Author
Contributions by role
Author 2
Dr. Qaisar Sohail
Department of Computer Science, University of Bari Aldo Moro, Bari (BA), Italy
Summary
Edited Journals
ICCK Contributions

Research Article | 23 July 2025
Optimizing Collaborative Task Allocation in Internet of Vehicles (IoV) through Blockchain-Enabled Incentive Mechanisms
ICCK Transactions on Sensing, Communication, and Control | Volume 2, Issue 3: 147-167, 2025 | DOI: 10.62762/TSCC.2025.962030
Abstract
The Internet of Vehicles (IoV) is a core component of smart transportation systems, making it feasible to exchange information among vehicles, infrastructure, and central systems in real time. However, the effective use of resources and the efficient distribution of tasks in these dynamic environments is a challenging task. This paper presents a blockchain-based collaborative task allocation framework method that can solve these problems by using a greedy algorithm for general task allocation and adopting a dynamic collaboration scheduling algorithm for emergent tasks. Employing the blockchain-based reward mechanism, the transparency, fairness, and security in dynamic mobile crowdsensing (MC... More >

Graphical Abstract
Optimizing Collaborative Task Allocation in Internet of Vehicles (IoV) through Blockchain-Enabled Incentive Mechanisms

Research Article | 19 May 2025
Optimizing Cloud Security with a Hybrid BiLSTM-BiGRU Model for Efficient Intrusion Detection
ICCK Transactions on Sensing, Communication, and Control | Volume 2, Issue 2: 106-121, 2025 | DOI: 10.62762/TSCC.2024.433246
Abstract
To address evolving security challenges in cloud computing, this study proposes a hybrid deep learning architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) for cloud intrusion detection. The BiLSTM-BiGRU model synergizes BiLSTM's long-term dependency modeling with BiGRU's efficient gating mechanisms, achieving a detection accuracy of 96.7% on the CIC-IDS 2018 dataset. It outperforms CNN-LSTM baselines by 2.2% accuracy, 3.3% precision, 3.6% recall, and 3.6% F1-score while maintaining 0.03% false positive rate. The architecture demonstrates operational efficiency through 20% reduced computational latency and 15% lower memory foo... More >

Graphical Abstract
Optimizing Cloud Security with a Hybrid BiLSTM-BiGRU Model for Efficient Intrusion Detection