Basim Mahmood

The recent extensive damage to the Iraqi Power Grid (IPG) infrastructure has brought significant challenges to power supply and demand. It has resulted in a substantial decrease in power supply, reaching up to 17,000 MVA, while the actual demand stands at 27,000 MVA. The gap between power production and power supply is considered one of the challenges of the Iraqi government. One of the solutions to fill this gap is expanding the power grid. However, the expansion of the power grid is subject to many factors that should be taken into considerations. One of these factors is the structure of the grid after performing the expansion (e.g., adding power stations), which should maximize the performance of the network. In this work, various scenarios have been put forth to improve the performance of IPG. Each proposed scenario aims to address a specific challenge that might be encountered in the network. The scenarios are visualized using concepts inspired by Complex Networks and then analyzed and benchmarked. Several network metrics have been involved in each scenario for performance evaluation. The results showed that the proposed scenarios are feasible to be adopted and can improve the whole performance of the IPG network model.

The spread of handheld mobile devices integrated with multiple sensors makes it easy for these devices to interact with each other. These interactions are useful in a variety of applications such as monitoring and notification systems that can be adopted in smart campuses. The performance of these applications depends primarily on the network infrastructure and network protocols. In cases of failure, smart campus requires the provision of effective alternatives that can handle essential services. Hence, this work uses the Wi-Fi mobile ad hoc network (MANET) as an alternative backup to the traditional infrastructure. The dynamic nature of such a network relies on individuals' mobility, this leads to a lack of end-to-end connectivity. To overcome this challenge, delay-tolerant networking (DTN) has been adopted as its primary approach to routing information inside campus. Spray and wait, binary spray and wait (BSW), and probabilistic flooding protocols are deeply assessed to ensure sustained communications in the working environment. The protocols’ parameters are comprehensively investigated and optimized. Moreover, the performance metrics that are used in the evaluation are messages consumption, node responsiveness, and coverage. The findings showed that the optimal protocol and its parameters is reliant upon the specific application and resources available.

This work quantifies the severity and likelihood of cyberattacks using complex network modelling. A dataset from common attack pattern enumerations and classifications (CAPEC) is collected and formalized as nodes and edges aiming at creating a network model. In this model, each attack pattern is represented as a node, and an edge is created between two nodes when there is a relation between them. The dataset includes 559 attack patterns and 1921 relations among them. Network metrics are used to perform the analysis on the network level and node level. Moreover, a rank of the CAPECs based on a complex network perspective is generated. This rank is compared with the CAPEC ranking system and deeply discussed based on cybersecurity perspective. The findings show interesting facts about the likelihood and severity of attacks. It is found that the network perspective should be given attention by the CAPEC ranking system. Finally, the results of this work can be of high interest to security architects.

Drone networks have been recently utilized in a wide range of real-life applications due to their efficient feasibility in performing tasks with minimal cost and effort. However, the most frequent issue with drones is the energy consumption due to their limited battery life. Most of the efforts in the literature try to minimize the energy consumption and flying time. Moreover, obstacle avoidance is another issue for drones during operation. However, to overcome these issues, most of the methods in the literature are developed based on standard algorithms. Hence, this work suggested a novel approach for drone trajectory path planning that combines the concepts of spiral and boustrophedon algorithms. Also, concepts inspired by complex networks are involved in the proposed approach for modeling a real-life environment. The proposed approach is evaluated under different flying heights using metrics such as power and time consumption, coverage, and obstacle avoidance. The findings show that the proposed approach outperforms other standard algorithms in the literature.

The efficiency and adaptability of drone operations depend heavily on two critical components: path planning and task scheduling. While the literature provides extensive research on these algorithms independently, there is a severe lack of studies addressing their combined impact on drone performance. Hence, this study aims to bridge this gap by developing a comprehensive framework that integrates three path planning algorithms (Spiral, Boustrophedon, and Hybrid) with four task scheduling algorithms (FirstCome First-Served (FCFS), Shortest Processing First (SPF), Earliest Deadline First (EDF), and Priority). The hybrid path planning algorithm is proposed for this work. The framework evaluates each combination's performance based on key metrics, including elapsed time and energy consumption. A virtual simulation environment is designed and implemented for the sake of this study. The results show that combining the SPF scheduling algorithm with Hybrid path planning offers the best balance between time efficiency and energy consumption. The Boustrophedon path planning method shows the highest elapsed times and is generally less efficient than Hybrid and Spiral.

The current revolution in technology makes data a crucial part of real-life applications due to its importance in making decisions. In the era of big data and the massive expansion of data streams on Internet networks and platforms, the process of data collection, mining, and analysis has become a not easy matter. Therefore, the presence of auxiliary applications for data mining and gathering has become a necessary need. Usually, companies offer special APIs to collect data from particular destinations, which needs a high cost. Generally, there is a severe lack in the literature in providing approaches that offer flexible, low, or free of cost tools for web scraping. Hence, this article provides a free tool that can be used for data mining and data collection purposes from the web. Specifically, an efficient Google Scholar web scraper is introduced. The extracted data can be used for analysis purposes and making decisions about an issue of interest. The proposed scraper can also be modified for crawling web links and retrieving specific data from a particular website. It can also formalize the collected data as a ready dataset to be used in the analysis phase. The efficiency of the proposed scraper is tested in terms of the time consumption, accuracy, and quality of the data collected. The findings showed that the proposed approach is highly feasible for data collection and can be adopted by data analysts.

The rapid advancement of technology has led to the generation of vast amounts of complex data, making it difficult to understand and analyze. Traditional 2D data visualization methods have limitations, and network models with nodes and edges have emerged as a more efficient way to analyze large-scale data. However, with the emergence of virtual reality (VR) technology and the metaverse concept, there is an opportunity to visualize and analyze data in an interactive 3D way. This paper investigates the integration of VR technology and complex networks to create interactive data visualization models. A 3D VR-based interactive biological network model was designed and implemented for the sex chromosomes' gene-gene interactions, which proved its efficiency in terms of usability when tested by experts. The developed model enables analysts to immerse themselves in the data and interact with its objects, leading to more professional, deep, and accurate analysis.

The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents’ matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use.