Introduction
In the era of the Internet of Things (IoT), wireless sensor networks (WSNs) have become integral components, enabling the collection, transmission, and processing of various physical parameters in smart homes, agriculture, and water management applications. However, these networks face challenges in terms of energy efficiency, a crucial aspect for their long-term sustainability.
To address the energy challenges in WSNs, researchers have explored optimization techniques, with the Particle Swarm Optimization (PSO) algorithm emerging as a promising solution. PSO offers high accuracy but can suffer from premature convergence and local optima issues. To tackle these limitations, this paper proposes the development of an Enhanced Particle Swarm Optimization for Node Power Estimation (EPSO-NPE) model.
The EPSO-NPE algorithm calculates distinct transmission powers for each node, preventing the formation of isolated areas within a sensor cluster. Unlike the original PSO, the EPSO algorithm enhances exploration capabilities by avoiding stagnation on search space boundaries. A comparative analysis with the original PSO-based model (PSO-NPE), where nodes adopt maximum power for connectivity, reveals the superior performance of EPSO-NPE.
Wireless Sensor Networks and Energy Efficiency Challenges
Wireless sensor networks (WSNs) consist of numerous sensor nodes that can sense physical phenomena, including light, heat, and pressure. These networks are essential in smart homes, smart agriculture, and smart water management, contributing to the broader concept of the Internet of Things (IoT). However, WSNs face the challenge of energy-related issues, necessitating the pursuit of energy-conserving techniques for communication.
Optimizing energy consumption in WSNs is typically addressed using the Particle Swarm Optimization (PSO) algorithm, as it offers high accuracy. However, PSO is prone to local optima, resulting in early convergence and suboptimal solutions. This limitation prompted the development of the Enhanced Particle Swarm Optimization for Node Power Estimation (EPSO-NPE) model, which aims to enhance exploration capabilities and overcome the challenges of local optima.
Enhanced Particle Swarm Optimization for Node Power Estimation (EPSO-NPE)
The EPSO-NPE model introduces several key features to address the limitations of the original PSO-based approach:
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Distinct Transmission Powers: Unlike the PSO-NPE model, where nodes adopt the maximum power for connectivity, the EPSO-NPE algorithm calculates distinct transmission powers for each node. This prevents the formation of isolated areas within a sensor cluster, ensuring better coverage and connectivity.
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Enhanced Exploration Capabilities: The EPSO algorithm enhances the exploration capabilities of the optimization process by avoiding stagnation on search space boundaries. This helps the algorithm escape local optima and achieve more optimal solutions.
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Comprehensive Optimization: The EPSO-NPE model considers multiple factors, such as node energy, base station (BS) distance, packet loss rate, and data delay, to provide a more holistic optimization of the sensor network.
By incorporating these enhancements, the EPSO-NPE model aims to achieve heightened energy-saving capabilities, ultimately extending the network’s lifetime while maintaining reliable and efficient data transmission.
Comparative Analysis: EPSO-NPE vs. PSO-NPE
To evaluate the performance of the EPSO-NPE model, a comparative analysis was conducted with the original PSO-NPE approach. The key findings include:
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Energy Savings: The EPSO-NPE model exhibits superior energy-saving capabilities compared to the PSO-NPE approach. By calculating distinct transmission powers and enhancing exploration, EPSO-NPE is able to more effectively balance energy consumption across the network, prolonging its overall lifespan.
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Network Lifetime Extension: The enhanced exploration and optimization capabilities of EPSO-NPE result in a significant extension of the network’s lifetime. Experiments have shown that the EPSO-NPE model can extend the network’s operational duration by up to 43.62% compared to the original PSO-NPE approach.
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Improved Connectivity: The EPSO-NPE algorithm’s ability to calculate distinct transmission powers for each node helps prevent the formation of isolated areas within the sensor cluster. This ensures better coverage and connectivity, contributing to the overall reliability of the network.
These findings demonstrate the effectiveness of the EPSO-NPE model in addressing the energy-related challenges faced by wireless sensor networks. By leveraging enhanced optimization techniques and a comprehensive approach to energy management, the EPSO-NPE model emerges as a superior solution for IoT networks, offering prolonged network lifetimes and improved data transmission efficiency.
Practical Applications and Implications
The EPSO-NPE model has significant practical applications in the realm of IoT and smart systems. By optimizing energy consumption and extending network lifetimes, the EPSO-NPE approach enables more sustainable and reliable data collection, monitoring, and control in various IoT-driven applications, such as:
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Smart Home Automation: WSNs equipped with the EPSO-NPE model can enhance energy efficiency and prolong the operational duration of smart home systems, ensuring continuous monitoring and control of environmental parameters.
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Precision Agriculture: IoT-based WSNs utilizing the EPSO-NPE algorithm can support smart farming initiatives by enabling long-term data collection and analysis of soil conditions, weather patterns, and crop health, leading to improved decision-making and resource optimization.
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Water Management Systems: The EPSO-NPE model can contribute to the optimization of water distribution and monitoring networks, ensuring reliable data transmission and extended operational lifetimes for IoT-based smart water management systems.
By addressing the energy efficiency challenges in WSNs, the EPSO-NPE model paves the way for more robust, sustainable, and scalable IoT deployments, ultimately driving the advancement of smart technologies and intelligent systems.
Conclusion and Future Directions
This paper has presented the Enhanced Particle Swarm Optimization for Node Power Estimation (EPSO-NPE) model, a novel approach to addressing the energy efficiency challenges in wireless sensor networks. By incorporating distinct transmission power calculations, enhanced exploration capabilities, and a comprehensive optimization strategy, the EPSO-NPE model outperforms the original PSO-based approach in terms of energy savings, network lifetime extension, and improved connectivity.
The successful implementation of the EPSO-NPE model in IoT applications, such as smart homes, precision agriculture, and water management systems, highlights its practical relevance and potential for widespread adoption. As the IoT landscape continues to evolve, the EPSO-NPE approach can serve as a valuable tool for ensuring the long-term sustainability and reliability of sensor-driven networks.
Moving forward, further research can explore the integration of the EPSO-NPE model with emerging technologies, such as edge computing and 5G/6G communication protocols, to enhance the overall performance and capabilities of IoT systems. Additionally, investigating the adaptability of the EPSO-NPE algorithm to dynamic network topologies and exploring security measures for IoT sensor nodes can be promising areas for future exploration.
By continuously refining and expanding the capabilities of the EPSO-NPE model, researchers and practitioners can unlock new opportunities for innovative and energy-efficient IoT deployments, driving the digital transformation of various industries and paving the way for a more sustainable and interconnected future.
