Spectrum congestion has motivated the recent introduction of integrated sensing and communication (ISAC) systems where radar and communications functionalities co-exist and share the same spectrum. This is accompanied by extensive research in the area of reconfigurable intelligent surfaces (RIS), thanks to their ability to improve the ISAC systems. In this paper, the physical layer security of a multiuser multiple-input single-output (MU-MISO) ISAC system is investigated when the system is subject to eavesdropping by a malicious unmanned aerial vehicle (UAV). In particular, we propose using an active RIS to maximize the achievable secrecy rate of the system through jointly designing the radar receive beamformers, the active RIS reflection coefficients matrix and the transmit beamformers at the dual-function base station of the ISAC system. This is done while taking into account a minimum radar detection signal-to-noise ratio (SNR) and total system power budget constraints. The resulting non-convex optimization problem is tackled by exploiting fractional programming (FP) and majorization-minimization (MM) techniques to achieve a solution. Our numerical results show the superiority of active RIS to achieve the goal when compared with passive RIS or with an ISAC without an RIS. Results also show that passive RIS could still be a viable solution to achieve this goal but at the expense of a much larger needed size especially when lower power budgets are available.

In this paper, we study radio resource allocation for multicasting in OFDMA based high altitude platforms (HAPs). We formulate and solve an optimization problem that finds the best allocation of HAP resources such as radio power, sub-channels, and time slots. The problem also finds the best possible frequency reuse across the cells that constitute the service area of the HAP. The objective is to maximize the number of user terminals that receive the requested multicast streams in the HAP service area in a given OFDMA frame. A bounding subroutine in a branch and bound algorithm can be obtained by decomposing it into two easier subproblems, due to its high complexity, and solving them iteratively. Subproblem 1 turns out to be a binary integer linear program of no explicitly noticeable structure and therefore Lagrangian relaxation is used to dualize some constraints to get a structure that is easy to solve. Subproblem 2 turns out to be a linear program with a continuous knapsack problem structure. Hence a greedy algorithm is proposed to solve subproblem 2 to optimality. The subgradient method is used to solve for the dual variables in the dual problem to get the tightest bounds.

Intelligent reflecting surfaces (IRS) are considered one of the prominent technologies to be adopted in 6G and beyond networks. However, one of the main IRS limitations is restricting the communication to the reflective dimension, which means that users located behind the IRS surface cannot benefit from it. In this paper, we make use of intelligent omni-surfaces (IOS), as an alternative to IRS, in addition to unmanned aerial vehicles (UAV) to achieve secure communication in an Internet-of-Things (IoT) communication system. Specifically, we propose IOS-UAV assisted communication wherein an access point (AP) tries to send confidential information to a legitimate IoT device in presence of an eavesdropper. The UAV serves two fold; it acts as a friendly jammer that is trying to degrade the signal quality at the eavesdropper and as a source of energy to power up the IoT device via simultaneous wireless information and power transfer (SWIPT) to address the limited power available to it. We formulate an average secrecy rate maximization problem, which jointly optimizes the AP and UAV transmission powers, the UAV trajectory, IOS phase shifts and power splitting factor. The formulated problem is non-convex, therefore we exploit successive convex approximation (SCA) to tackle this issue. Simulation results show that the iterative SCA solution converges rapidly. Besides, the proposed scheme outperforms the traditional system without IOS by a gap of 140% in terms of the secrecy capacity.

Blockchain has been forming the central piece of various types of vehicle-to-everything (V2X) network for trusted data exchange. Recently, permissioned blockchains garner particular attention thanks to their improved scalability and diverse needs from different organizations. One representative example of permissioned blockchain is Hyperledger Fabric (Fabric). Due to its unique execute-order procedure, there is a critical need for a client to select an optimal number of peers. The interesting problem that this paper targets to address is the tradeoff in the number of peers: a too large number will degrade scalability while a too small number will make the network vulnerable to faulty nodes. This optimization issue gets especially challenging in V2X networks due to mobility of nodes: a transaction must be executed, and the associated block must be committed before the vehicle leaves a network. To this end, this paper proposes a mechanism for selecting an optimal set of peers based on reinforcement learning (RL) to keep a Fabric-empowered V2X network impervious to dynamicity due to mobility. We model the RL as a contextual multi-armed bandit (MAB) problem. The results demonstrate the outperformance of the proposed scheme.

Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.

There is an urgent need to support high data rate among connected vehicles for both safety and infotainment purposes. However, high rate for vehicular communications is impacted by several challenges such as optimal beam tracking for multiple simultaneous-transmitting vehicles in highly-dynamic environments. In this paper, we aim to maximize the overall network throughput for multi-vehicular communications. We propose a reinforcement learning (RL) approach called combinatorial multi-armed bandit (CMAB) framework, where multiple arms (i.e., actions) form a super arm and can be played together, to handle the beam selection problem in a vehicular network. More specifically, we propose an adaptive combinatorial Thompson sampling algorithm, namely adaptive CTS, that CMAB embodies for appropriate selection of simultaneous beams in a high-mobility vehicular environment. The proposed approach applies the smart exploration-exploitation trade-off for the fast selection of beams. As the proposed adaptive CTS scheme produces a higher complexity over the search space which increases exponentially with the number of user, we also propose a sequential Thompson sampling (TS) algorithm where beam selection is performed in a one-by-one manner. We analyze the regret bounds for the proposed beam tracking algorithms. The performance of the proposed strategies are evaluated for multi-vehicle millimeter-wave (mmWave) simulation environments. Our results suggest that the proposed sequential approach performs almost similar to the simultaneous adaptive CTS scheme for tracking optimal beams in a multi-vehicular network with much reduced complexity. Simulation results also show that both of our proposed strategies approach the optimal achievable rate achieved by the genie-aided solution.