PELE: Power efficient legitimate eavesdropping via jamming in UAV communications

Wang, Xiaoming; Li, Kai; Kanhere, Salil S.; Li, Demin; Zhang, Xiaolu; Tovar, Eduardo

doi:10.1109/iwcmc.2017.7986320

Cited by 15 publications

(11 citation statements)

References 21 publications

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“…However, only a few research works consider this scenario. To the best of our knowledge, only [16] and [17] discussed the PLS transmission with UAV wiretappers.…”

Section: A Motivationmentioning

confidence: 99%

Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

Wang

Zhang

et al. 2020

IEEE Trans. Commun.

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The deployment of unmanned aerial vehicle (UAV) for surveillance and monitoring gives rise to the confidential information leakage challenge in both civilian and military environments. The security and covert communication problems for a pair of terrestrial nodes against UAV surveillance are considered in this paper. To overcome the information leakage and increase the transmission reliability, a multi-hop relaying strategy is deployed. We aim to optimize the throughput by carefully designing the parameters of the multi-hop network, including the coding rates, transmit power, and required number of hops. In the secure transmission scenario, the expressions of the connection probability and secrecy outage probability of an end-to-end path are derived and the closed-form expressions of the optimal transmit power, transmission and secrecy rates under a fixed number of hops are obtained. In the covert communication problem, under the constraints of the detection error rate and aggregate power, the sub-problem of transmit power allocation is a convex problem and can be solved numerically. Simulation shows the impact of network settings on the transmission performance. The trade-off between secrecy/covertness and efficiency of the multi-hop transmission is discussed which leads to the existence of the optimal number of hops. Index TermsPhysical layer security, unmanned aerial vehicle, covert communication, multi-hop network. DRAFT 6 the two joint distributions under different hypotheses. The optimization of transmit power at each hop turns to be a convex optimization problem and could be solved numerically. The expression of the optimal transmission rate is also derived, and the optimal number of hops is evaluated.Note that though there exist some differences between the secrecy and covert communications in problem construction and solving procedures, the considered problems in secrecy and covert communication parts are closely related. First, both problems aim to guarantee the transmission security in the multi-hop networks. Secrecy communication prevents information being demodulated and covert communication conceals the transmission from being detected by the UAV.Second, there exists a trade-off between the security and efficiency in both secrecy and covert communications. The increase of number of hops will degrade UAV's wiretapping and detecting ability and result in the increase of transmission rate, but more time for receiving information will be required and more chances for wiretapping/detecting signals will be provided. In addition, both problems maximize the end-to-end throughput, and the opposite influence of transmission rate on connection probability and the coding rate which are two important component of the throughput leads to the existence of optimal secrecy rate in secrecy communication and transmissions rate in covert communication. C. Related WorksMore recently, PLS and covert communications have also been extended to multi-hop relaying systems [33], [35]-[37]. Specifically, in [33], the optimal pa...

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“…However, only a few research works consider this scenario. To the best of our knowledge, only [16] and [17] discussed the PLS transmission with UAV wiretappers.…”

Section: A Motivationmentioning

confidence: 99%

Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

Wang

Zhang

et al. 2020

IEEE Trans. Commun.

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“…where P z i (t) is the transmit power of device i, and σ 2 0 is noise power at the UAV. In particular, for illustration convenience, we consider a special case of the Nakagami-m model in this paper where m = 1 [35,36,37]. Note that the proposed deep reinforcement learning approach is generic, and can work with other Nakagami fading channel model with any m values.…”

Section: System Modelmentioning

confidence: 99%

On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

Li¹,

Tovar³

et al. 2019

IEEE Trans. Veh. Technol.

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Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of sensing devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that can enlarge the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that the proposed deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.

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“…We have previously discussed the first approach to eavesdrop suspicious communication link by jamming UAV SR , as shown in Figure 2a [13], so this paper mainly focuses on the other three eavesdropping and jamming cases, as shown in Figure 2b–d. In practice, UAV’s trajectory period depends on the battery charge.…”

Section: Introductionmentioning

confidence: 99%

Eavesdropping and Jamming Selection Policy for Suspicious UAVs Based on Low Power Consumption over Fading Channels

Wang

Guo

et al. 2019

Sensors

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Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV’s eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.

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PELE: Power efficient legitimate eavesdropping via jamming in UAV communications

Cited by 15 publications

References 21 publications

Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection

Eavesdropping and Jamming Selection Policy for Suspicious UAVs Based on Low Power Consumption over Fading Channels

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