To tackle the distinctive challenges of satellite mobility—challenges that exert significant impacts on both the reliability of communication links and the efficiency of wireless power transfer (WPT)—we propose a distributed satellite-fusion cell-free (SFcf) integrated sensing and communication (ISAC) architecture. Traditional adaptive beamforming, which performs reactive compensation based on estimated channel state information (CSI), faces inherent limitations in achieving robust data transmission and efficient energy delivery within such high-dynamic environments, primarily due to its intrinsic latency. To surmount this bottleneck, we put forward a novel closed-loop ISAC framework that facilitates a proactive paradigm for the joint transfer of wireless information and power. The core concept lies in estimating the physical source of channel variation—namely the satellite’s motion parameters—and utilizing these parameters to enable predictive adaptation of both communication and energy beams. In this framework, sensing-derived Doppler and time of arrival (ToA) parameters are fed back to actively pre-compensate for channel mismatch, ensuring communication signals are robust and that energy beams remain precisely focused on the moving target. Therefore, our proposed SFcf-ISAC framework is specifically designed to validate this effective synergy, enabling sustainable 6G satellite-ground connectivity through high-efficiency WPT, remote sensing and resilient high-mobility communication.

Automotive FMCW radars remain reliable in rain and glare, yet their sparse, noisy point clouds constrain 3-D object detection. We therefore release CoVeRaP, a 21 k-frame cooperative dataset that time-aligns radar, camera, and GPS streams from multiple vehicles across diverse manoeuvres. Built on this data, we propose a unified cooperative-perception framework with middle- and late-fusion options. Its baseline network employs a multi-branch PointNet-style encoder enhanced with self-attention to fuse spatial, Doppler, and intensity cues into a common latent space, which a decoder converts into 3-D bounding boxes and per-point depth confidence. Experiments show that middle fusion with intensity encoding boosts mean Average Precision by up to 9 × at IoU 0.9 and consistently outperforms single-vehicle baselines. CoVeRaP thus establishes the first reproducible benchmark for multi-vehicle FMCW-radar perception and demonstrates that affordable radar sharing markedly improves detection robustness. Dataset and code are publicly available to encourage further research.

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we proposeIota, a logic programming-based framework to perform system-level security analysis for IoT systems.Iotagenerates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In buildingIota, we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions.Iotaautomatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose three metrics based on the attack graph, which provide guidance on hardening IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows thatIota's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We applyIotato 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

Millimeter-wave (mmWave) communication is poised to significantly enhance vehicle-to-vehicle (V2V) networks by facilitating real-time data transmission between vehicles at gigabits-per-second (Gbps) data rates. However, the high relative mobility between vehicles results in substantial beamforming overhead, negatively affecting V2V network throughput and latency. In this paper, we introduce a novel real-time radarassisted beamforming approach for V2V networks and assess its performance in four typical scenarios using commercial off-the-shelf (COTS) devices. In the transmitter static scenario, our proposed scheme surpasses the default 802.11ad protocol by up to 54% in throughput. In the highly dynamic scenario, our approach yields a 67% improvement in throughput and exhibits 90% lower latency than the default 802.11ad protocol. Furthermore, we investigate a non-line-of-sight (NLOS) scenario, demonstrating that our proposed scheme can achieve higher data throughput rates by opting for the most robust beam sector rather than frequently alternating between weak beam patterns. Finally, the preliminary result in the highway scenario shows that our protocol can improve the throughput by 66% more than the default protocol.

COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User’s confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1][2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems.We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.

In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more security. The massive idle computing resources closing to the edge also enhance the deployment of edge services. Instead of using cloud services from some primary providers, edge computing provides people a great chance to join the market of computing resources actively. However, edge computing also has some critical impediments that we have to overcome.In this paper, we design an edge computing service platform that can receive and distribute the computing resources from the end-users in a decentralized way. Without the centralized trade control, we propose a novel Blockchain-enabled decentralized technique to establish the trade trust among users and implement it with using embedded immutable intermediary smart contract. Our system also considers and resolves a variety of security and privacy challenges when utilizing the Blockchain technique. We implement our system and conduct extensive experiments to show the feasibility and effectiveness of our proposed system.