Integrated sensing and communication (ISAC) is a rapidly evolving and highly promising technology for wireless networks, enabling the concurrent execution of communication and sensing functions within constrained spectrum resources. This paper examines a secure ISAC system assisted by an uncrewed aerial vehicle (UAV), in which the extended Kalman filter is incorporated for accurate estimation and prediction of eavesdropper states. Radar signals are transmitted to track and jam potential eavesdroppers while communication services are simultaneously provided to ground users. Under practical constraints—including maximum UAV velocity, transmit power, and propulsion energy consumption—the achievable uplink rate is maximized through joint optimization of the transmit beamforming and UAV trajectory. To address the computationally complex and inherently non-convex optimization problem, an efficient iterative algorithm combining block coordinate descent with successive convex approximation is designed, yielding a high-quality suboptimal solution. Finally, comprehensive simulations are conducted to evaluate the effectiveness of the proposed scheme against benchmark methods and to elucidate the inherent trade-off between communication and sensing performance.

Integrated sensing and communication (ISAC), an emerging technology for wireless networks, enables efficient use of scarce spectrum resources by supporting simultaneous communication and sensing. This work investigates the secrecy performance of an uncrewed aerial vehicle (UAV)-assisted ISAC system, in which a UAV operates as an aerial base station to serve multiple ground users while simultaneously transmitting sensing signals to detect potential eavesdroppers. To maximize the average secrecy rate, we jointly optimize the user scheduling, transmit beamforming, receive filtering vector, and the UAV’s flight trajectory. The formulated optimization problem is decomposed into four subproblems, which are transformed into convex forms via successive convex approximation, and is solved iteratively using the block coordinate descent method. Simulation results demonstrate the correctness and effectiveness of the proposed scheme.

Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we investigate the performance of an uncrewed aerial vehicle (UAV) -assisted secure integrated sensing, communication, and computing system, where the UAV sends radar signals to locate and suppress a potential eavesdropper while providing offload services to ground users (GUs). Considering the constraints of UAV maximum speed, transmit power, propulsion energy, data transmission, and computation time, the total energy consumption of GUs is minimized by jointly optimizing user offloading ratio, user scheduling strategy, transmit beamforming, and UAV trajectory. An efficient iterative optimization algorithm is proposed to solve the nonconvex optimization problem caused by tightly coupled dependent variables. In particular, the original optimization problem is decomposed into four suboptimization problems, and the nonconvex subproblems are transformed into approximately convex forms via successive convex approximation. Then, all subproblems are solved successively by using the block coordinate descent technique. Numerical results demonstrate the convergence and validate the effectiveness of the proposed algorithm.

In this paper, we investigate the performance of the energy harvesting (EH) concept in full-duplex multiple-input-single-output cognitive radio networks (FD-MISO-CRNs). In the proposed model, a single secondary user (SU) harvests energy from a hybrid base station (HBS) and the primary user (PU) transmission on the downlink and transmits its data on the uplink over Nakagami-m fading channels. Assuming underlay CRN, the total SU transmitted power (i.e., data and energy) cannot exceed the maximum allowable PU interference threshold. In practice, the HBS might have no prior knowledge on the SU battery status. A closed-form expression for the SU exact outage probability is derived and simplified to its asymptotic formula for the high signal-to-noise-ratio (SNR) regime. Based on the derived expressions, a power allocation optimization problem is formulated to minimize the SU asymptotic outage probability. Simulation results are obtained to validate the derived analytical formulas. Moreover, The proposed power allocation model shows a significant enhancement of the system performance compared to conventional equally distributed power allocation model.

Conventional electricity grids evolve into smart grids by incorporating Information and Communications Technology (ICT) services. These services are meant to provide finegrained monitoring of the grid and pervasive control over its components. The ultimate goal is to avoid major failures and blackouts and to improve the efficiency of energy generation, transmission, and consumption. Smart grids support two-way flow of information and energy between the supply and demand sides at various levels from generation sites down to individual consumers. A smart grid can therefore potentially contain millions of intelligent devices able to collect, process and communicate data. These devices need to be monitored and configured to ensure proper operation. This is the role of a network management protocol which enables communication between a network management system and grid devices. Currently no protocol is widely accepted as a management protocol for smart grid devices. We, therefore, assess the use of some standard Internet protocols as potential candidates for this job. Namely, we consider the Simple Network Management Protocol (SNMP), the Hyper Text Transfer Protocol (HTTP) and the Constrained Application Protocol (CoAP). We compare these protocols according to a set of criteria that we propose. Also, we test implementations of all three protocols over the Arduino hardware platform.

Cell selection algorithms are considered one of crucial features in LTE-Heterogeneous Networks (HetNets). Due to different downlink transmit power levels and randomness deployment of femtocells, achieving better user throughput and reducing the necessity of dynamic load balancing techniques require appropriate algorithms for selecting optimum serving cell. In this work, a new cell selection algorithm is proposed to enable new user to select best serving cell that achieves maximum effective achievable data rate. A new prediction algorithm is designed within the new proposed cell selection algorithm to predict the performance of Proportional Fair (PF) scheduling algorithm without running it after every Resource Block (RB), to calculate the expected degradation in theoretical new user's achievable data. The numerical results show that the new cell selection proposed algorithm achieves higher average cell throughput than conventional cell selection methods and maintains better balanced load between different adjacent cells.