Internet of Underwater Things — Cognitive Networking

Overview

My doctoral and ongoing research at the James Watt School of Engineering, University of Glasgow focuses on the Internet of Underwater Things (IoUT) — one of the most demanding application areas in wireless engineering due to harsh acoustic channels, low bandwidth, high latency, and severe energy constraints.

Key Engineering Contributions

DEKCS — Dynamic Clustering Protocol

A clustering and routing protocol for underwater wireless sensor networks that selects cluster heads dynamically based on residual energy and distance. Extends network lifetime by more than 70% compared to standard protocols.

• Published in IEEE Sensors Journal (2021); cited 100+ times.
• One of the most downloaded papers in its category.
• Led to an invited presentation at the IEEE Sensors Conference, Dallas.

RL-SWIPT — Sustainable Underwater Charging

A reinforcement-learning approach for simultaneous wireless information and power transfer (SWIPT) using AUVs. Models the joint throughput / harvested-power problem as an MDP — achieves up to 207% improvement in energy efficiency vs random-trajectory baselines.

• Published in IEEE Internet of Things Journal (2024).
• First RL-based approach for this application.

Q-Learning Route Selection

Models routing as a reinforcement-learning problem prioritising energy efficiency and packet delivery — yielding orders-of-magnitude energy savings vs direct transmission.

• Published in IEEE WCNC.

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Current Manuscripts (Under Review)

Hierarchical Federated Anomaly Detection (IEEE IoT Journal)

A three-tier federated learning framework for IoUT anomaly detection. Feasibility-aware sensor-to-fog association, compressed model updates, and selective cooperative aggregation.

• 31–33% energy reduction vs always-on inter-fog exchange.
• 71–95% total energy reduction with compressed uploads.

STAN — Semantic Telemetry & Anchor Navigation (IEEE JOE)

Transforms AUVs from passive recorders into intelligent edge nodes:

• FathomNet-trained YOLOv8 for semantic detection.
• 64-byte semantic tokens replace raw images (99.9%+ bandwidth savings).
• Persistent underwater structures act as visual anchors for drift correction.

ML for IoUT — Survey & Tutorial (IEEE COMST)

A comprehensive tutorial-survey synthesising ~300 papers (2012–2025) on machine learning across the IoUT protocol stack — from physical layer through application layer — with a technology roadmap to 2035+.

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Why It Matters

The engineering principles developed here — resilient sensing, energy-aware protocol design, and federated edge intelligence in constrained environments — generalise directly to other application domains, including smart housing, environmental monitoring, and critical infrastructure resilience.