77 citations · 130 across the 10 of their papers we have counts for
10 papers
Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks
Amir Hossein Estiri, Muthucumaru Maheswaran
Machine learning (ML) is expected to play a major role in 5G edge computing. Various studies have demonstrated that ML is highly suitable for optimizing edge computing systems as r…
A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges
Muthucumaru Maheswaran, Tianzi Yang, Salman Memon
Autonomous driving is expected to provide a range of far-reaching economic, environmental and safety benefits. In this study, we propose a fog computing based framework to assist a…
A Language for Programming Edge Clouds for Next Generation IoT Applications
Muthucumaru Maheswaran, Robert Wenger, Richard Olaniyan +3
For effective use of edge computing in an IoT application, we need to partition the application into tasks and map them into the cloud, fog (edge server), device levels such that t…
Multi-Point Synchronization for Fog-Controlled Internet of Things
Richard Olaniyan, Muthucumaru Maheswaran
This paper presents a fog-resident controller architecture for synchronizing the operations of large collections of Internet of Things (IoT) such as drones, Internet of Vehicles, e…
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Salman Memon, Muthucumaru Maheswaran
Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet…
Opportunistic Edge Computing: Concepts, Opportunities and Research Challenges
Richard Olaniyan, Olamilekan Fadahunsi, Muthucumaru Maheswaran +1
The growing need for low-latency access to computing resources has motivated the introduction of edge computing, where resources are strategically placed at the access networks. Un…