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.

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.

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.

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, a dual-hop underlay cognitive relay network (CRN) with a best-relay selection (BRS) scheme under spectrum-sharing constraints from the primary user (PU) is investigated. The system model in this work consists of one PU, one secondary user (SU) and a few SU relays. Both users exchange the information using a half-duplex mode through amplify-and-forward (AF) SU relays. Moreover, all channels are modelled using the Nakagami-m fading model. In this work, the outage probabilities (OPs) are derived for the proposed system model under several scenarios to investigate the network performance under interference power constraint $I_p$ at the PU receiver. In addition, the impacts of the number of relays and the channel fading severity parameters are investigated as well. Furthermore, the system performance is investigated for several PU locations. The various numerical results are verified using a Monte Carlo simulation. Overall, the obtained results show that several factors such as the number of relays, channel fading severity parameters and PU location have a major impact on the outage performance of the SU. The simulation and analytical results are perfectly matched, confirming the accuracy of the analytical derivations.