https://re.public.polimi.it/bitstream/11311/1140135/1/PreprintSubmitted.pdf

Surveying pervasive public safety communication technologies in the context of terrorist attacks

The wireless sensor networks is a new technology for information acquisition and processing, which includes the techniques of sensor, computer, Internet of Things and network. Due to the fragility of wireless sensor networks, the security issue has become a prevalent concern in the wireless communication for Internet of Things. This paper offers a deep insight to the secrecy capacity of wireless sensor network and a calculable threshold of capacity based on the amplify-and-forward (AF) compressed sensing scheme. Moreover, we provide a feasible algorithm based on augmented Lagrange method for source reconstruction for the legitimate nodes and un-authorized nodes. Furthermore, we also discuss the impact of the numbers of active sensor nodes, relay nodes and eavesdropper nodes to the secrecy capacity. Simulation results demonstrate the correctness of the derived secrecy capacity.

/pdf/study-on-secrecy-capacity-of-wireless-sensor-networks-in-1kn014kg41.pdf

Study on Secrecy Capacity of Wireless Sensor Networks in Internet of Things Based on the Amplify-and-Forward Compressed Sensing Scheme

As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service. Unfortunately, most conventional wireless positioning technologies are vulnerable to jamming attacks due to inherent shortcomings like weak signal strength and unfavorable anchor geometry. Thanks to their high operational flexibility, unmanned aerial vehicles (UAVs) could be a promising solution to the above challenges. Therefore, in this article, we propose a UAV-assisted anti-jamming positioning system, in which multiple UAVs first utilize time-difference-of-arrival (TDoA) measurements from ground reference stations and double-response two-way ranging (DR-TWR) measurements from UAV-to-UAV links to perform self-localization as well as clock synchronization, and then act as anchor nodes to provide TDoA positioning service for ground users in the presence of jamming. To evaluate the feasibility and performance of the proposed system, we first derive the Cramer-Rao lower bound (CRLB) of UAV self-localization. Then, the impacts of UAV position uncertainty and synchronization errors caused by jamming on positioning service are modeled, and the theoretical root-mean-square error (RMSE) of user position estimate is further derived. Numerical results demonstrate that the proposed system is a promising alternative to existing positioning systems when their services are disrupted by jamming. The most notable advantage of the proposed system is that it is fully compatible with existing user equipment terminals and positioning methods.

/pdf/feasibility-study-of-uav-assisted-anti-jamming-positioning-51s3pn1mqu.pdf

Feasibility Study of UAV-Assisted Anti-Jamming Positioning

Performance of global navigation satellite systems (GNSSs) mounted on aerial platforms could be degraded by the presence of jamming or spoofing threats. Detection of jamming and spoofing is essential considering practical applications of satellite navigation in passenger aircrafts, unmanned aerial vehicles (UAVs), helicopters and fighters. Different algorithms and methods have been proposed for detection of these threats; however, their usage has many limitations because of their demanding weight, size and computational complexity, when embedded on aerial systems. In this study, the authors develop a theoretical framework to detect the presence of the threat of UAVs. The idea is based on the fact that, due to the UAV motion, the samples of received signal power from a fixed threat and from a GNSS satellite have different empirical probability density functions. Moreover, by using two antennas (an omnidirectional and a down-tilted-directional), they introduce a new method to distinguish between aerial and ground-based threats. The proposed algorithms have a low-computational burden and can consider the fading loss as well. Simulation results show the superior performance of the proposed methods, in terms of detection and false alarm probability, compared to the existing methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2020.0285

Low-complexity framework for GNSS jamming and spoofing detection on moving platforms

Unmanned aerial vehicle (UAV) networks are vulnerable to jamming attacks because of the high mobility, limited battery and scarce spectrum resources of UAVs. In this paper, we propose a reinforcement learning based UAV relay scheme to improve the anti-jamming capability and save energy consumption of the UAV network. Based on the real-time channel conditions and the historical relay experiences, the proposed scheme enables UAVs to improve the policy of relay power and strategies without knowing the UAV network and channel model. Simulation results show that the proposed UAV relay scheme reduces the bit error rate of the messages and reduces the energy consumption of the UAV network compared with the state-of-the-art benchmark.

Energy Efficient Relay in UAV Networks Against Jamming: A Reinforcement Learning Based Approach

Unmanned aerial vehicle (UAV) has been widely used in various fields, which mainly relies on global navigation satellite system (GNSS) to obtain position information. However, fast-moving jamming poses a big challenge for UAV navigation. This motivates us to develop new robust adaptive beamforming technique that is capable of enhancing the navigation signal and suppressing the jamming efficiently. Since the navigation signal and jamming present non-Gaussianity in statistics, the minimum dispersion distortionless response (MDDR) beamformer is utilized, which exhibits better performance of non-Gaussian signal reception over the minimum variance-based beamformer. To suppress the fast-moving jamming, the minimum power constraint is added to the optimization problem regarding to the MDDR beamformer, leading to a resultant problem with a quadratic constraint. Furthermore, instead of the quadratic constraint, a linear version is derived under the power constraint to achieve a shape null sector towards the fast-moving jamming, and as a consequence, a linearly constrained MDDR beamforming is established. This allows us to develop two algorithms, i.e., quasi-MVDR algorithm and Newton's method, to accelerate the solution. Simulation results show that the proposed beamformer is able to suppress the fast-moving jamming efficiently for UAV navigation.

Fast-Moving Jamming Suppression for UAV Navigation: A Minimum Dispersion Distortionless Response Beamforming Approach

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix $\mathbf {\Phi }$ is utilized to sample a sparse signal, while sensing matrix $\mathbf {\Psi }$ is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between $\mathbf {\Phi }$ and $\mathbf {\Psi }$ while keeping the columns of $\mathbf {\Psi }$ highly correlated to their counterparts of $\mathbf {\Phi }$ . Simulation results are presented to verify the effectiveness of the proposed methods.

Jingwei Yin

Papers

Fast-Moving Jamming Suppression for UAV Navigation: A Minimum Dispersion Distortionless Response Beamforming Approach

Sensing Matrix Design for MMV Compressive Sensing: An MVDR Approach