This paper proposes a novel design for UAV-assisted mobile edge computing (MEC) that emphasizes user experience, addressing often overlooked elements like security and system robustness. The system works through the collaboration of multiple ground users and a UAV, completing computational tasks despite potential threats and uncertainties like eavesdropping or unpredictable flight paths due to severe weather or UAV instability. We formulate these challenges as probabilistic constraints in our model and cleverly merge the safety and robustness problems into one, greatly reducing the complexity of the problem. The model utilizes variations in energy consumption and delay to calculate a user's body sensation index, which we aim to minimize within these constraints. Given the non-convex nature of the problem, we employ Bernstein-type inequalities and the posterior method to transform the constraints into a deterministic form and the objective function into a convex one. Non-convex constraints are approximated as convex ones through first-order Taylor expansion and handled using successive convex approximation methods. An iterative algorithm is then employed for efficient solutions. Numerical simulations validate the effectiveness of our design in enhancing user experiences.

Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes.

Recently, vulnerable samples have been shown to be crucial for improving adversarial training performance. Our analysis on existing vulnerable samples mining methods indicate that existing methods have two problems: 1) valuable connections among different pairs of natural samples and their adversarial counterparts are ignored; 2) parts of vulnerable samples are unconsidered. To better leverage vulnerable samples, we propose INter PAir ConstrainT (INPACT) and Vulnerable Aware adveRsarial Training (VART) to address these drawbacks respectively. INPACT assesses adversarial risk with more comprehensive regularization on sample relationships, which takes both inter and intra connections of natural/adversarial sample pairs into consideration. Meanwhile VART makes full use of all vulnerable samples, including notable proportion neglected by existing instance re-weighting strategies. Extensive experiments on different datasets and backbones demonstrate the effectiveness of the proposed method.

This paper focuses on enhancing the robustness of task offloading within UAV-assisted Mobile Edge Computing (MEC) systems. We consider a UAV traversing predefined flight paths to provide computational support for IoT devices. The most significant challenges in this context arise from the jitter during UAV motion and the uncertainty in task size. Jitter in UAV motion is a practical concern, and to mitigate its impact, we propose Distributionally Robust Trajectory Constraints (DRTC), ensuring stable UAV trajectories while considering uncertain factors. Furthermore, we acknowledge the inherent variability in task sizes that arises from dynamic environmental conditions and specific task requirements. Traditional deterministic methods may increase system overhead and task retransmissions, compromising robustness. To address these issues, we utilize a data-driven approach that effectively captures the uncertainty related to task sizes in UAV. This approach forms the basis of our Distributionally Robust Offloading and Trajectory Optimization (DROTO) algorithm, which is crucial in achieving near-optimal solutions. Thus, it ensures both the efficiency and robustness of the system. Our findings, supported by extensive simulations, provide valuable insights by comparing them with alternative strategies, thus highlighting the algorithm's effectiveness in UAV -assisted MEC systems.

In this paper, we consider an aerial video streaming scenario where multiple cellular connected UAVs are employed to capture videos from different Point of Interest (PoI) areas. The videos are transmitted to the base stations (BSs) such that ground users can share the visions of the UAVs. We aim to maximize the minimum quality of experience (QoE) of all users by optimizing transmission scheduling jointly with video playback rate and UAV trajectory design, where the uplink interference as well as the trade off between video quality and video smoothness are taken into account. The formulation problem is a mixed integer nonconvex optimization problem that is difficult to solve. We address it through an inexact block coordinate descent method with overlapped blocks of variables to improve optimization flexibly. To relax binary constraints, we adopt the exact penalty method with equilibrium constraints, where the exactness of the penalty function is guaranteed. In addition, successive convex approximation method is adopted to tackle the non-convexity of the optimization problem. Simulation results indicate that the proposed scheme achieves significant performance improvement compared with the baseline schemes, and reveal the tradeoff between video quality and playback smoothness.

Administrative punishment is one of the most important ways of enforcing administrative law in the field of market supervision in China. However, at the current stage, the abuse of data access permission and the difficulty in managing timeliness in administrative punishment still remain unresolved, which hinders the legalization and standardization of the administrative punishment system. Inspired by blockchain, which is inherently traceable, tamper-proof, and transparent, we design a system, punishment supervisor (PEATS), which is suitable for administrative punishment and technically overcomes the defects of the traditional administrative punishment procedure. To prevent the abuse of data access permission, we innovatively introduce the ACG (authorization control gateway) to verify the access permission of users based on the records in the MSD (market supervision department) contract. To ensure the timeliness of the administrative punishment procedure, we design a special case contract that has the same status as the general case processing state of administrative punishment to track case progress on the blockchain. We experiment and evaluate PEATS in terms of functionality and performance and find that PEATS provides traceability, transparency, and timeliness assurance. In addition, PEATS has 80.9% of the throughput of a traditional server with no more than an additional 3% latency and at most 60 KB additional storage space per case.

Abstract With the development of 5G, the wireless Internet of Things (IoT) has become possible; how to provide privacy protections for the communication of IoT devices in a more vulnerable wireless transmission environment is a huge challenge. Thus, steganography is introduced as a safe and effective technology. Blockchain systems have been widely used in the area of steganography. Several works attempted to embed covert data into transactions in public blockchain systems such as Bitcoin, Ethereum and Monero. However, most of them merely focus on putting covert data into certain fields in transactions based on cryptographic algorithms. In this paper, a Covert Transaction Recognition (CTR) model is proposed by the Text Convolutional Neural Networks and Back Propagation Neural Networks. When utilizing the covert data-embedded field for recognizing, our CTR model can attain 0.79 precision and 0.83 recall on average for seven covert transaction construction schemes. The precision and recall can increase by at most 43 and 47%, respectively, if other unembedded fields were additionally exploited for recognition. We further propose a Practical Covert Transaction Construction (PCTC) model. This model fixes the contents in the embedded fields of the constructed transactions, and generates the contents in other fields using Generative Adversarial Networks. Experimental results demonstrated that the precision and recall are greatly decreased when identifying the covert transactions generated by our PCTC model. The data underlying this article are available in ‘covert-transaction-model’, at https://github.com/1997mint/covert-transaction-model.

The quality of covert communications is determined by the choice of communication channels and the design of data embedding schemes. Recently, the Bitcoin system is prevalent as a covert communication channel. The consensus mechanism requires participants to spread their found valid blocks under an adjustable difficulty, which provides a stable periodic broadcast channel. Moreover, senders and receivers are difficult to be traced, because the Bitcoin system is pseudonymous. However, since the historical data in the ledger cannot be removed from the Bitcoin system, the openness and the persistent storage of the ledger in the Bitcoin system post new challenges when designing data embedding schemes. More concreteness, most traditional data embedding schemes either design by heuristic or empirical algorithms or use a fixed field to embed data in the transactions. Therefore, the covert data can be recognized once the algorithm is leaked or the pattern is explored. In this article, we first propose a hash chain-based covert data embedding (HC-CDE) scheme. The embedded transactions are difficult to be discovered. We further propose an elliptic curve Diffie–Hellman chain-based covert data embedding (ECDHC-CDE) scheme to enhance the security of the HC-CDE scheme. Experimental analysis on the Bitcoin Testnet verifies the security and the efficiency of the proposed schemes.