Limited power and computational resources make the employment of complex classical encryption schemes unrealistic in resource-limited networks, e.g., the Internet of Things (IoT). To this end, physical layer security (PLS) has shown great potential in securing such resource-limited networks. To further combat the power scarcity in IoT nodes, radio frequency (RF) based energy harvesting (EH) is an attractive energy source while relaying can enhance the energy efficiency and extend the range of data transmission. Additionally, due to deploying low-cost hardware, imperfections in the RF chain of IoT transceivers are common. Against this background, in this paper, we investigate an untrusted EH relay-aided secure communication with RF impairments. Specifically, the relay simultaneously receives the desired signal from the source and the jamming from the destination in the first phase. Hence the relay is unable to extract the confidential desired signal. The resultant composite signal is then amplified by the relay in the second phase by using the energy harvested from the composite signal followed by its transmission to the destination. Since the destination is the original source of the jamming, its effect can be readily subtracted from the composite signal to recover the original desired signal of the source. Moreover, in the face of hardware impairments (HWIs) in all nodes, maintaining optimal power management both at the source and destination may impose excessive computations on an IoT node. We solve this problem by deep learning (DL) based optimal power management maximizing the secrecy rate based on the instantaneous channel coefficients. We show that our learning-based scheme can reach the accuracy of the exhaustive search method despite its considerably lower computational complexity. Moreover, we developed an optimization framework for judiciously sharing HWIs across the nodes, so that we attain the maximum secrecy rate. To derive an efficient solution, we utilize a majorization-minimization (MM)algorithm, which is a particular instance in the family of successive convex approximation (SCA) methods. The simulation results show that the proposed HWI aware design considerably improves the secrecy rate.
Limited power and computational resources make the employment of complex classical encryption schemes unrealistic in resource-limited networks, e.g., the Internet of Things (IoT). To this end, physical layer security (PLS) has shown great potential in securing such resource-limited networks. To further combat the power scarcity in IoT nodes, radio frequency (RF) based energy harvesting (EH) is an attractive energy source while relaying can enhance the energy efficiency and extend the range of data transmission. Additionally, due to deploying low-cost hardware, imperfections in the RF chain of IoT transceivers are common. Against this background, in this paper, we investigate an untrusted EH relay-aided secure communication with RF impairments. Specifically, the relay simultaneously receives the desired signal from the source and the jamming from the destination in the first phase. Hence the relay is unable to extract the confidential desired signal. The resultant composite signal is then amplified by the relay in the second phase by using the energy harvested from the composite signal followed by its transmission to the destination. Since the destination is the original source of the jamming, its effect can be readily subtracted from the composite signal to recover the original desired signal of the source. Moreover, in the face of hardware impairments (HWIs) in all nodes, maintaining optimal power management both at the source and destination may impose excessive computations on an IoT node. We solve this problem by deep learning (DL) based optimal power management maximizing the secrecy rate based on the instantaneous channel coefficients. We show that our learning-based scheme can reach the accuracy of the exhaustive search method despite its considerably lower computational complexity. Moreover, we developed an optimization framework for judiciously sharing HWIs across the nodes, so that we attain the maximum secrecy rate. To derive an efficient solution, we utilize a majorization-minimization (MM)algorithm, which is a particular instance in the family of successive convex approximation (SCA) methods. The simulation results show that the proposed HWI aware design considerably improves the secrecy rate.
Nowadays, the Internet of Things (IoT) acts as a key enabler for smart cities, intelligent transportation systems, precision medicine, smart grids, etc. However, the computing power of IoT nodes is weak, and encryption algorithms cannot be used to ensure the secure transmission of data because of the high complexity. In recent years, the emerging technologies of wireless powered communication network (WPCN) and physical layer security (PLS) are regarded as potential solutions to allow IoT nodes to harvest energy from radio frequency (RF) and ensure secure data delivery. The secrecy rate, which is defined as the difference of main channel capacity and eavesdropping channel capacity, represents the secure data delivery ability. How to maximize the secrecy rate with the rather limited energy for IoT network is a challenging problem. In this paper, we formulate the secrecy rate maximization problem as an optimization problem. We solve this problem by two steps: using one-dimension research to find the optimal energy harvesting duration and find the closed-form solution for transmission power and transmission duration for every node. Compared with the heuristic algorithm, the proposed algorithm can obtain better performance from secrecy rate perspective.
In this paper, the radio frequency energy harvesting (EH) and security issues in Internet of Things (IoT) sensor networks with multiple untrusted relays are considered. In particular, the communication protocol is divided into two phases. The first phase is used for EH, in which the IoT sensor nodes (SNs) and relays harvest energy from multiple power transfer stations. The second phase is used for information transmission in two steps: 1) the selected SN uses the harvested energy to broadcast information to the controller and the relays, and 2) the selected relay forwards information to the controller by applying the amplify-and-forward protocol to improve the quality of the communication between the SN and the controller. During information transmission, the controller is at risk of losing information because the relay may act as an eavesdropper (namely, an untrusted relay). Thus, to improve the secrecy performance of the considered system, we propose an optimal scheme, namely, best-sensor-best-untrusted-relay (BSBR) and compare this scheme with random-sensor-random-untrusted-relay and a threshold-based scheme. The closed-form expressions for the secrecy outage probability (SOP) and secrecy throughput (ST) are obtained and verified through Monte Carlo simulations to confirm the superior performance of our approach. EH time optimization and the target secrecy rate optimization algorithms are also proposed. In addition, the impacts of the EH time, the EH efficiency coefficient, the numbers of SNs and untrusted relays, and the target secrecy rate on the SOP and the ST are investigated. The results indicate that the BSBR generally outperforms the two baseline schemes in terms of the SOP and ST.
In this paper, we investigate the achievable secrecy rate in a relay channel where the source and the destination wish to keep the information confidential from an untrusted relay (potential eavesdropper). The relay has no energy supply, and harvests its required energy for relaying from its received signals. For radio-frequency (RF) Energy Harvesting (EH) at the relay, we adopt power splitting (PS) approach. We study the problem of achieving positive secrecy rates between the transmitter and the receiver while making use of an untrusted relay. Energy limitation at the relay makes the problem more interesting. Because, the relay must choose either to eavesdrop the signal to be able to decode or to harvest energy (from the information signal) to be able to forward the message. Our scheme to guarantee the secrecy uses cooperative jamming by either the destination or an extra jammer node. We derive the achievable secrecy rates for the compress-and-forward (CF) and amplify-and-forward (AF) relays for two different scenarios. First, we assume the destination and the jammer are two separate nodes. Then, we consider destination-assisted jamming. In addition, we let the relay to use both the sources and the jammers transmitted signals to harvest energy. Our numerical results show that our proposed schemes guarantee a positive secrecy rate by properly locating the jammer and allocating enough jamming power. However, for a given power budget (i.e., fixed total transmission and jamming power) there is an optimum amount for the jamming power.
The broadcast nature of the wireless medium allows unintended users to eavesdrop on confidential information transmission. In this regard, we investigate the problem of secure communication between a source and a destination via a wireless energy harvesting untrusted node that acts as a helper to relay the information; however, the source and destination nodes wish to keep the information confidential from the relay node. To realize the positive secrecy rate, we use destination-assisted jamming. Being an energy-starved node, the untrusted relay harvests energy from the received radio-frequency (RF) signals, which include the source's information signal and the destination's jamming signal. Thus, we utilize the jamming signal efficiently by leveraging it as a useful energy source. At the relay, to enable energy harvesting and information processing, we adopt power splitting (PS) and time switching (TS) policies. To evaluate the secrecy performance of this proposed scenario, we derive analytical expressions for two important metrics, viz., the secrecy outage probability and the ergodic secrecy rate. The numerical analysis reveals design insights into the effects of different system parameters such as PS ratio, energy harvesting time, target secrecy rate, transmit signal-to-noise ratio (SNR), relay location, and energy conversion efficiency factor, on secrecy performance. Specifically, the PS policy achieves better optimal secrecy outage probability and optimal ergodic secrecy rate than that of the TS policy at higher target secrecy rate and transmit SNR, respectively.
Energy harvesting (EH) technology has imposed new constraints on the amount of time an energy-constrained relay node needs to use for EH and information forwarding. Moreover, EH techniques may increase vulnerability to eavesdropping, since a power receiver, as a potential eavesdropper, usually has a shorter access distance than an information receiver. Motivated by this issue, in this paper, we investigate a tradeoff between energy consumption and physical layer security in a wireless powered-untrusted cooperative communication (WPUCC) network consisting of one source-destination pair and multiple amplify-and-forward untrusted relays. To minimize the energy consumption of the network, we propose a secure adaptive relay-based cooperation approach. Moreover, a greedy battery-aware relay-selection scheme is proposed to minimize the power consumption in WPUCC systems. Using power-splitting-based energy accumulation, untrusted relays attempt to intercept the information intended to the legitimate destination while harvesting energy at the same time. To keep the information secure from potential eavesdropping, the source injects a jamming signal along with its data signal. By modeling the dynamic behavior of the relay battery as a finite-state Markov chain, we derive an analytical expression for the secrecy outage probability of our proposed secure adaptive harvest-use-store cooperative communication scheme and show that it is more secure than its direct transmission and pure two-hop transmission counterparts.
This paper investigates the physical layer security design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Taking into account the hardware impairments, and power budget constraints, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aimed at maximizing average secrecy rate (ASR). The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to improve the ASR while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases.