Babatunji Omoniwa

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About

Babatunji Omoniwa is a researcher specializing in fog computing, Internet of Things (IoT), and reinforcement learning (RL)-based optimization for wireless communication systems. His work sits at the intersection of edge intelligence and network efficiency, addressing critical challenges in how IoT devices communicate and consume energy in dynamic, resource-constrained environments. Omoniwa's most notable contribution explores the application of reinforcement learning to improve communication performance and energy utilization in fog-based IoT networks. His research investigates the strategic use of mobile fog devices — including smartphones, drones, and industrial robots — as intelligent relay nodes to minimize communication outages between IoT sensors and destination devices. This work is particularly relevant to localized IoT applications in manufacturing, smart homes, and autonomous systems, where reliable, low-latency communication is essential. By framing relay selection as a learning problem, Omoniwa advances a self-adaptive framework that allows networks to dynamically optimize performance without centralized control — a significant step toward autonomous, scalable IoT infrastructure. With citations reflecting growing interest in this domain, his research contributes meaningfully to the fields of edge computing and intelligent networking, offering practical pathways for next-generation IoT deployments where energy efficiency and communication reliability are paramount concerns.