Energy-Efficient Routing Protocols for IoT-Based Wireless Sensor Networks in Nuclear Facility Monitoring
Abstract
Reliable and energy-efficient wireless sensor networks (WSNs) are important for continuous monitoring in nuclear facilities, where limited battery resources, restricted maintenance access, and safety-critical operation require stable and efficient communication. This study evaluates four routing protocols for IoT-based WSN monitoring: Low-Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient and Balanced Aggregation in Sensor Networks (PEBAIS), Threshold-sensitive Energy Efficient Network (TEEN), and Geographic Adaptive Fidelity (GAF). A simulation-based comparative framework was developed using 200 sensor nodes distributed over a 5000 m2 monitoring area, with an initial node energy of 0.5 J, a packet size of 4000 bits, and a first-order radio-energy model. The protocols were compared using the energy consumption, residual energy, network lifetime, latency, throughput, signal-to-noise ratio (SNR), and bit error rate (BER). The results show that LEACH provides simple clustering and data aggregation but is more sensitive to the cluster-head distribution and residual-energy imbalance. PEBAIS achieves the most balanced overall behavior by combining efficient data aggregation with improved energy distribution among nodes. TEEN is suitable for event-driven monitoring because threshold-based transmission reduces unnecessary communication and supports a rapid response to significant changes. GAF extends the network lifetime by deactivating redundant nodes; however, its sleep-scheduling mechanism may increase the delay in time-sensitive situations. Overall, PEBAIS and GAF are more suitable for long-term monitoring, whereas TEEN is more appropriate for urgent event-detection scenarios. These findings support the selection of routing strategies for IoT-enabled WSNs in nuclear-facility monitoring and indicate the need for future hybrid protocols that combine energy efficiency, responsiveness, and operational reliability under safety-critical conditions.