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I. D. Ikpaya

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Open access Aug 2026

A Hotspot-Aware Load-Balanced Clustering and Routing Protocol for Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, industrial automation, and Internet of Things (IoT) applications. However, limited energy resources and hotspot formation near the base station significantly reduce network lifetime and communication efficiency. This study proposes a Hotspot-Aware Load-Balanced Clustering and Routing Protocol (HALBCRP) to improve energy utilization and mitigate hotspot formation in WSNs. The proposed protocol integrates energy-aware cluster formation, hotspot-aware cluster head selection, dynamic load balancing, and fault-tolerant multi-hop routing into a unified framework. Sensor nodes are grouped using an energy-distance-based clustering strategy, while cluster heads are selected based on residual energy, connectivity, traffic load, and proximity to the base station. To prevent energy holes and traffic congestion, overloaded cluster heads dynamically redistribute traffic to neighboring cluster heads. The performance of HALBCRP was evaluated using MATLAB and compared with ITSA-UCHSE, HHDAP, Q-DAEER, and IPSO based on energy consumption, average throughput, number of alive nodes, number of dead nodes, and network lifetime. Simulation results show that HALBCRP achieves lower energy consumption (0.605–1.053 J), maintains a higher number of alive nodes throughout the simulation, and attains an average throughput of 28.62 packets per round. Furthermore, the protocol records superior network lifetime values of FND = 800 rounds, HND = 1150 rounds, and LND = 1340 rounds. These results demonstrate that HALBCRP effectively mitigates hotspot formation, balances network load, and significantly extends network lifetime compared with existing approaches.

Okehie Baslem, I. D. Ikpaya, Onyeanusi Ugochukwu Chimobi et al. · 0 citations
#explainable ai Open access Sep 2026

Explainable deep learning with novel marine domain metrics for oil spill detection

This study demonstrates the application of explainable artificial intelligence (XAI) specifically the deep learning model for oil spill detection, which integrates the SpillNet, a customised Convolutional Neural Network architecture with five XAI techniques and unique evaluation metrics suitable for marine environmental monitoring.

Tokula I. Umaha, F. Ale, I. D. Ikpaya et al. · 0 citations

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