Eoghan Furey

Zahid Ali, Saritha Unnikrishnan, Eoghan Furey et al.

In post-disaster environments, damaged communication infrastructure severely limits coordination among emergency response teams. Cognitive radio networks (CRNs) enable rapidly deployable communication by allowing nodes to opportunistically access available spectrum. However, existing multihop rendezvous protocols typically rely on N-1 termination conditions, which can lead to premature termination, resulting in incomplete neighbour discovery and invalid network topology formation. This work identifies this limitation as a previously overlooked issue in multihop rendezvous protocols. This paper proposes a Multihop Reliable Dual-Modular Clock Algorithm (MR-DMCA) that eliminates premature termination and ensures reliable network formation. The proposed protocol introduces a coordinate-assisted neighbour validation mechanism and an autonomous termination strategy that guarantees complete neighbour and topology discovery before protocol termination. Although implemented within MR-DMCA, the proposed validation and termination approach is applicable to a wider class of multihop rendezvous protocols. Extensive simulations demonstrate that, in a worst-case scalable scenario with 20 nodes and 20 channels under high primary radio activity (m=2), MR-DMCA achieves 100% accurate neighbour and topology discovery while reducing rendezvous time by up to 76%, 37%, and 17% compared with baseline protocols. The results highlight that addressing premature termination is critical for reliable multihop rendezvous in cognitive radiobased emergency communication networks.

Zahid Ali, Saritha Unnikrishnan, Eoghan Furey et al.

This paper addresses the challenge of efficient rendezvous in multihop cognitive radio networks, where existing channel-hopping algorithms designed for single-hop scenarios incur increased delay and coordination inefficiencies in multinode topologies. To overcome these limitations, we propose a Multihop Dual Modular Clock Algorithm (M-DMCA), which systematically extends modular clock-based rendezvous to multihop environments while preserving efficient channel coordination. The proposed scheme enables dual-channel selection per timeslot and incorporates a lightweight three-way handshake mechanism to improve coordination among intermediate nodes. Simulation results under worst-case conditions, including high primary user activity, asymmetric channel availability, and dense network settings, demonstrate that M-DMCA significantly reduces rendezvous time compared to existing approaches, achieving up to 24% improvement. These results demonstrate the suitability of M-DMCA for timely node discovery in dynamic emergency response scenarios.

Muhammad Jawad Bashir, Shagufta Henna, Eoghan Furey

The integration of the Internet of Robotic Things (IoRT) in smart greenhouses has revolutionised precision agriculture by enabling efficient and autonomous environmental control. However, existing time series forecasting models in such setups often operate as black boxes, lacking mechanisms for explainable decision-making, which is a critical limitation when trust, transparency, and regulatory compliance are paramount in smart farming practices. This study leverages the Temporal Fusion Transformer (TFT) model to automate actuator settings for optimal greenhouse management. To enhance interpretability and trust in the model decision-making process, both local and global explanation techniques were employed using model-inherent interpretation, local interpretable model-agnostic explanations (LIME), and SHapley additive explanations (SHAP). These explainability methods provide information on how different sensor readings, such as temperature, humidity, CO2 levels, light, and outer climate, contribute to actuator control decisions in an automated greenhouse. The trained TFT model achieved a test accuracy of 95% on a class-imbalanced dataset for actuator control settings in an automated greenhouse environment. The results demonstrate the varying influence of each sensor on real-time greenhouse adjustments, ensuring transparency and enabling adaptive fine-tuning for improved crop yield and resource efficiency.