The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone’s 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

Rapid growth of Internet of Things (IoT) devices dealing with sensitive data has led to the emergence of new access control technologies in order to maintain this data safe from unauthorized use. In particular, a dynamic IoT environment, characterized by a high signaling overhead caused by subscribers’ mobility, presents a significant concern to ensure secure data distribution to legitimate subscribers. Hence, for such dynamic environments, group key management (GKM) represents the fundamental mechanism for managing the dissemination of keys for access control and secure data distribution. However, existing access control schemes based on GKM and dedicated to IoT are mainly based on centralized models, which fail to address the scalability challenge introduced by the massive scale of IoT devices and the increased number of subscribers. Besides, none of the existing GKM schemes supports the independence of the members in the same group. They focus only on dependent symmetric group keys per subgroup communication, which is inefficient for subscribers with a highly dynamic behavior. To deal with these challenges, we introduce a novel Decentralized Lightweight Group Key Management architecture for Access Control in the IoT environment (DLGKM-AC). Based on a hierarchical architecture, composed of one Key Distribution Center (KDC) and several Sub Key Distribution Centers (SKDCs), the proposed scheme enhances the management of subscribers’ groups and alleviate the rekeying overhead on the KDC. Moreover, a new master token management protocol for managing keys dissemination across a group of subscribers is introduced. This protocol reduces storage, computation, and communication overheads during join/leave events. The proposed approach accommodates a scalable IoT architecture, which mitigates the single point of failure by reducing the load caused by rekeying at the core network. DLGKM-AC guarantees secure group communication by preventing collusion attacks and ensuring backward/forward secrecy. Simulation results and analysis of the proposed scheme show considerable resource gain in terms of storage, computation, and communication overheads.

The rapid growth of Internet of Things (IoT) technology offers huge opportunities and also brings many new challenges related to the authentication in (IoT) devices. Using passwords or pre-defined keys have drawbacks that limit their use for different (IoT) applications like smart hotel and smart office. In fact, they didn't provide temporary access to data in such reservation systems. Thus, authenticating users basing on password mechanism is not feasible. In this paper, we propose a new Token-Based Lightweight User Authentication (TBL UA) for (IoT) devices, which is based on token technique in order to enhance the robustness of authentication. Security analysis shows the security strength of the proposed scheme such as token security, Perfect Forward Secrecy (PFS), etc. In addition, the presented performance analysis shows that it is a strong competitor among existing ones for user authentication in (IoT) environments.

This paper presents an analysis of the impact of intentional electromagnetic (EM) interferences on the performance of Wi-Fi communications. The goal is to analyze the susceptibility of orthogonal frequency-division multiplexing (OFDM) signals and to understand how certain parameters of the interference signal act on the degradation of the communication, taking into account the signal processing performed at the reception stage of the communication system. We analyze the relationship between the parameters of the interference and the signal processing at the reception. This study is a contribution for agile communication solutions that would have the ability to reconfigure the signal processing in order to reduce the impact of interference signals. The scenario corresponds to an IEEE 802.11n wireless network affected by a frequency-sweeping interference signal. The interference signal sweeps over a given frequency band, and different sweep periods and interference to signal power ratios are applied. A set of measurements was performed to evaluate the performance of the communication network in the presence of different EM interferences. Bit rate measurements are presented and the impact of the interference characteristics on communication quality is analyzed, by highlighting the effect of the signal processing performed by the OFDM receiver.