With the increasing deployment of Low Earth Orbit (LEO) satellites (e.g., Starlink), rate adaptation has been widely applied in satellite communication systems to rapidly adapt changing satellite-ground channel. However, the state-of-the-art rate adaptation solutions still do not approach the Shannon capacity, since they always operates in discrete steps (e.g., switching between fixed modulation orders and coding rates), leading to abrupt performance deterioration when the channel fluctuates between two thresholds. We propose a new plug-and-play rate adaptation, called ERA-LEO integrated to satellite communication systems without any extra hardware modification. For adapting smoothly to changes in channels, we first design a probabilistic constellation shaping enabled system to provide a continuous and more granular adjustment of the constellation by tuning the probability distribution of symbols. Second, to avoid the frequent feedback, used to select a target probability distribution based on the channel condition, we establish a theoretical model to demonstrate that the SNR of the return link can reliably predict that of the forward link, and present a lightweight prediction mechanism based on neural network. We also implement ERA-LEO prototype using FPGA-based software defined radio platforms. The extensive evaluations demonstrate ERA-LEO achieves an average gain of 2.19 dB compared to state-of-the-art baselines and a 43.08% improvement in network throughput.

This paper introduces an autonomous UAV vision system for continuous, real-time tracking of marine animals, specifically sharks, in dynamic marine environments. The system integrates an onboard computer with a stabilised RGB-D camera and a custom-trained OSTrack pipeline, enabling visual identification under challenging lighting, occlusion, and sea-state conditions. A key innovation is the inter-UAV handoff protocol, which enables seamless transfer of tracking responsibilities between drones, extending operational coverage beyond single-drone battery limitations. Performance is evaluated on a curated shark dataset of 5,200 frames, achieving a tracking success rate of 81.9% during real-time flight control at 100 Hz, and robustness to occlusion, illumination variation, and background clutter. We present a seamless UAV handoff framework, where target transfer is attempted via high-confidence feature matching, achieving 82.9% target coverage. These results confirm the viability of coordinated UAV operations for extended marine tracking and lay the groundwork for scalable, autonomous monitoring.

Integrated sensing and communications (ISAC) is emerging as a transformative 6G technology. By employing bistatic passive sensing between base stations (BSs), ISAC shows significant potential for intelligent transport systems (ITS), enabling real-time measurements of multiple vehicles across large areas without extensive hardware modifications. However, bistatic sensing faces significant challenges due to clock asynchronism. Existing phase offset elimination schemes are often limited by insufficient resolution or the introduction of undesired interference. To address these challenges, we propose a novel joint Doppler-delay (DD) estimation scheme that accounts for clock asynchronism, comprising four key components: (1) snapshot augmentation for covariance matrix estimation; (2) a spectrumweighted integrated periodogram-multiple signal classification (SWIP-MUSIC) algorithm for composite timing offset (CTO) estimation; (3) residual phase offset estimation and alignment; and (4) a periodogram-based joint DD estimation algorithm. Simulation results validate the robustness of the proposed method in multi-target scenarios, highlighting its potential to advance traffic monitoring and other sensing applications in the 6G era.

Perceptive mobile networks (PMNs), integrating ubiquitous sensing capabilities into mobile networks, represent an important application of integrated sensing and communication (ISAC) in 6G. In this paper, we propose a practical framework for uplink sensing of angle-of-arrival (AoA), Doppler, and delay in millimeter-wave (mmWave) communication systems, which addresses challenges posed by clock asynchrony and hybrid arrays, while being compatible with existing communication protocols. We first introduce a beam scanning method and a corresponding AoA estimation algorithm, which utilizes frequency smoothing to effectively estimate AoAs for both static and dynamic paths. We then propose several methods for constructing a “clean” reference signal, which is subsequently used to cancel the effect caused by the clock asynchrony. We further develop a signal ratio-based joint AoA-Doppler-delay estimator and propose an AoA-based 2D-FFT-MUSIC (AB2FM) algorithm that applies 2D-FFT operations on the signal subspace, which accelerates the computation process with low complexity. Our proposed framework can estimate parameters in pairs, removing the complicated parameter association process. Simulation results validate the effectiveness of our proposed framework and demonstrate its robustness in both low and high signal-to-noise ratio (SNR) conditions.

Digital technology advances have converted traditional archives into digital formats, broadening data diversity and posing new management challenges. This paper introduces a novel framework for Multimodal Archival Data Ecosystems, designed to enhance the management, accessibility, and utilization of archival data across diverse modalities. The need for such a framework arises from critical challenges in archival resource management, including the absence of systematic organizational structures, inefficiencies in inter-regional communication, and insufficient strategies for preserving and enhancing archival material values. Traditional archival systems struggle with these issues, especially in the face of growing data diversity and volume due to advancements in digital technologies. Our method is grounded in information ecology theory, leveraging recent advancements in information science. We propose a multimodal approach that integrates various data types and sources, enhancing the analytical and knowledge services capabilities of archival resources. This framework promotes efficient knowledge exchange by implementing collaborative mechanisms among archival stakeholders and incorporating intelligent archiving services to improve user interaction and resource accessibility. We conducted a comprehensive analysis of the ecosystem’s structure and operational mechanisms, emphasizing the integration of collaborative and intelligent services to meet the dynamic needs of users and keep pace with ongoing technological advancements. This study supports innovative practices that aim to transform traditional archival systems into dynamic, multimodal ecosystems capable of handling the complexities of modern archival data.

This study introduces the Smart Road Stud (SRS), an innovative Anisotropic Magneto-Resistive (AMR) detector strategically placed on lane markings for advanced traffic monitoring. Unlike traditional detectors, the SRS, when deployed in pairs or more, facilitates simultaneous vehicle detection. Analysis reveals that single AMR detectors often face challenges, such as interference signals and misidentification of double-vehicle signals. To address these issues, a method leveraging multiple AMR detectors is proposed. Experimental results demonstrate over 95% accuracy in identifying interference signals and distinguishing double-vehicle signals. The approach achieves a remarkable 98.7% accuracy in lane-level traffic volume estimation. The SRS outperforms conventional detectors, showcasing its potential to enhance accuracy and versatility in traffic monitoring.

The unique nature of road bottleneck areas involves a sudden decrease in lane capacity, making them prone to congestion. Particularly on highways, this issue demands optimization and resolution. The traditional road infrastructure control technique, known as variable speed limit (VSL) technology, not only mandates the installation of gantries for variable message signs but also remains susceptible to various factors such as weather conditions and driver compliance rates. Meanwhile, as the intelligence level of connected and automated vehicles (CAVs) continues to advance, CAV can serve not only the traditional transportation function but also be controlled as a mobile intelligent control platform in the future. Leveraging this, this paper proposes a novel paradigm for highway traffic management. It involves substituting VSL control by controlling CAVs in mixed traffic scenarios to optimize road infrastructure and enhance traffic performance. Specifically, this paper initially proposes a CAVs control strategy aimed at global optimization to enhance overall road operations. Additionally, this paper delves deeper into the impact of compliance rates and penetration rates on the effectiveness of the existing VSL control. Furthermore, our proposed control strategy is compared with the widely implemented VSL, demonstrating a significant enhancement in traffic performance. This strategy leads to a 19.1% increase in average speed as compared to no-control strategy, while the optimization effect achieved by VSL control is only 9.6%. Simulation results reveal the transformation of CAVs into mobile intelligent control platforms, not only optimizing traffic congestion but also effectively replacing traditional infrastructure control to maximize socio-economic benefits.

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

Scientific workflow scheduling is one of the most challenging problems in cloud computing because of the large-scale computing tasks and massive data volumes involved. A cloud system is a distributed system that follows the on-demand resource provisioning and pay-per-use billing model. Therefore, practical scheduling approaches are essential for good workflow performance and low overheads. This paper proposes a novel workflow allocation approach, the Geo-aware Multiagent Task Allocation Approach (GMTA), which aims to optimize large-scale scientific workflow execution in container-based clouds. GMTA is an agent-based workflow allocation method that includes a market-like agent negotiation mechanism and a dynamic workflow restructuring strategy. It decreases workflow makespans and traffic overheads by reasonable task replications. Furthermore, the performance of GMTA is verified on real scientific workflows in the CloudSim environment.

As the dominant mobile operating system in the markets of smartphones, Android platform is increasingly targeted by attackers. Besides, attackers often produce novel malware to bypass the conventional detection approaches, which are largely reliant on expert analysis to design the discriminative features manually. Therefore, more effective and easy-to-use approaches for detection of Android malware are in demand. In this paper, we design and implement EasyDefense, a lightweight defense system that is integrated with Android OS for easy and effective detection of Android malware utilizing machine learning methods and the ensemble of them. Besides universal static features such as permissions and API calls, EasyDefense also employs the N-gram features of operation codes (opcodes). These N-gram features are extracted and learnt automatically from raw data of applications. Experimental results on 204,650 applications show that users can easily and effectively protect the privacy and security on their smartphones through this system.

The rapid development of mobile internet attracts end-users to creating mobile applications. The traditional development process cannot meet their needs. In this paper, we present a cross-platform mobile development environment, EasyApp. It provides a highly-integrated, UI-friendly and easily-operating environment. The architecture of this environment is based on OSGi framework. Users could create mobile applications with draggable widgets and package applications for multiple platforms. Native APIs could be invoked with native API plugins.

Customizable mobile services are usually expressed with complex services composed of different atomic services. Fine-grained atomic mobile services are not so convenient for end users to reuse. Considering that in identical or similar service domains, a great deal of the business logics and functions are reusable within the scope. So we present a template-based framework to allow reuse of services and to achieve rapid mobile application development. The reusable fine-grained service logics and functions are encapsulated into comparatively coarse-grained templates, from which the designers can create the personalized composite services and edit the templates efficiently.

Internet of Things(IoT) was first coined in 1999 by Kevin Ashton. However with the technologies advancement, it seems that intelligent devices will invisibly be embedded in our life in few years. Enormous amounts of data need be exchanged every seconds. It calls for a seamless effect and easily interpretable communicating architecture. The research proposal will try to address some challenges and possible path in IoT enabled smarthome. It focuses primarily on two categories. Firstly, devices in IoT field is always distributed. So, there is a distributed IoT services architecture instead of traditional control-center solution. However, those devices are also limited in a certain area (such as a home local network). In order to reduce delay and burden, those distributed devices collect context information though a self-organizing broadcast network, and determine their next action based on that context data. Secondly, using Docker (a lightweight hardware-agnostic and platform-agnostic container) to package services. In our smarthome network, IoT services are distributed across different home electronics. Docker shields all the differences, so that we can quickly deploy and update module in the smarthome network after purchasing new electronics.

In this paper, we show an end-user oriented tool suite for mobile application development. The advantages of this tool suite are that the graphical user interface (GUI), as well as the application logic can both be developed in a rapid and simple way, and web-based services on the Internet can be integrated into our platform by end-users. This tool suite involves three sub-systems, namely ServiceAccess, EasyApp and LSCE. ServiceAccess takes charge of the registration and management of heterogeneous services, and can export different form of services according to the requirements of the other sub-systems. EasyApp is responsible for developing GUI in the form of mobile app. LSCE takes charge of creating the application logic that can be invoked by mobile app directly. Finally, a development case is presented to illustrate the development process using this tool suite. The URL of demo video: https://youtu.be/mM2WkU1_k-w

*** The rapid development of mobile internet abstracts many non-professional persons to creating mobile applications. Traditional development process cannot meet their needs. In this paper, we present a cross-platform mobile development environment based on OSGi framework, EasyApp. It provides a highly-integrated, UI-friendly and easily-operating environment. Applications are comprehensively developed with web techniques. Users could create mobile applications with draggable widgets. Native APIs of mobile phone can be invoked with abundant plugins. After designing, users could package and download applications of multiple platforms. ***