Internet of Things (IoT) is considered as the enabling platform for a variety of promising applications, such as smart transportation and smart city, where massive devices are interconnected for data collection and processing. These IoT applications pose a high demand on storage and computing capacity, while the IoT devices are usually resource constrained. As a potential solution, mobile edge computing (MEC) deploys cloud resources in the proximity of IoT devices so that their requests can be better served locally. In this work, we investigate computation offloading in a dynamic MEC system with multiple edge servers, where computational tasks with various requirements are dynamically generated by IoT devices and offloaded to MEC servers in a time-varying operating environment (e.g., channel condition changes over time). The objective of this work is to maximize the completed tasks before their respective deadlines and minimize energy consumption. To this end, we propose an end-to-end Deep Reinforcement Learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. The simulation results are provided to demonstrate that the proposed approach outperforms the existing methods.

Internet of Things (IoT) is regarded as the fundamental platform for many emerging services, such as smart city, smart home, and intelligent transportation systems. With ever-increasing penetration of IoT, it becomes of great importance to ensure the IoT security, as the security threats are extended from the cyber world to the physical world. In this paper, we investigates physical layer authentication to help verify the identity of IoT entities for preventing unauthorized access to information or service. Specifically, we propose a Gaussian-tag embedded physical layer authentication (GTEA) scheme by using weighted fractional Fourier transform (WFRFT). Through superimposition of a low-power Gaussian WFRFT tag onto the message signal, the legitimate receiver can verify the authenticity of the received signal at the physical layer, without being detected by adversaries. Moreover, security analysis shows that, with the deliberately designed Gaussian tag, the GTEA scheme is robust against spoofing and replaying attacks. In addition, tradeoff analysis and simulation results are provided to demonstrate the capability of GTEA scheme in achieving reliability of the message delivery, stealthy of the embedded tag signal, and balancing the tradeoff among the robustness of user authentication. Moreover, a prototype is further developed using FPGA and experiments are conducted to demonstrate the effectiveness and performance improvement of the proposed GTEA scheme.

In this paper, we propose a novel user cooperation scheme based on weighted fractional Fourier transform (WFRFT), to enhance the physical (PHY) layer security of wireless transmissions against eavesdropping. Specifically, instead of dissipating additional transmission power for friendly jamming, by leveraging the features of WFRFT, the information bearing signal of cooperators can create an identical artificial noise effect at the eavesdropper while causing no performance degradation on the legitimate receiver. Furthermore, to form the cooperation set in an autonomous and distributed manner, we model WFRFT-based PHY-layer security cooperation problem as a coalitional game with non-transferable utility. A distributed merge-and-split algorithm is devised to facilitate the autonomous coalition formation to maximize the security capacity while accounting for the cooperation cost in terms of power consumption. We analyze the stability of the proposed algorithm and also investigate how the network topology efficiently adapts to the mobility of intermediate nodes. Simulation results demonstrate that the WFRFT-based user cooperation scheme leads to a significant performance advantage, in terms of secrecy ergodic capacity, compared with the conventional security-oriented user cooperation schemes, such as relay-jamming and cluster-beamforming.

As a lightweight, flexible, and high-performance operating system virtualization, containers are used to speed up the big data platform. However, due to the imperfection of the resource isolation mechanism and the property of shared kernel, the meltdown and spectre attacks can lead to information leakage of kernel space and coresident containers. In this article, a noise-resilient and real-time detection system, named ContainerGuard, is proposed to detect meltdown and spectre attacks in the container-based big data platform. ContainerGuard uses a nonintrusive manner to collect lifecycle multivariate time-series performance event data of processes in containers and then uses ensemble of variational autoencoders as generative neural networks to learn the robust representations of normal patterns. Therefore, ContainerGuard meets the urgent need for information protection in the container-based big data platform. Our evaluations using real-world datasets show that ContainerGuard achieves excellent detection performance and only introduces about 4.5% of running performance overhead to the platform.

Affine Frequency Division Multiplexing (AFDM) is a chirp-transform modulation technique that has shown reliable performance in high-mobility scenarios, making it an attractive option for next generation communication systems. Recent literature suggests that under chirp parameter adjustment, AFDM can achieve optimal diversity performance in delay-doppler channels with maximum likelihood (ML) detection. However, the performance of AFDM with minimum mean square error equalization (MMSE-Eq) has not been extensively investigated in the existing literature. In this paper, we analyze the performance of AFDM with MMSE-Eq, derive a lower bound for the theoretical bit error rate (BER) of the AFDM system, and discuss the relationship between chirp parameters and performance degradation. To optimize BER performance, we propose two distinct chirp parameter selection strategies for frequency selective and doubly selective channels, respectively. These strategies offer the advantage of avoiding extensive computations. Additionally, we propose a low-complexity and high-performance iterative MMSE-Eq algorithm based on time-domain channel matrix operations. The algorithm resolves the issue encountered in existing low-complexity methods, where different chirp parameter selections significantly impact the complexity. Simulation results demonstrate the efficacy of our proposed parameter selection strategies and the outstanding BER performance achieved by the iterative MMSE-Eq algorithm.

Space-air-ground integrated network (SAGIN), as an integration of interconnected space, air, and ground network segments, is expected to see prevalent usage as part of intelligent transportation systems (ITS), providing an enhanced service provision in terms of coverage, flexibility and reliability. However, restricted by the limited and unbalanced network resources, the efficiency and security of the underlying connectivities of SAGIN are of utmost concern for ITS applications. In this paper, a weighted fractional Fourier transform (WFRFT) based parallel complex spreading (PCS) approach is proposed to improve the communication efficiency and security of SAGIN at the physical (PHY-) layer. The concept of WFRFT along with the direct sequence spread spectrum technology establish the security kernel of the proposed scheme. The practicability of the complex-valued WFRFT-spreading architecture is verified by studying the correlation properties of the WFRFT-spreading signals. Taking advantages of the signal uniqueness of WFRFT, the proposed scheme is capable of providing more flexibility in signal characteristic control. Moreover, the complex-valued WFRFT-spreading processing makes the proposed scheme inherently robust against the large Doppler shift distortions in SAGIN. Simulation results demonstrate the superiority of the proposed WFRFT-PCS scheme in terms of communication efficiency and PHY-layer security. Finally, as a proof of concept, an all-digital FPGA prototype system is designed to show the practicability and the performance enhancement of the proposed scheme.