Optimization of Data Aggregation Techniques in IoT-Based Wireless Sensor Networks

Abstract

This article delves into the optimization of data aggregation techniques in IoT-based wireless sensor networks (WSNs), aiming to improve network efficiency and extend sensor node lifespan. It explores various algorithms and methods used for aggregating sensor data, emphasizing their role in reducing communication overhead and conserving energy in IoT environments. Key challenges such as balancing aggregation accuracy, latency, and energy consumption are discussed, highlighting the need for adaptive and context-aware aggregation approaches tailored to specific IoT applications. The article presents a simulation and experimental framework for evaluating these techniques, outlining metrics and methodologies for performance assessment. A comparative analysis assesses the strengths, limitations, and suitability of different aggregation methods across diverse IoT scenarios, providing practical insights for implementing effective data aggregation strategies in WSNs. Finally, it concludes by outlining future research directions, advocating for advancements in optimization algorithms and collaborative efforts to meet evolving IoT demands and maximize the benefits of data aggregation.