Secrecy Capacity Maximization for a UAV-Assisted MEC System

Unmanned aerial vehicle(UAV)communication has attracted wide attentions in the mobile edge computing(MEC)system owing to its high-flexibility and simple operation auxiliary communication mode.Users can offload computing tasks to UAVs,which serves as edge nodes.Meanwhile,UAVs forward the tasks onto a cloud center or base station for processing,thereby shortening the implementation time of tasks.Nevertheless,the offloading links of an UAV-assisted MEC system adopt a radio broadcasting mode.Several eavesdroppers might be present in the environment to eavesdrop the data sent by users and UAVs,thereby causing significant effects on the secrecy performance.An optimized iterative algorithm is proposed in this paper to realize the maximum secrecy capacity of the MEC system and further improve the secrecy performance of an UAV-assisted MEC system and assure secrecy transmit.By doing so,the secrecy transmit problems of the two-staged offloading model of the UAV-assisted MEC system are solved.The maximum secrecy capacity of the system is obtained through joint optimization of the UAV positions,transmit power of the UAV,task offloading ratio,and allocation of offloading users considering the limited time and energy of an UAV.Simulation results demonstrate that the proposed iterative algorithm can effectively improve the secrecy capacity of the system.

An improved robust stability and robust stabilization method for linear discrete-time uncertain systems

This paper addresses the problems of the robust stability and robust stabilization of a discrete-time system with polytopic uncertainties. A new and simple method is presented to directly decouple the Lyapunov matrix and the system dynamic matrix. Combining this method with the parameter-dependent Lyapunov function approach yields new criteria that include some existing ones as special cases. A numerical example illustrates the improvement over the existing ones.

Adaptive Learning-Based Delay-Sensitive and Secure Edge-End Collaboration for Multi-Mode Low-Carbon Power IoT

Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping.However,edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service(QoS)guarantee,inadaptability of resource management,and multi-mode access conflict.We propose an Adaptive learning based delAysensitive and seCure Edge-End Collaboration algorithm(ACE_(2))to optimize multi-mode channel selection and split device power into artificial noise(AN)transmission and data transmission for secure data delivery.ACE_(2) can achieve multi-attribute QoS guarantee,adaptive resource management and security enhancement,and access conflict elimination with the combined power of deep actor-critic(DAC),“win or learn fast(WoLF)”mechanism,and edge-end collaboration.Simulations demonstrate its superior performance in queuing delay,energy consumption,secrecy capacity,and adaptability to differentiated low-carbon services.

IRS-Aided SWIPT Systems with Power Splitting and Artificial Noise

Intelligent reflecting surface(IRS) is regarded as a promising technology because it can achieve higher passive beamforming gain. In particular, the IRS assisted simultaneous wireless information and power transfer(SWIPT) system can make the information decoding receivers(IDRs) have a higher signal-to-noise ratio(SNR), and the energy harvesting receivers(EHRs) have the guarantee of minimum harvested energy threshold. Motivated by the above,in this paper, we use the power splitting(PS) at the user and introduce artificial noise(AN) into the access point(AP), so that the user in system can harvest energy and decode information simultaneously,further improve the security of user. We jointly optimize the beamforming matrix at AP, the reflection phase shift at IRS and the PS ratio, in order to maximize the user’s achievable secrecy rate, subject to the user’s minimum harvested energy threshold and AP’s transmission power. Due to the introduction of PS ratio, the coupling between variables is increased,and the complexity of the problem is significantly increased. Furthermore, the problem is non-convex, so we propose an efficient algorithm based on Taylor Formula, semi-definite relaxation(SDR) and alternating optimization(AO) to get the solution. Numerical results show that the proposed IRS-SWIPT system with PS and AN achieves significant performance improvement compared with other benchmark scheme.

Delay-dependent robust H-infinity control for discrete-time uncertain systems with time-varying state delays

This paper examines the delay-dependent H-infinity control problem for discrete-time linear systems with time-varying state delays and norm-bounded uncertainties. A new inequality for the finite sum of quadratic terms is first established. Then, some new delay-dependent criteria are derived by employing the new inequality to guarantee the robust stability of a closed-loop system with a prescribed H-infinity norm bound for all admissible uncertainties and bounded time-vary delays. A numerical example demonstrates that the proposed method is an improvement over existing ones.