LAB-AM

Department of Computer Engineering · Faculty Research Lab

Prof. Mohammad Amjad

Professor

Researching fault-tolerant routing in mobile ad-hoc and sensor networks, automated test case generation from UML models, green cloud scheduling algorithms, and IoT device authentication via blockchain ledgers.

MANET RoutingAutomated TestingCloud SecurityIoT-Blockchain

Collaborate with this Lab →Lab Publications (5) ↓

Research Specialisations

Areas of active investigation.

01Mobile Ad-hoc Networks (MANETs) & Wireless Sensor Networks (WSNs)

02Software Engineering & Automated Software Testing / Test Case Generation

03Security in Cloud Computing & Green Cloud Computing

04IoT Architecture & Blockchain Integration

Principal Investigator · Profile

Prof. Mohammad Amjad

Professor

Department of Computer Engineering

Expert in wireless ad-hoc networks, automated software testing, cloud security, and IoT-blockchain integration architectures.

Propose Collaboration →Apply for Lab Fellowship

Laboratory Literature

Published papers, preprints & inventions.

[LAB-AM]Software Engineering & Automated Testing2016-12-09[PEER-REVIEWED]Citations: 32+

Automatic Test Case Generation for Unit Software Testing Using Genetic Algorithm and Mutation Analysis

Amjad, M., Beg, M. S., Doja, M. N.

IEEE UP Section Conference on Electrical Computer and Electronics (UPCON)

This paper presents an automated test case generation approach for unit software testing using genetic algorithms combined with mutation analysis. The method evolves high-coverage test suites that expose software faults with minimal manual effort, validated on benchmark Java programs.

#Genetic Algorithm#Test Case Generation#Software Testing#Mutation Analysis#Automated Testing

View in Main Bibliography →

[LAB-AM]Software Engineering & Performance Engineering2016-09-01[PEER-REVIEWED]Citations: 21+

Performance Testing (Load) of Web Applications Based on Test Case Management

Amjad, M., Beg, M. S.

Perspectives in Science (Elsevier)

Proposes a meta-heuristic test case management strategy for web application load and performance testing. The approach optimizes test suite selection to maximize fault detection under realistic concurrent-user scenarios, reducing testing overhead while maintaining coverage integrity.

#Performance Testing#Load Testing#Web Applications#Test Case Management#Meta-heuristics

View in Main Bibliography →

[LAB-AM]IoT Architecture & Blockchain Integration2021-07-28[PEER-REVIEWED]Citations: 38+

Efficient Data Communication Using Distributed Ledger Technology and IOTA-Enabled Internet of Things for a Future Machine-to-Machine Economy

Amjad, M., Rehmani, M. H., Manzoor, S.

Sensors (MDPI)

Investigates the application of IOTA's Tangle distributed ledger technology for enabling efficient, fee-free machine-to-machine (M2M) data communication in IoT ecosystems. The paper evaluates throughput, latency, and energy efficiency for future autonomous M2M economic transactions.

#IOTA#Distributed Ledger#IoT#M2M Communication#Blockchain

DOI: 10.3390/s21154995 ↗View in Main Bibliography →

[LAB-AM]Green Computing & Sustainable AI2024-06-01[PEER-REVIEWED]Citations: 12+

Predicting Energy Consumption Patterns with Advanced Machine Learning Techniques for Sustainable Urban Development

Amjad, M., Rustam, F., Ashraf, I., Mehmood, A.

Sustainable Cities and Society (Elsevier)

Applies ensemble machine learning and deep learning models to predict urban energy consumption patterns from smart grid telemetry. The work contributes to sustainable urban development by enabling proactive demand forecasting and load balancing in smart city infrastructure.

#Energy Prediction#Machine Learning#Smart Cities#Sustainable Development#Demand Forecasting

View in Main Bibliography →

[LAB-AM]Industrial AI & Predictive Maintenance2024-09-01[PEER-REVIEWED]Citations: 8+

AI-Powered Fault Detection in Gas Turbine Engines: Enhancing Predictive Maintenance

Amjad, M., Alotaibi, A., Rustam, F., Ashraf, I.

Engineering Applications of Artificial Intelligence (Elsevier)

Presents an AI-driven fault detection framework for gas turbine engines using sensor fusion and anomaly detection models. The system enables predictive maintenance by identifying degradation signatures before critical failures, reducing unplanned downtime in industrial energy infrastructure.

#Predictive Maintenance#Fault Detection#Gas Turbines#Industrial AI#Anomaly Detection

View in Main Bibliography →

Laboratory Mission Statement

Wireless Networks, Cloud Security & IoT Systems Lab

Apply for Fellowship →All Publications

Prof. Mohammad Amjad

Professor

MANET RoutingAutomated TestingCloud SecurityIoT-Blockchain