Physical Layer Security Techniques Research Articles

Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

The key principle of physical layer security (PLS) is to permit the secure transmission of confidential data using efficient signal-processing techniques. Also, deep learning (DL) has emerged as a viable option to address various security concerns and enhance the performance of conventional PLS techniques in wireless networks. DL is a strong data exploration technique which can be used to learn normal and abnormal behavior of 5G and beyond wireless networks in an insecure channel paradigm. Also, since DL techniques can successfully predict future new instances by learning from existing ones, they can successfully predict new attacks, which frequently involve mutations of earlier attacks. Thus, motivated by the benefits of DL and PLS, this survey provides a comprehensive review that overviews how DL-based PLS techniques can be employed for solving various security concerns in 5G and beyond networks. The survey begins with an overview of physical layer threats and security concerns in 5G and beyond networks. Then, we present a detailed analysis of various DL and deep reinforcement learning (DRL) techniques that are applicable to PLS applications. We present the specific use-cases of PLS design for each type of technique, including attack detection, physical layer authentication (PLA), and other PLS techniques. Then, we present an in-depth overview of the key areas of PLS where DL can be used to enhance the security of wireless networks, such as automatic modulation classification (AMC), secure beamforming, PLA, etc. Performance evaluation metrics for DL-based PLS design are subsequently covered. Finally, we provide insights to the readers about various challenges and future research trends in the design of DL-based PLS for terrestrial communications in 5G and beyond networks.

Key Generation Technique Based on Channel Characteristics for MIMO-OFDM Wireless Communication Systems

Dynamic secret key generation from wireless channel characteristics is a promising technique for physical layer security. One of the important issues in this field is extending the secret key’s length while preserving its uniformity and randomness. This paper proposes a key generation method based on time-varying and the reciprocity of wireless channels for Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless communication systems. In the proposed technique, the complex impulse response (CIR) of the estimated channel state information (CSI) is extracted, and a quantization algorithm is designed to convert the maximum peaks of the CIR into secret key bits. The effectiveness of the proposed key generation technique is assessed in terms of the randomness of the produced key bits with different key lengths by using a statistical test suite of the National Institute of Standards and Technology (NIST). The proposed technique is employed in the MIMO-OFDM systems with different modulation schemes through Additive White Gaussian Noise (AWGN) and Rayleigh channels. The simulation results show that the secret keys with various key lengths generated from the proposed technique for the MIMO-OFDM systems guarantee randomness. Moreover, the proposed CSI-based key generation technique provides better effectiveness in terms of security when compared to some existing techniques.

High-Rate Secret Key Generation Using Physical Layer Security and Physical Unclonable Functions

Physical layer security (PLS) can be adopted for efficient key generation and sharing in secured wireless systems. The inherent random nature of the wireless channel and the associated channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable a high-rate secret key generation process. The proposed system integrates physically unclonable functions (PUFs) and CR to generate and exchange secret keys between two nodes securely. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. Moreover, we propose a novel bit extraction scheme to reduce the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that the proposed system can operate efficiently even when the channel is nearly flat or time-invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. Furthermore, the results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs) substantially faster than other PLS techniques. Adopting the AF into static channels managed to reduce the mismatch ratio between the generated secret sequences and degrade the eavesdropper’s capability to predict the secret keys.

Proactive Eavesdropping of Physical Layer Security Aided Suspicious Communications in Fading Channels

Proactive eavesdropping is an effective approach to legitimately surveil the suspicious communications. Current studies all considered that physical layer security (PLS) techniques such as the wiretap coding are not applied by the suspicious users (SUs) to protect their communications. Contrary to that, we consider that the wiretap coding in PLS is adopted by the SUs to defend against the proactive eavesdropping from the legitimate monitor over fading channels. Under the fixed power allocation (FPA) and the water-filling power allocation (WFPA) at the SUs, the problem of jamming power allocation at the monitor to maximize the relative eavesdropping rate under the average transmit power constraint is investigated. The optimization problem is solved by two nested optimizations, where the bisection search method is used in the outer optimization, and the Lagrange duality method and the successive convex approximation method are used in the inner optimization. Simulation results confirm the effectiveness of the proposed algorithms compared to various baseline algorithms. It is shown that the proposed algorithm under the WFPA at the SUs outperforms the one under the FPA at the SUs, and the monitor achieves lower relative eavesdropping rate when the SUs adopt a stricter secure transmission scheme.

Physical Layer Security in Downlink of Cell-Free Massive MIMO With Imperfect CSI

This paper investigates the threat of passive eavesdropping on downlink cell-free massive MIMO (CF-MaMIMO) systems, examining in a particular system under the effects of imperfect channel estimation. Physical layer security (PLS) techniques and power allocation algorithms are typically adopted to deteriorate the quality of eavesdropped signals. In the downlink stream, artificial noise (AN) is broadcasted simultaneously with the users’ data streams, with the aim of jamming the eavesdropper’s signal without sacrificing the quality of service (QoS). However, imperfect channel estimation results in AN leakage to legitimate users. Our work considers this condition, and the power allocation algorithms we present determine the minimum power that is needed to maintain the user’s Qos even in the presence of leaked AN. We first propose the cooperative PLS algorithm (COP), which facilitates AN broadcasting via access point (AP) cooperation. Unfortunately, this cooperation wastes some of the power used to send the AN and, in most cases, increases the computational complexity. We then present the independent PLS algorithm (IND), where the goal is to regain this power by allowing each AP to broadcast AN independently. However, to meet the feasibility criterion, this occurs at the cost of increasing the number of used antennas. This technique reduces the power allocation complexity but increases the computational complexities of the precoder design and the channel estimation process. Our results reveal that the proposed algorithms enhance the security performance of CF-MaMIMO. We evaluate such performance from numerous angles including the number of users, number of APs, QoS, beamforming, AN leakage, and channel estimation techniques.

Physical layer security using boundary technique for emerging wireless communication systems

AbstractThe broadcast nature of radio propagation in wireless communication has been suspected as the loopholes of passive or active attacks by unauthorized users (eavesdroppers). The physical layer security techniques operate at the lowest stack of OSI layer against conventional cryptographic approaches, operating at the upper layer. However, techniques such as channel coding, power (directional antenna and artificial noise), and spread spectrum have been (and continuously) deployed to safeguard against sophisticated attacks. Most of these deployments are theoretical, and a few are enhanced for efficient security against an intruder. In this article, a boundary technique approach is proposed and applied to the physical layer to improve its secrecy‐capacity and subdue adversary effects at the legitimate receiver. Hybrid performance metrics were adopted, and a Monte Carlo simulation was performed. The result obtained using secrecy outage probability, secrecy‐capacity, and intercept‐probability show that our proposed techniques enhance the secret transmission between the main transmitter and legitimate receiver. The simulation results were compared with the analytical methods. It was found that the channel between the transmitter and the main receiver has a better signal‐to‐noise ratio than the corresponding eavesdropper's channel. Conclusively, performance of the proposed technique is validated for applications in emerging wireless communication systems.

Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design

This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and detects a malicious target with the same waveform. Since the waveform is embedded with information, the information is risked to be intercepted by the target. To address this problem, physical-layer security technique is exploited. By using secrecy rate and estimation rate as performance measure for communication and radar, respectively, three secrecy rate maximization (SRM) problems are formulated, including the SRM with and without artificial noise (AN) and robust SRM. For the SRM beamforming, we prove that the optimal beamformer can be computed in closed form. For the AN-aided SRM, by leveraging alternating optimization, similar closed-form solution is obtained for the beamformer and the AN covariance matrix. Finally, the imperfect CSI of the target is also considered under the premise of a moment-based random phase-error model on the direction of arrival at the target. Simulation results demonstrate the efficacy and robustness of the proposed designs.

Trajectory Design and Communication Resources Allocation for Wireless Powered Secure UAV Communication Systems

Benefiting from the intrinsic convenience, flexibility, low cost, and unmanned aerial vehicles (UAVs) are envisioned to play an important role in future wireless communication systems. However, the limited on-board battery capacities and the broadcast characteristic of air-to-ground wireless channels bring some nonnegligible challenges on achieving secure UAV communications. To tackle these challenges, we propose to apply both the wireless power transfer and physical layer security techniques in a UAV communication system. We investigate the secrecy rate maximization problem by jointly optimizing the wireless charging duration, the trajectory, and transmit power of the UAV, subject to limited battery capacity, maximum flying speed, and energy-harvesting causal constraints. To solve this nonconvex problem, we first transform this problem into an equivalent problem with a smooth objective function, and then propose a low-complexity trajectory design and communication resources allocation algorithm for settling the reformulated problem via employing alternating optimization and SCP. Specifically, the reformulated problem is decomposed into three independent subproblems: the wireless charging duration optimization, the transmit power control, and the trajectory design of the UAV. Subsequently, we iteratively solve one of these three subproblems, while the others being fixed until the proposed algorithm converges. Extensive simulation results are provided to demonstrate that the proposed algorithm can significantly enhance the secrecy rate relative to the benchmark algorithms in absence of rechargeable battery or with fixed trajectory and transmit power.

DRL-Based IRS-Assisted Secure Visible Light Communications

In this paper, we develop a novel Physical Layer Security (PLS) technique for a Visible Light Communication (VLC) system composed of light fixtures assisted by mirror array sheets serving as Intelligent Reflecting Surfaces (IRS). Our objective is to optimize the Secrecy Capacity (SC) by finding the optimal beamforming (BF) weights equipped at the VLC fixtures and mirror orientations at the mirror array sheet. Due to many design parameters, including the beamforming weights, the mirror orientations, and the mobility of the users, conventional optimization techniques may not be practical to optimize the SC capacity. Therefore, we proposed a Deep Reinforcement Learning (DRL) solution based on Deep Deterministic Policy Gradient (DDPG) algorithm to solve the highly complex SC problem by adjusting the BF weights and mirror orientations. The DDPG-based algorithm provides an optimized solution that can adapt to the large size of design parameters and act fast to the channel variations due to users’ mobility. Our results show that considering both mirror array sheet and BF vectors provide the highest SC for the system. Moreover, we show the effect of changing the mirror arrangements of the mirror array sheet on SC. We conclude that for a fixed mirror array sheet size, there exists a specific mirror arrangement (i.e., number of mirrors) that optimizes the SC. After this number, the performance of SC saturates. We also show the trade-off between the training complexity and SC performance considering different mirror arrangements in the mirror array sheet.

A novel interference signal superposition algorithm for providing secrecy to subcarrier number modulation‐based orthogonal frequency division multiplexing systems

AbstractOrthogonal Frequency Division Multiplexing with Subcarrier Number Modulation (OFDM‐SNM) is a transmission scheme that aims to convey additional data bits by exploiting the number of active subcarriers. Even though OFDM‐SNM creates many advantages, such as low bit error rate (BER), high spectral efficiency, and high reliability, it suffers from the security vulnerabilities against eavesdroppers and malicious wire‐tappers. The reason why OFDM‐SNM is vulnerable against security threats is that the nature of subcarrier number‐based additional data transmission differs from the conventional data modulation techniques, which makes the existing physical layer security (PLS) applications to be inapplicable for OFDM‐SNM. Therefore, novel PLS techniques must be developed for OFDM‐SNM to secure the data that is transmitted by exploiting the number of subcarriers. In this paper, a novel interference signal superposition security algorithm for OFDM‐SNM is proposed at the physical level to overcome its security vulnerabilities. The proposed security application aims to provide the data secrecy by embedding an artificial interference signal into the data that are meant to be conveyed by the active subcarriers. This artificial interference signal is created by exploiting the channel state information of the legitimate user, and deliberately included into the transmitted data to create a confusion for those who try to infiltrate the communication system, such as eavesdroppers or malicious wire‐tappers. In this paper, the transmission of data for both the legitimate user and eavesdropper is operated by M‐ary PSK/QAM modulation over a Rayleigh fading channel. The theoretical calculations and simulation results prove that the proposed security application for OFDM‐SNM is a strong candidate to provide the security requirements of such cases in future wireless networks, where providing data secrecy is among the top priorities.

Interference-aware modeling and analysis for the secrecy performance of cooperative vehicular relaying networks over mixed Nakagami-[formula omitted] and double Nakagami-[formula omitted] fading channels

Due to the massive wireless communications among numerous vehicular and static entities in the internet of vehicle networks, these networks became vulnerable to eavesdropping and interference. Thus, using physical-layer security techniques in vehicular networks can significantly enhance secrecy performance. This paper investigates the secrecy performance of cooperative vehicular relaying networks (CVRNs) over mixed Nakagami-m and double Nakagami-m fading channels in the presence of a single eavesdropper and co-channel interference. The relaying node is assumed to operate with different relaying schemes, namely, decode-and-forward, amplify-and-forward with variable gain, and amplify-and-forward with fixed gain. Hence, mathematical expressions for the average secrecy capacity (ASC) are obtained for the relaying schemes. Furthermore, a power allocation model is proposed to maximize the derived ASCs in terms of the source and relay nodes’ transmission powers, enhancing the relaying schemes’ secrecy performance. The results demonstrate the impact of channel condition and the strength of interference on the system secrecy performance. Moreover, the results show that the proposed power allocation model enhances the secrecy performance significantly compared to the equal power distribution model.

A PLS-HECC-based device authentication and key agreement scheme for smart home networks

IoT devices permeate our society, collect personal data, and support critical infrastructures such as the healthcare. Therefore, there is a critical need for authentication and authorization schemes for IoT devices to meet privacy requirements, such as mutual authentication and user anonymity, as well as robustness against security attacks. In this paper, we propose a device authentication and key agreement scheme for IoT networks. Our proposal takes as a model the scheme proposed by Rezai et al., and combines it with a physical layer security technique and a hyper-elliptic curve cryptosystem . Our results show that not only our authentication scheme provides anonymity, mutual authentication, and efficiency, but it also provides resilience to various attacks, including man-in-the-middle, replay, and de-synchronization attacks. Our comparison shows that our scheme performs better than the state-of-the-art in terms of security properties, while adding a small overhead of ≈ 10 ( ms ) .

DRL-Based Secure Video Offloading in MEC-Enabled IoT Networks

Wireless offloading in mobile-edge-computing (MEC)-enabled Internet of Things (IoT) networks inevitably suffers the risk of eavesdropping. Physical-layer security (PLS) approaches can be applied to prevent eavesdropping. However, the existing PLS techniques are not well targeted for videos due to the fact that video’s distortion characteristics, which allow encoding parameters to be flexibly adjusted to enhance security in offloading, are ignored. A deep reinforcement learning (DRL)-based real-time, secure, and efficient video offloading scheme is proposed in this article, where video frame resolution, one key parameter of video’s distortion characteristics, is introduced and jointly optimized with PLS scheme to guarantee video’s security, improve users’ Quality of Experience (QoE) and save energy consumption. We formulate a joint optimization problem of video frame resolution selection, computation offloading, and resource allocation strategy, to minimize energy consumption and maximize QoE in terms of delay and analytic accuracy, while subject to security rate, computing capability, and transmission power. To solve the formulated NP-hard problem with the form of high-dimensional nonlinear mixed-integer programming, the hierarchical reward-function-based DRL (JVFRS-CO-RA-MADDPG) algorithm is proposed to guide the agents to obtain the optimal policy efficiently. Finally, the simulation results show that the proposed algorithm outperforms the existing algorithms in terms of delay, energy consumption, and security level.

Empowering secure transmission for downlink of multiple access system relying non-orthogonal signal multiplexing

The growth of internet-of-things (IoT) inspired use cases in different run of the mill environments such as cities, industries, healthcare, agriculture, and transportation, has led to a greater desire for safer IoT data gathering and storage. However, securing IoT is challenging due to form-factor, complexity, energy, and connectivity limitations. Conventional coding-based security techniques are unsuitable for ultra-reliable low-latency and energy-efficient communication in IoT. Numerous research studies on physical layer security (PLS) techniques for fifth generation (5G) have emerged recently, but not all of the solutions can be used in IoT networks due to complexity limitations. Non-orthogonal multiple access (NOMA) is billed as a possible technology to solve connectivity and latency requirements in IoT. In this study, we exploit the power allocation characteristics of NOMA to enhance security in a downlink deviceto-device (D2D) decode and forward (DF) IoT network infiltrated by an eavesdropper. Our performance metric of choice is the secrecy outage probability (SOP). We formulate exact SOP results for different users. Simulation results demonstrate the positive impact of NOMA on SOP in a D2D IoT-NOMA network.

Security Energy Efficiency Analysis of Analog Collaborative Beamforming With Stochastic Virtual Antenna Array of UAV Swarm

To extend the transmission range governed by limited transmit power, collaborative beamforming (CB) can be employed with a virtual antenna array (VAA) constructed by an unmanned aerial vehicle (UAV) swarm. CB techniques combined with physical layer security (PLS) algorithms are known to better secure transmissions of sensitive information in the presence of eavesdropping attacks, compared to co-located antenna-based schemes. In particular, analog collaborative beamforming (ACB) with random VAA topologies, which is suitable for UAV networks due to its lower signaling/channel estimation overhead compared to the digital CB, can provide a significantly higher secrecy rate by creating noise-like signals at eavesdroppers. However, the increase in energy consumption to improve secrecy rate had been ignored in the existing ACB-based PLS schemes, which is of paramount importance considering the limited onboard energy of UAVs. Thus, motivated by such limitation, in this paper, we examine the security energy efficiency of the ACB-based PLS technique, where a group of UAVs (or VAA elements) randomly change their locations in a distributed manner to randomize the array factor of the VAA, i.e., a stochastic VAA. The proposed stochastic VAA outperforms the existing static VAA in terms of secrecy energy efficiency as verified by analytic and numerical results. Further, the impact of phase errors of the proposed scheme is also presented, which indicates the significance of the accurate phase estimation.

The impact of mobility on physical layer security in 5G uRLLC

Due to the openness nature of wireless medium, security issues have increasingly become a bottleneck that restricts the development of ultra-reliable and low-latency communications (uRLLCs). Physical layer security (PLS) technique has been proposed to fulfill the security and confidentiality of information transmission by exploiting the characteristics of the wireless channel, which caters to the features of uRLLC. Furthermore, PLS also shows great practicality in artificial intelligence field, especially in wireless intelligent networks. However, the previous works on the study of PLS ignored the significance of mobility and limited packet length constraint required by uRLLC for satisfying low latency, in this paper, we investigate the impact of mobility on the secrecy performance of uRLLC by using the Random WayPoint (RWP) model and the Random Direction (RD) model. Specifically, with the tools of stochastic geometry, to observe the impact of key system parameters on the secrecy performance, we establish the closed-form expression of connection outage probability, secrecy outage probability, and decoding error probability-based secrecy transmission capacity (DEP-STC). Furthermore, we derive the condition that achieves a positive DEP-STC under two moving models, which can offer the network designer some greatly significant insights into achieving perfect secrecy. Simulations validate our derived theoretical results, and indicate that RWP moving receiver can obtain a higher security level than RD moving one, while RWP eavesdropper can lead to a lower security.

Waveform-Defined Security: A Low-Cost Framework for Secure Communications

Communication security could be enhanced at physical layer but at the cost of complex algorithms and redundant hardware, which would render traditional physical layer security (PLS) techniques unsuitable for use with resource-constrained communication systems. This work investigates a waveform-defined security (WDS) framework, which differs fundamentally from traditional PLS techniques used in today's systems. The framework is not dependent on channel conditions such as signal power advantage and channel state information (CSI). Therefore, the framework is more reliable than channel dependent beamforming and artificial noise (AN) techniques. In addition, the framework is more than just increasing the cost of eavesdropping. By intentionally tuning waveform patterns to weaken signal feature diversity and enhance feature similarity, eavesdroppers will not be able to identify correctly signal formats. The wrong classification of signal formats would result in subsequent detection errors even when an eavesdropper uses brute-force detection techniques. To get a robust WDS framework, three impact factors, namely training data feature, oversampling factor and bandwidth compression factor (BCF) offset, are investigated. An optimal WDS waveform pattern is obtained at the end after a joint study of the three factors. To ensure a valid eavesdropping model, artificial intelligence (AI) dependent signal classifiers are designed followed by optimal performance achievable signal detectors. To show the compatibility in available communication systems, the WDS framework is successfully integrated in IEEE 802.11a with nearly no adding computational complexity. Finally, a low-cost software-defined radio (SDR) experiment is designed to verify the feasibility of the WDS framework in resource-constrained communications.

Security and Reliability Performance of Noise-Loop Modulation: Theoretical Analysis and Experimentation

Security is a crucial feature of current (5G) and even more for future (6G) communication networks. While the innate nature of the wireless channel is an issue for guaranteeing security, its physical characteristics can be exploited for protecting condential data. Physical-layer Security (PLS) mechanisms take advantage of unique features of the transmission media or of the user equipment to introduce an additional level of data protection. The physical-layer security (PLS) can be seen as the rst level of defense. The noise-loop (NL) modulation is a PLS technique that intrinsically provides condentiality. In this paper, we rst provide the theoretical analysis to evaluate the reliability and security in multipath fading channels, and then the theoretical results are evaluated by experimentation. A Software Dened Radio (SDR) testbed that exploits the NL secure modulation was implemented and a measurement campaign has been carried out in an indoor environment, with passive and active malicious nodes. An evaluation of the complexity of the proposed system is also reported, showing that the NL has low hardware resource occupancy and low computational cost. These fundamental characteristics make the NL modulation a valid PLS technique for providing a security solution for beyond 5G and 6G networks.

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security.

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies.

Cooperative Communication and Mobility for Securing URLLC of Future Wireless Networks

Ultra-reliable low-latency communication (uRLLC) emerges as one of the three service categories in fifth-generation (5G) wireless networks, with a potential to support multiple advanced services for latency-sensitive communications. The inherent openness of the wireless environment makes security in 5G unprecedentedly urgent to be addressed. Accordingly, the physical layer security (PLS) technique exploits the randomness characteristics of the wireless channel to fulfill security and confidentiality of information transmission. Different from many current works, which ignored the significance of security in uRLLC, this paper studies secrecy improvement strategy design in uRLLC scenarios. First, we propose suitable performance metrics for evaluating PLS of uRLLC and make a compromise between reliability, latency, and security, while maximizing the network throughput. Next, we identify the key challenging issues for achieving PLS in the context of uRLLC, and discuss the role of cooperative communication and mobility in terms of possible solutions. To the best of our knowledge, this is the first paper to make a trade-off among reliability, security and network throughput. Simulations validate the theoretical analyses.