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Mobile Networks and Applications

Secrecy Performance in the Internet of Things: Optimal Energy Harvesting Time Under Constraints of Sensors and Eavesdroppers

Van Nhan Vo, Tri Gia Nguyen, Chakchai So–In, Hung Tran, Surasak Sanguanpong

Published Feb 5, 2019• 4 citations
Publisher
DOI
10.1007/s11036-019-01217-7

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One of the key enablers of 6th-generation (6G) wireless networks is cognitive radio, offering optimized spectrum utilization, enhanced device intelligence, and improved security. This article investigates secure communication in an energy-harvesting (EH) cognitive Internet of Things (CIoT) network operating over cascaded fading channels. Here, a CIoT transmitter employs an intelligent strategy to allocate time for simultaneous wireless information and power transfer (SWIPT) and transmission power to maximize the secrecy rate. The CIoT receiver operates in full-duplex (FD) mode, receiving confidential messages while simultaneously emitting cooperative jamming signals to disrupt an eavesdropper. We formulate the challenging non-convex optimization problem to maximize secrecy capacity while ensuring spectrum sharing and energy constraints and model the CIoT agent’s decision-making as a model-free Markov decision process (MDP). We then propose a deep reinforcement learning (DRL) approach to determine the optimal strategy for secure transmission and resource allocation. Specifically, we derive the instantaneous secrecy rate and employ a deep deterministic policy gradient (DDPG) algorithm with a lightweight actor-critic architecture to efficiently address dynamic channel occupancy, EH opportunities, and fading conditions. The proposed DDPG algorithm allows the CIoT agent to adapt to dynamic environments, enhance transmission security, and extend network lifetime without prior knowledge. Extensive simulations confirm its convergence and effectiveness in improving secrecy capacity, throughput, and energy efficiency. Moreover, the results attest to its superior performance compared to existing benchmarks.
In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max-min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.