RECENT ADVANCES IN WIRELESS SENSOR NETWORKS LOAD-BALANCING OPTIMIZATION: A REVIEW OF APPROACHES AND EMERGING TRENDS

Wireless Sensor Networks (WSNs) play an essential role in applications such as environmental monitoring, smart cities, and industrial automation, where numerous sensor nodes are positioned in remote areas with limited energy resources. The longevity of these networks depends significantly on active load-balancing mechanisms to consistently distribute data processing and communication tasks, thereby preventing early energy depletion of individual nodes. This study employs a systematic review methodology to evaluate recent developments in load-balancing strategies for WSNs. We organize these strategies into three primary types: centralized, decentralized, and hybrid approaches. Centralized methods utilize a global view for optimal load distribution but are hindered by scalability and bottleneck issues. Decentralized methods, where nodes make independent decisions, improve scalability and fault tolerance but have a high likelihood of causing uneven load distribution. Hybrid methods incorporate features from both centralized and decentralized approaches, seeking a balance between global coordination and local adaptability. Additionally, this review examines emerging techniques that integrate machine learning and game theory, providing dynamic and real-time adaptations to changing network conditions. Results indicate that while each approach has distinct strengths, challenges remain, especially regarding energy efficiency, scalability, and adaptability to environmental changes. The analysis underscores the need for adaptive and context-aware load-balancing solutions that enhance WSN resilience in complex scenarios. In conclusion, this review provides insights into the latest advancements and identifies areas for future research in load-balancing strategies, aiming to support sustainable WSN deployments across diverse applications.