, Data routing, Cluster formation, Cluster head selection, Network topology, Node selection, Energy conservation, Self-organization, Wireless communication, Sensor nodes, Application-specific networks, Energy-aware protocols, Artificial intelligence

SEO Tags

Problem Definition, Wireless Sensor Networks, WSN, Energy Efficiency, Cluster Head Selection, Node Selection, Network Topology, Energy Conservation, Data Aggregation, Data Routing, Chaotic Map, Artificial Bee Colony, ABC Optimization Algorithm, Network Lifespan, Communication Process, Relay Node, Clustering Protocol, Cuckoo Search, Network Performance, Self-organization, Energy Protocol, Global Fitness Value, Optimization Methods, Literature Analysis, Research Scholars, PHD Students, MTech Students, Research Topic, Wireless Communication, Sensor Nodes, Energy Utilization, Node Lifespan, Effective Results, Optimization Model, Fitness Function, Residual Energy, Distance Metric, Throughput Analysis, Communication Phase, Base Station, Relay Node Placement, Energy Consumption, Online Visibility, Academic Research.