Browsing by Author "Shi, Weisong"

Now showing 1 - 8 of 8
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    A Simple Mobile Plausibly Deniable System Using Image Steganography and Secure Hardware
    (Proceedings of the 2024 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems, 2024-06-19) Xia, Lichen; Liao, Jinghui; Chen, Niusen; Chen, Bo; Shi, Weisong
    Traditional encryption methods cannot defend against coercive attacks in which the adversary captures both the user and the possessed computing device, and forces the user to disclose the decryption keys. Plausibly deniable encryption (PDE) has been designed to defend against this strong coercive attacker. At its core, PDE allows the victim to plausibly deny the very existence of hidden sensitive data and the corresponding decryption keys upon being coerced. Designing an efficient PDE system for a mobile platform, however, is challenging due to various design constraints bound to the mobile systems. Leveraging image steganography and the built-in hardware security feature of mobile devices, namely TrustZone, we have designed a Simple Mobile Plausibly Deniable Encryption (SMPDE) system which can combat coercive adversaries and, meanwhile, is able to overcome unique design constraints. In our design, the encoding/decoding process of image steganography is bounded together with Arm TrustZone. In this manner, the coercive adversary will be given a decoy key, which can only activate a DUMMY trusted application that will instead sanitize the sensitive information stored hidden in the stego-image upon decoding. On the contrary, the actual user can be given the true key, which can activate the PDE trusted application that can really extract the sensitive information from the stego-image upon decoding. Security analysis and experimental evaluation justify both the security and the efficiency of our design.
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    E3-UAV: An Edge-Based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles
    (IEEE Internet of Things Journal, 2023-08-03) Suo, Jiashun; Zhang, Xingzhou; Shi, Weisong; Zhou, Wei
    Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E3-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.
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    Heterogeneous Task Oriented Data Scheduling in Vehicular Edge Computing via Deep Reinforcement Learning
    (IEEE Transactions on Vehicular Technology, 2024-08-16) Luo, Quyuan; Luan, Tom H.; Shi, Weisong; Fan, Pingzhi
    In vehicular edge computing environment, massive computation-intensive tasks would be produced from diverse vehicular applications. Data scheduling among vehicles and roadside units(RSUs) is a fundamental issue in timely processing those tasks. However, the task heterogeneity with different computation resource requirements and delay constraints, the distinct capacities of vehicles and RSUs, and the stochastic task arrival, pose significant challenges in realizing efficient data scheduling. The existing literature ignores the multi-core feature of both vehicles and RSUs in data scheduling, which may lead to an inefficient resource usage. To cope with these challenges, in this paper, we first construct a multi-queue multi-block model for heterogeneous task oriented data caching on both vehicle and RSU sides. By fully utilizing the multi-core features of both vehicles and RSUs, a fine-grained offloading model is then developed, involving the association between data blocks and computing cores, and the allocation of computation and communication resources. After that, a long-term loss minimization problem is formulated to facilitate data processing. We leverage the Markov decision process (MDP) to model the optimization problem, which is then solved by our proposed deep deterministic policy gradient (DDPG) based association mapping and resource allocation algorithm (D-AMRA). In D-AMRA, an action transformation method is proposed to map the outputs of DDPG to the form of optimization variables. Eventually, extensive simulations with comparative benchmarks are conducted to evaluate the effectiveness of our proposed D-AMRA.
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    Joint Optimization of Security Strength and Resource Allocation for Computation Offloading in Vehicular Edge Computing
    (IEEE Transactions on Wireless Communications, 2023-04-13) Xiao, Huizi; Zhao, Jun; Feng, Jie; Liu, Lei; Pei, Qingqi; Shi, Weisong
    Vehicular Edge Computing (VEC) is a promising new paradigm that has attracted much attention in recent years, which can enhance the storage and computing capabilities of vehicular networks to provide users with low latency and high-quality services. Due to the open access and unreliable wireless channels, some appropriate security measures should be implemented in the VEC to ensure information security. However, the operation of the security mechanism dominates supererogatory computing resources, thus affecting the performance of VEC systems. The scarcity of computation and energy resources of the vehicles conflicts with the requirement of tasks for time delay and information security. In this paper, taking the driving velocity and position of the vehicles, the number of lanes, the model and density of the attackers, and security strength into consideration, we formulate a max-min optimization problem to jointly optimize offloading decision, transmit power, task computation frequency, encryption computation frequency, edge computation frequency, and block length to obtain optimal secure information capacity and local computation delay. The formulated optimization problem is a mixed integer nonlinear programming (MINLP), which is intractable. We apply the generalized benders decomposition (GBD)-based method to solve it. The simulation results show that our proposed algorithms have convergence and effectiveness and achieve fairness among vehicles on the road.
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    Resource Optimization of MAB-based Reputation Management for Data Trading in Vehicular Edge Computing
    (IEEE Transactions on Wireless Communications, 2023-01-09) Xiao, Huizi; Cai, Lin; Feng, Jie; Pei, Qingqi; Shi, Weisong
    Vehicles are hesitant to upload data to edge servers in vehicle edge computing (VEC) as many vehicle data collected and perceived by various on-board sensors contain sensitive and personal information and lack economic incentive. Instead of free access to shared data, encrypted data trading will alleviate security and privacy concerns and provide an incentive for vehicle owners to share their data. The edge server needs to pay the price in data trading, and reputation management is a great method to help it trade with reliable and available vehicles. In this paper, we propose a multi-armed bandit (MAB)-based reputation management scheme, so the edge servers can select the high reputation vehicles for data trading, which can ensure the credibility and reliability of the data. The encryption scheme is applied to achieve the required transmission security level and defend the rights and interests of the edge server. On the other hand, implementing security measures will consume the computation and communication resources of the vehicles. We formulate an optimization problem that maximizes the revenue of vehicles in data trading under the constraints of time delay, energy consumption, and security level. Simulation results demonstrate that the proposed scheme is effective and efficient for vehicle reputation management, data trading selection, and resource allocation.
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    Towards C-V2X Enabled Collaborative Autonomous Driving
    (IEEE Transactions on Vehicular Technology, 2023-08-14) He, Yuankai; Wu, Baofu; Dong, Zheng; Wan, Jian; Shi, Weisong
    Intelligent vehicles, including autonomous vehicles and vehicles equipped with ADAS systems, are single-agent systems that navigate solely on the information collected by themselves. However, despite rapid advancements in hardware and algorithms, many accidents still occur due to the limited sensing coverage from a single-agent perception angle. These tragedies raise a critical question of whether single-agent autonomous driving is safe. Preliminary investigations on this safety issue led us to create a C-V2X-enabled collaborative autonomous driving framework (CCAD) to observe the driving circumstance from multiple perception angles. Our framework uses C-V2X technology to connect infrastructure with vehicles and vehicles with vehicles to transmit safety-critical information and to add safety redundancies. By enabling these communication channels, we connect previously independent single-agent vehicles and existing infrastructure. This paper presents a prototype of our CCAD framework with RSU and OBU as communication devices and an edge-computing device for data processing. We also present a case study of successfully implementing an infrastructure-based collaborative lane-keeping with the CCAD framework. Our case study evaluations demonstrate that the CCAD framework can transmit, in real-time, personalized lane-keeping guidance information when the vehicle cannot find the lanes. The evaluations also indicate that the CCAD framework can drastically improve the safety of single-agent intelligent vehicles and open the doors to many more collaborative autonomous driving applications.
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    Towards Resilient Network Slicing for Satellite-Terrestrial Edge Computing IoT
    (IEEE Internet of Things Journal, 2023-05-18) Esmat, Haitham H.; Lorenzo, Beatriz; Shi, Weisong
    Satellite-Terrestrial Edge Computing Networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs such as Software-Defined Networking (SDN), satellite edge computing, and Network Function Virtualization (NFV) are key to realizing this vision. In this paper, we survey and analyze network slicing solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite edge computing, mmWave/THz, and AI solutions to make network slicing adaptive. In addition, we identify challenges and open issues to slice STECNs. In particular, resilient network slicing is crucial for essential and critical services. Network failures are unavoidable in large networks and can cause significant disruptions in network slicing, compromising many services. To this end, we present a resilient network slicing design to cope with failures and guarantee service continuity which is agnostic to the integration architecture and inherently multi-domain. Further, we present strategies to achieve resilient networking and slicing in STECNs including planning and provisioning of redundant network resources, design rules for service level agreement decomposition, and cross-domain solutions to detect and mitigate failures. Finally, promising future research directions are highlighted. This paper provides valuable guidelines for slicing STECNs and will benefit key sectors, such as smart healthcare, e-commerce, industrial IoT, and education, among others.
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    WiLDAR: WiFi Signal-Based Lightweight Deep Learning Model for Human Activity Recognition
    (IEEE Internet of Things Journal, 2023-07-11) Deng, Fuxiang; Jovanov, Emil; Song, Houbing; Shi, Weisong; Zhang, Yuan; Xu, Wenyao
    In recent years, the WiFi channel state information (CSI) has been increasingly used for human activity recognition (HAR) during activities of daily living, because of non-intrusiveness and privacy preserving properties. However, most previous works require complex processing of CSI signals, and the large number of classification network parameters significantly increases the recognition time and deployment costs. Accordingly, a WiFi signal based lightweight deep learning (WiLDAR) network is developed in this study to ensure systematic operation on edge computing devices. We combine the random convolution kernel with deep separable convolution and residual structure, so that WiLDAR can easily extract CSI signal features without filtering and denoising. The parameter number and training time of WiLDAR are thus much less than those of previous neural networks. In addition, a tiny HAR system using only Raspberry Pi and router is implemented. Experiments verify that WiLDAR can achieve real-time HAR on IoT devices, which makes HAR deployment more convenient. We test WiLDAR on three different fine-grained action datasets to achieve 99%, 93.5% and 97.5% recognition accuracy, respectively. The demonstrated learning capability of WiLDAR makes it an excellent option for the remote HAR.