Active and completed research projects across Fog Computing, Blockchain, Distributed Systems, and Next-Generation Computing — grounded in published work.
✓ Completed · PhD Thesis
Resource Management in Fog Computing Environments
Comprehensive research into stochastic modelling, meta-scheduling, and energy-aware resource allocation for Fog Computing. Developed generic stochastic models for resource availability, efficient monitoring services, and micro-level compensation-based cost models that jointly optimise latency, energy, and cost in Fog–Cloud hybrid deployments.
Fog ComputingStochastic ModellingResource AllocationIoTJava
Developing a blockchain-based framework for automated Service Level Agreement (SLA) management using smart contracts, combined with novel cross-chain NFT standards. The project addresses trust, verifiability, and interoperability gaps in decentralised Fog deployments, enabling automated enforcement and dispute resolution across multi-provider environments.
HRFP: Prefetching & Caching for Distributed File Systems
Highly Relevant Frequent Patterns-Based algorithms that improve I/O performance for big data workloads in HDFS by predicting access patterns and preloading data into cache.
HDFSCachingBig Data
Completed
Energy-Aware VM Management for IoT in Fog
Data-specific VM consolidation policy that reduces energy consumption in Fog nodes while meeting the latency SLAs of heterogeneous IoT application workloads.
VM ManagementEnergyIoT
Completed
FogAuthChain: Blockchain Location Authentication
Secure, location-based authentication scheme for Fog computing using blockchain to prevent replay attacks and ensure data integrity in edge deployments.
AuthenticationLocationFog
Completed
SDP: Scalable Real-Time Dynamic Graph Partitioner
Novel real-time dynamic graph partitioning system that distributes workloads across cloud clusters with minimal data migration overhead for large-scale streaming analytics.
Graph PartitioningCloudReal-time
Completed
SMOaaS: Scalable Matrix Operation as a Service
Cloud-native service model for distributing large-scale matrix operations across heterogeneous nodes with fault-tolerance and elastic scaling for scientific computing workloads.
CloudHPCMatrix
Active
Neuromorphic Computing for Distributed Workloads
Theoretical and applied research into spiking neural network architectures for energy-efficient processing at the edge, targeting next-generation AI inference workloads.
NeuromorphicEdge AISNN
Timeline
Project Timeline 2014–2024
All research projects plotted chronologically. Hover for details.
Key Publications Supporting These Projects
IEEE 2020
A Generic Stochastic Model for Resource Availability in Fog Computing Environments
IEEE Transactions on Parallel and Distributed Systems, Vol. 32(4)
IEEE 2019
An Efficient Resource Monitoring Service for Fog Computing Environments
IEEE Transactions on Services Computing, Vol. 13(4)
Springer 2022
A Blockchain-Based Framework for Automatic SLA Management in Fog Computing
Journal of Supercomputing, Vol. 78(15)
Elsevier 2020
FogAuthChain: A Secure Location-Based Authentication Scheme in Fog Computing Using Blockchain
Computer Communications, Vol. 162
MDPI 2023
HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching for Distributed File Systems
Electronics, Vol. 12(5)
Wiley 2020
IoTSim-Edge: A Simulation Framework for Modeling IoT and Edge Computing Environments
Software: Practice and Experience, Vol. 50(6) — Top-Cited Article