Mohammad Shurman

Unmanned Aerial Vehicles (UAVs) have proliferated across diverse domains. However, optimal UAV operations necessitate precise and reliable navigation systems. UAVs predominantly rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS), for navigation. Nevertheless, GNSS signals are susceptible to blockage, reflection, and spoofing, introducing significant risks, including navigation loss and potential UAV loss. This research investigates cutting-edge navigation solutions, emphasizing deep learning-based visual localization approaches tailored for UAVs. Our focus is on scenarios characterized by GPS-denied environments where GPS signals may be absent or unreliable. We provide a comprehensive review of contemporary deep learning-based visual localization approaches and compare them to traditional aerial visual localization methods, such as template matching and feature matching. This comparison highlights both the potential benefits and challenges associated with these approaches. Furthermore, we systematically evaluate and classify recent deep learning-based methods based on main criteria, including model type/architecture, reference imagery, operational context, and resultant accuracy levels. Our findings underscore the substantial promise inherent in various approaches while also shedding light on their unique deployment challenges. Finally, we discuss potential research directions, to inspire further innovations and progress in this domain. The ultimate goal is to develop more accurate, dependable, and secure navigation solutions for UAVs.

Recently, unmanned aerial vehicles (UAVs) have emerged to enhance data processing, network monitoring, disaster management and other useful applications in many different networks. Due to their flexibility, cost efficiency and powerful capabilities, combining these UAVs with the existing wireless sensor networks (WSNs) could improve network performance and enhance the network lifetime in such networks. In this research, we propose a task offloading mechanism in UAV-aided WSN by implementing a utility-based learning collaborative algorithm that will enhance the service satisfaction rate, taking into account the delay requirements of the submitted tasks. The proposed learning algorithm predicts the queuing delays of all UAVs instead of having a global overview of the system, which reduces the communication overhead in the network. The simulation results showed the effectiveness of our proposed work in terms of service satisfaction ratio compared with the non-collaborative algorithm that only processes the task locally in the WSN cluster.

Researchers have developed the development of a Heterogeneous Cloud Radio Access Network (HCRAN) consists of a Small Remote Radio Head (S-RRH) for the secondary users (Small Cell Users SUEs) and macro RRH for the primary users (multiple macrocell users MUEs), to meet the continuous increase in the number of mobile phone users and the change in traffic patterns. However, H-CRAN layers may overlap each other. Researches were able to reduce internal interference between RRHs through central collaborative processing in the BBU group. Layer interference between small RRHs cells (S-RRHs) and RRH Macrocell (RRH macros) was resolved by proposing a framework to reduce interference between Microcell networks and small cellular networks by suggesting a two-way collaborative algorithm. But they assumed that the channel status information (CSI) was available. This assumption makes no sense in the wireless communication environment, due to the possible interruption of specific ranges, which results in devices not obtaining or delaying the information. The study was also limited to fixed users or infantry subjects. They did not predict the movement of people moving at high speeds. In this paper, we will use the frequency hopping (FH) orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) modulation to overcome an inaccurate or unknown CSI problem, In addition to the ability to predict the movement of users who move between cells, and provide services within the cell in which the user is located.

Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput.

The zero-day attack in networks exploits an undiscovered vulnerability, in order to affect/damage networks or programs. The term “zero-day” refers to the number of days available to the software or the hardware vendor to issue a patch for this new vulnerability. Currently, the best-known defense mechanism against the zero-day attacks focuses on detection and response, as a prevention effort, which typically fails against unknown or new vulnerabilities. To the best of our knowledge, this attack has not been widely investigated for Software-Defined Networks (SDNs). Therefore, in this work we are motivated to develop anew zero-day attack detection and prevention mechanism, which is designed and implemented for SDN using a modified sandbox tool, named Cuckoo. Our experiments results, under UNIX system, show that our proposed design successfully stops zero-day malwares by isolating the infected client, and thus, prevents these malwares from infesting other clients.

In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.

With the wide‐spread wireless applications and services, the spectrum scarcity has become a challenging problem. Thus, it is necessary to find a solution to have more spectrum bands. One from these solutions is the cognitive relay network, where we can increase the spectrum utilization by combining the cognitive radio (CR) with cooperative spectrum sharing systems. In this paper, the performance of cognitive relay networks under interference power constraints from the primary user (PU) is studied. Here, the system models are studied by utilizing different scenarios. The first scenario is by using a single amplify‐and‐forward (AF) relay, and the second one is by using a single AF relay with selection diversity. Tabulated expressions for the outage probability (OP) are derived over independent and non‐identically distributed Nakagami‐m fading channels. Numerical results show that the cooperative spectrum sharing transmission with a single AF relay and selection diversity outperforms both the direct transmission and the transmission using a single AF relay for the underlay cognitive relay networks. Furthermore, the results show that the outage performance depends on the minimum fading severity parameters of the two hops of the secondary user (SU) and the OP decreases as the PU moves away from the SU network.

In this paper, the outage performance is investigated for cognitive dual-hop relay networks with amplify-and-forward (AF) relay and selection combining (SC) receiver at the destination under spectrum sharing constraints on primary user (PU) over independent and identically distributed (i.i.d.) Nakagami-m fading channels. We derive an exact closed-form expression for the outage probability (OP) of the considered system under the peak interference power I_p at the primary user, and the impact of the PU location on the OP performance is studied also. Moreover; the effects fading severity for the two hops on the OP is investigated also for this cognitive relay network (CRN).