Related papers: Ratio1 -- AI meta-OS

While many resource-constrained networks, such as Internet of Things (IoT) and Internet of Vehicles (IoV), are inherently distributed, the majority still rely on central servers for fast authentication and data sharing. Blockchain-based…

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous…

The rapid expansion of IoT ecosystems introduces severe challenges in scalability, security, and real-time decision-making. Traditional centralized architectures struggle with latency, privacy concerns, and excessive resource consumption,…

In the Industrial Internet of Things (IoT), a large amount of data will be generated every day. Due to privacy and security issues, it is difficult to collect all these data together to train deep learning models, thus the federated…

The security of image data in the Internet of Things (IoT) and edge networks is crucial due to the increasing deployment of intelligent systems for real-time decision-making. Traditional encryption algorithms such as AES and RSA are…

Rapid CMOS device size reduction resulted in billions of transistors on a chip have led to integration of many cores leading to many challenges such as increased power dissipation, thermal dissipation, occurrence of transient faults and…

Asynchronous frameworks for distributed embedded systems, like ROS and MQTT, are increasingly used in safety-critical applications such as autonomous driving, where the cost of unintended behavior is high. The coordination mechanism between…

With the surge in IoT devices ranging from wearables to smart homes, prompt transmission is crucial. The Age of Information (AoI) emerges as a critical metric in this context, representing the freshness of the information transmitted across…

Efficiency and security have become critical issues during the development of the long-range (LoRa) system for Internet-of-Things (IoT) applications. The centralized work method in the LoRa system, where all packages are processed and kept…

Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on different robot platforms, generalist cross-embodiment robot…

Recently, implementing Radio Access Network (RAN) functionalities on cloud-based computing platform has become an emerging solution that leverages the many advantages of cloud infrastructure, such as shared computing resources and storage…

The widespread significance of Android IoT devices is due to its flexibility and hardware support features which revolutionized the digital world by introducing exciting applications almost in all walks of daily life, such as healthcare,…

This paper considers a cooperative Internet of Things (IoT) system with a source aiming to transmit randomly generated status updates to a designated destination as timely as possible under the help of a relay. We adopt a recently proposed…

The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the absence of benchmarks that realistically capture the interplay…

The emerging industrial metaverses realize the mapping and expanding operations of physical industry into virtual space for significantly upgrading intelligent manufacturing. The industrial metaverses obtain data from various production and…

Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four limitations: (1) Robustness, with single points of failure vulnerable to attacks and…

Edge intelligence enables AI inference at the network edge, co-located with or near the radio access network, rather than in centralized clouds or on mobile devices. It targets low-latency, resource-constrained applications with large data…

The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large…

We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framework, which incorporates large-scale, progressive,…

Metareasoning, a branch of AI, focuses on reasoning about reasons. It has the potential to enhance robots' decision-making processes in unexpected situations. However, the concept has largely been confined to theoretical discussions and…