Optimizing Energy Efficiency in Wireless Sensor Networks through Hybrid Optimization Algorithms and Range-Based Communication

Problem Definition

The existing literature on enhancing the lifespan of wireless networks reveals that while various techniques have been proposed, there is still room for improvement in the selection of Cluster Heads (CH) within the network. Traditionally, factors such as energy, node degree, and sensor node distance have been considered when choosing CH, but it is evident that there are other crucial factors that must also be taken into account. Furthermore, researchers have attempted to use optimization algorithms in their work, but these methods often suffer from slow convergence rates and can be trapped in local minima. In real-world scenarios, nodes frequently encounter large communication distances, leading to excessive energy consumption and data loss. These challenges underscore the urgent need to enhance the current algorithm in order to address these limitations and ultimately prolong the lifespan of wireless networks.

Objective

The objective is to enhance the lifespan of wireless sensor networks by addressing the limitations in existing CH selection methods. This will be achieved through the proposed hybrid optimization algorithm combining GOA and ABC, which aims to minimize node energy while prolonging network lifespan. The new model considers additional factors like average distance between nodes and implements range-based communication to optimize energy usage and increase network durability. By improving CH selection and communication methods, the proposed work seeks to significantly extend the lifetime of wireless sensor networks.

Proposed Work

In order to overcome the shortcomings of traditional models, a new and effective method is proposed in this paper that is based on hybrid optimization algorithms. The key goal of the proposed model is to improve the lifespan of the wireless network while minimizing the node energy. In a standard WSN, choosing the best CH for the network is vital for extending the network lifespan; as a result, choosing CH for the network must be done using an efficient method. To accomplish this task, we have used a hybrid optimization algorithm in which Grasshopper optimization algorithm (GOA) and Artificial Bee colony (ABC) algorithm are hybridized. In the proposed study, two optimization approaches were merged mainly to solve problems with slow convergence rate and tendency to get stuck in local minima.

We have also changed the network's CH selection standards. The prior approach just used the node density, residual energy, and distance factors for selecting the CH in the network, as was previously mentioned. However, after investigating the literature, we found that the average distance between two adjacent nodes is important in selecting the appropriate CH. As a result, we have taken this into account while recommending the best CH. Furthermore, we also considered the fact that certain nodes in the sensing region were not connected to any cluster groups, despite communication in the usual design occurring from sink node to CH to node.

Such nodes directly interface with the sink node to transmit data, which uses a lot of energy and eventually depletes the nodes. In the proposed study, we have chosen to adopt range-based communication as a solution, that means that non-cluster member nodes will seek out the closest node or CH while transmitting data. By doing this, the non-cluster member nodes can send information to a nearby node that will subsequently send them to the sink node. In this way, node energy usage is optimized, and network durability is increased. Consequently, the lifetime of the wireless sensor network can be greatly extended by employing the hybrid optimization method and range-based communication system.

Application Area for Industry

This project can be beneficial for various industrial sectors such as agriculture, healthcare, environmental monitoring, smart cities, and industrial automation. In agriculture, the proposed solutions can help in monitoring crop conditions, optimizing irrigation systems, and enhancing overall farm productivity. For healthcare, the project can aid in remote patient monitoring, real-time health data collection, and ensuring timely medical interventions. In environmental monitoring, the solutions can be used to monitor air quality, water quality, and detect natural disasters. In smart cities, the project can assist in optimizing traffic management, waste management, and energy consumption.

Lastly, in industrial automation, the proposed solutions can help in monitoring equipment performance, optimizing production processes, and improving overall operational efficiency. The key challenges that industries face, such as excessive energy consumption, slow convergence rates, and data loss, can be effectively addressed by implementing the proposed solutions. By using a hybrid optimization algorithm and incorporating factors like average distance between nodes and range-based communication, the network lifespan can be extended, energy consumption can be minimized, and data loss can be reduced. This will lead to increased efficiency, improved decision-making processes, and enhanced overall performance across various industrial domains. By leveraging the innovative solutions proposed in this project, industries can achieve significant cost savings, operational improvements, and competitive advantages in their respective fields.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of wireless sensor networks. By introducing a novel hybrid optimization algorithm combining Grasshopper optimization algorithm (GOA) and Artificial Bee Colony (ABC) algorithm, the project aims to address the limitations of traditional methods in selecting cluster heads (CH) and improving network lifespan while minimizing node energy consumption. This project can provide a valuable contribution to academic research by offering a new perspective on CH selection criteria, incorporating factors such as the average distance between nodes and employing range-based communication for non-cluster member nodes. Researchers in the field of wireless sensor networks can leverage the code and literature of this project to enhance their own work, explore innovative research methods, and conduct simulations for data analysis. MTech students and PhD scholars can benefit from the proposed project by utilizing the hybrid optimization algorithm to optimize network performance and extend the lifespan of wireless sensor networks.

The integration of ABC and GOA algorithms can offer a more efficient solution compared to traditional optimization techniques, leading to improved results and potential applications in various research domains within educational settings. As a future scope, researchers can further explore the potential applications of hybrid optimization algorithms in enhancing network performance, reducing energy consumption, and improving data transmission efficiency in wireless sensor networks. This project opens up possibilities for innovative research methods and simulations, making it a valuable resource for academic research, education, and training in the field of wireless sensor networks.

Algorithms Used

The proposed model in this project utilizes a hybrid optimization algorithm combining Grasshopper Optimization Algorithm (GOA) and Artificial Bee Colony (ABC) algorithm to improve the lifespan of wireless sensor networks while minimizing node energy. These algorithms overcome the shortcomings of traditional models by enhancing convergence rates and avoiding local minima. The model selects cluster heads (CH) based on factors such as node density, residual energy, distance, and average distance between adjacent nodes. Range-based communication is also implemented for non-cluster member nodes to transmit data efficiently by seeking the closest node or CH. This optimization approach significantly extends the lifetime of the wireless sensor network and improves network efficiency.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, WSN, Network stability, Stability assured algorithm, GOA-ABC, Grasshopper Optimization Algorithm, Artificial Bee Colony, Optimization, Energy-efficient routing, Power control, Network performance, Energy management, Swarm intelligence, Hybrid metaheuristic, Self-organization, Node stability, Network topology, Energy-efficient communication, Network reliability, Artificial intelligence, Lifespan enhancement, CH selection, Hybrid optimization algorithms, Local minima, Communication distance, Node energy, Range-based communication, Sensor node distance, Convergence rate, Optimization approaches, Network durability, Data loss prevention, Network lifespan extension.

SEO Tags

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