-
Research Article
Finding Type of ARP Request That Introducing the MAC Address Table Instability Results in Network Sensitivity
Md. Abdullah Yusuf Imam,
Prodip Kumar Biswas*
Issue:
Volume 14, Issue 2, December 2025
Pages:
20-35
Received:
24 May 2025
Accepted:
16 June 2025
Published:
7 July 2025
Abstract: In network analysis, "looping" or "network loops" refers to situations where a path in a network returns to the same node or nodes multiple times, creating a closed circuit or cycle. In a loop, a single ARP (ARP or Address Resolution Protocol) is a networking protocol that translates Internet Protocol (IP) addresses to Media Access Control (MAC) addresses within a local area network (LAN). This can occur in various network contexts, including project management, computer networks, and electrical circuits. loops occur when a path traverses the same node twice or more. Looping in Computer Programming can be stated as a "loop" is a sequence of instructions that is repeatedly executed until a certain condition is met. This paper is trying to introduce the verities of looping criteria where the ARP is infected first and after that effect of its network smoothness, and also how it can be avoided is tries to show in Computer technology.
Abstract: In network analysis, "looping" or "network loops" refers to situations where a path in a network returns to the same node or nodes multiple times, creating a closed circuit or cycle. In a loop, a single ARP (ARP or Address Resolution Protocol) is a networking protocol that translates Internet Protocol (IP) addresses to Media Access Control (MAC) ad...
Show More
-
Research Article
Reliability Assessment of Distribution Network for Improved Feeder Bule Hora University, West Guji, Oromiya Regional State, Ethiopia
Kumilachew Chane*
Issue:
Volume 14, Issue 2, December 2025
Pages:
36-46
Received:
26 January 2025
Accepted:
18 June 2025
Published:
28 July 2025
Abstract: The knowledge of the reliability of distribution networks and systems is important consideration in the system planning and operations for development and improvements of power distribution systems. To achieve the target as minimum interruptions as possible to customers, utilities must strive to improve the reliability but at the same time reduce cost. It is a known fact that most of customer interruptions are caused by the failure in distribution system and the data and statistics were not easy to collect the reliability performance. The objective of the study was to examine the reliability distribution of networks and systems in the study area. In terms of methods, these studies used analytical methods to determine the reliability indices and effect of distribution substation configuration and network to the reliability indices performance. The key findings showed that there is always uncertainty associated with the distribution network reliability in the study area. The authors concluded that for evaluation and analysis of reliability, having data on the number and range of the examined piece of equipment, it is important to have database for failure rates, repair time and unavailability for each component in distribution network. Finally, the authors recommended that in order to improve the present status of the reliability distribution of network for improved feeder, training of workers, experts and customers strategies are very necessary. So that planners, policy makers, local communities, stakeholders and households need strong coordination to achieve the objective.
Abstract: The knowledge of the reliability of distribution networks and systems is important consideration in the system planning and operations for development and improvements of power distribution systems. To achieve the target as minimum interruptions as possible to customers, utilities must strive to improve the reliability but at the same time reduce c...
Show More
-
Research Article
Quantum-inspired Optimization for Efficient Vehicular Edge Computing Resource Allocation in Intelligent Transportation Systems
Issue:
Volume 14, Issue 2, December 2025
Pages:
47-58
Received:
29 June 2025
Accepted:
9 July 2025
Published:
28 July 2025
Abstract: The increasing adoption of smart mobility and connected vehicles necessitates significant improvements in underlying infrastructure, particularly in real-time data processing and decision-making. Vehicular Edge Computing (VEC) has emerged as a vital solution by enabling computation closer to data sources, thereby reducing latency and reliance on centralized cloud systems. However, efficient allocation of edge resources (processing power, bandwidth, and storage) remains a critical challenge due to the highly dynamic, decentralized nature of vehicular networks. Traditional optimization techniques often fall short under these conditions. This study explores a quantum-inspired optimization framework designed to enhance resource management in VEC environments by leveraging principles of quantum computing such as superposition and probabilistic state selection within classical hardware. Extensive simulations involving 10 vehicles and 3 edge servers were conducted to evaluate the framework's performance. The dynamic resource demand fluctuated between 7 and 18 units, and server utilization ranged from 0.2% to 1.4%, illustrating diverse operational conditions. The proposed quantum-inspired model showed superior efficiency, achieving up to 35% improvement in fitness gain compared to traditional algorithms, with convergence to optimal fitness in just 45 iterations. The solution space was explored effectively using quantum state amplitude representations, which improved solution diversity and robustness in decision-making. Furthermore, fairness in resource distribution was evaluated using Jain’s Fairness Index, yielding a high score of 0.914, demonstrating equitable allocation among vehicles. Additional results revealed that task completion times ranged from 1.5 to 3.5 seconds, with processing delays being the major contributor. The system exhibited sublinear scalability, performing well up to 50 vehicles but declining as the vehicle count increased to 200, indicating a need for further optimization strategies. Although the model operates in a classical environment without quantum hardware, it offers substantial performance benefits. This research highlights the potential of quantum-inspired optimization for real-time, fair, and scalable resource management in vehicular networks. Future work should incorporate real-world vehicular trace data, expand scalability tests, and explore integration with 5G and energy harvesting mechanisms. These advancements will further support intelligent, secure, and sustainable transportation systems driven by edge computing technologies.
Abstract: The increasing adoption of smart mobility and connected vehicles necessitates significant improvements in underlying infrastructure, particularly in real-time data processing and decision-making. Vehicular Edge Computing (VEC) has emerged as a vital solution by enabling computation closer to data sources, thereby reducing latency and reliance on ce...
Show More
-
Research Article
A Practical Study of Naive Bayes and Weighted Naive Bayes Cybersecurity Models for Wireless Networks
Issue:
Volume 14, Issue 2, December 2025
Pages:
59-70
Received:
14 August 2025
Accepted:
26 August 2025
Published:
15 September 2025
Abstract: Intrusion Detection Systems (IDS) are essential for protecting wireless networks against an increasingly complex range of cyber threats. This study evaluates and compares the effectiveness of Naïve Bayes, Weighted Naïve Bayes, and Convolutional Neural Network (CNN) using the Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD) dataset. The Naïve Bayes model was used as a baseline due to its simplicity and computational efficiency, delivering consistent results across multiple iterations. The Weighted Naïve Bayes model, which incorporated a feature-weighting mechanism was used to improve precision and Receiver Operating Characteristics-Area Under Curve (ROC-AUC) scores while striking a balance between performance and interpretability. These qualities make it well-suited for real-time intrusion detection in wireless environments, where both transparency and resource efficiency are crucial. The Naïve Bayes model was used as a baseline for offering a straightforward and efficient classification approach with moderate overall performance while the Weighted Naïve Bayes model was used to enhance the standard version by introducing a feature-weighting mechanism, which improved precision and reduced false positives. The CNN model outperformed the Naïve Bayes-based approaches across all evaluation metrics, underscoring its ability to learn complex patterns in network traffic. The Weighted Naïve Bayes model was also used to strike a practical balance between accuracy and efficiency, making it especially suitable for wireless networks. The CNN model was used to deliver the highest scores across all evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, reflecting its ability to learn complex patterns in the data. Experimental results demonstrated the potential of the Weighted Naïve Bayes model to support online learning and dynamic feature weighting, which is necessary for boosting adaptability to new and evolving attacks while preserving simplicity and transparency.
Abstract: Intrusion Detection Systems (IDS) are essential for protecting wireless networks against an increasingly complex range of cyber threats. This study evaluates and compares the effectiveness of Naïve Bayes, Weighted Naïve Bayes, and Convolutional Neural Network (CNN) using the Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD) ...
Show More
