One of the main design challenges in wireless sensor networks (WSNs) is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP) is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO) techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO) model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV). In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived. In addition, the average symbol error rate (ASER) with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI) and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.

In this study, security schemes at the physical layer are proposed for an amplify‐and‐forward relay system in the presence of a passive eavesdropper. We focus on maximising the transmit power of the artificial noise (AN) to degrade the minimum mean square error (MMSE) level at the eavesdropper, while achieving MMSE constraint for the desired receiver. Assuming that the desired receiver and the passive eavesdropper possess perfect channel state information (CSI), AN is designed to lie in the null space of legitimate channels. However, the available CSI is impossible to be perfect in practice. Therefore the authors present a robust beamforming scheme which maximises the worst‐case transmit power of the AN under the condition of imperfect CSI and recovers a large fraction of the performance in the perfect CSI case. A classical alternating optimisation algorithm is proposed to obtain the relay beamforming weights, and simulation results show the benefit of proposed schemes.

Relay strategies can increase coverage range and improve reliability for power‐limited sensors. In this study, cooperative jamming schemes are investigated for security communications in two‐hop multi‐sensors relay networks where a passive eavesdropper can wiretap the relay channels in both hops. The normally inactive nodes can be used as cooperative jamming sources to confuse the eavesdropper. Artificial noise (AN) is maximised to degrade the signal‐to‐interference‐plus‐noise ratio (SINR) level at the eavesdropper, while satisfying the individual power constraint of each relay sensor and SINR constraint at the desired receiver. Assuming that CSI is perfect, relay beamforming weights can be obtained by solving a second‐order cone programming problem. However, the available CSI is impossible to be perfect in practice. Therefore, a robust beamforming scheme is also presented. The transmit power used for AN is maximised, while satisfying the power constraint of each relay sensor and worst‐case SINR constraint at the desired receiver under a bounded spherical region for the norm of the CSI error vector from the relays to the destination. Robust beamforming weight vector can be obtained by using S‐procedure and rank relaxation techniques. The benefits of the proposed schemes are shown in simulation results.

In this paper, physical layer security is studied for multiuser peer-to-peer (MUP2P) relay networks using amplify-and-forward (AF) protocol, where a secure user and other unclassified users coexist with an eavesdropper. Though the beamforming design has been previously investigated with perfect channel state information (CSI) in the presence of an eavesdropper, this paper focuses on researching the robust relay beamforming mechanism for MUP2P relay networks. The worst-case secrecy rate is maximized, while subject to the worst-case signal-to-interference-noise ratio constraint at each user and the total power constraint at relays under a bounded spherical region for the norm of the CSI error vector from the relays to the destination. Mathematically, the optimization problem is nonlinear and nonconvex. We propose an iterative algorithm to obtain an efficient solution of the nonconvex problem by employing S-Procedure and rank relaxation techniques. The convergence of the proposed robust algorithm is proved and its complexity is analyzed. Simulations show that the proposed design can significantly improve the performance of secure wireless communications.

In this study, physical layer security techniques are studied for multi‐user peer‐to‐peer relay networks using amplify‐and‐forward protocol in the presence of an eavesdropper. Two system designs are considered: the minimum achievable secrecy rate maximisation and the total transmit power minimisation. First, considering user fairness, the authors study the max–min achievable secrecy rate problem subject to a total transmit power constraint. Mathematically, the optimisation problem is non‐linear and non‐convex. They propose a polynomial time algorithm based on the bisection technique to solve it. Second, they study the total transmit power minimisation problem which aims at minimising the total transmit power at relays such that individual secrecy rate for each user is satisfied. The total power optimisation problem is also non‐convex, so they use the relaxation technique to transform it into a convex problem which can be efficiently solved by the gradient‐based algorithms. Numerical evaluation of the obtained secrecy rate and transmit power results shows that the proposed designs can significantly improve the performance of secure wireless communications.

Network intellectualization enables networks to adapt to the dynamic environment by perceiving, thinking, and interpreting the overall environmental changes like a human. However, the thinking capability of existing intelligent networks is generally limited or even absent. To further strengthen the thinking capability, we propose a novel network-node-embedded network cognition model based on a knowledge module to provide superior network intelligence. The proposed model is designed based on the OODA cycle, which is named from its components of “Observe”, “Orient”, “Decide”, and “Act” modules, and is widely applied to improve the network intellectualization. We firstly reorganize the OODA cycle by extending the perceptive range of the “Observe” module and classifying the other three modules as “Learning Analysis” and “Optimization Decision-making” modules. Furthermore, we innovatively introduce a “Network Knowledge” module using the knowledge graph, which constructs the correlation between the network and its overall environmental changes to generate effective decisions. Besides, the proposed model is designed to be directly embedded in network nodes to verify and execute decisions simultaneously, eliminating the time delay between them. The use cases that illustrate the applicability of the proposed model are presented, and simulation results can demonstrate that the proposed model is feasible and effective.