Yichao Jin

Low Earth Orbit (LEO) satellite constellations have experienced rapid growth in recent years, driven by their potential to deliver global, high-bandwidth Internet services with low latency. Beyond connectivity, LEO constellations also offer promising opportunities to enable in-orbit processing of space-native data to support a wide range of emerging space applications. In this context, the concept of space computing has been proposed, a paradigm that seamlessly integrates networking and computing to provide computing-as-a-service anytime and anywhere in space. However, the inherent characteristics of satellite constellations, such as dynamic network topologies, constrained system resources, and the harsh space environment, pose significant challenges in achieving this vision. This paper outlines the system architecture and the key enabling technologies for space computing, including spaceborne computers, laser communications, spaceborne router, distributed operating systems, and onboard AI. We also present the implementation of an open space computing platform, the 3-Body computing constellation, along with the in-orbit experimental results that demonstrate the advantages of multi-satellite distributed computing. Furthermore, we outline future research directions essential for advancing toward a truly interconnected, autonomous, and intelligent space computing system.

The proliferation of multi-vendor autonomous systems (MVAS), such as shared warehouses, necessitates robust cooperation frameworks for robotic agents from different vendors. Ensuring operational integrity in these environments is a critical challenge, as the misbehaviour of a single agent can disrupt the entire system. This paper proposes a decentralised framework for secure and trustworthy multi-robot cooperation. Our approach leverages distributed ledger technology (DLT) to create an immutable record of operations and introduces a novel, lightweight Proof of Location (PoL) mechanism. This PoL allows agents to verify task completions both physically and cryptographically with minimal computational overhead. The framework is further strengthened by a real-time, algorithmic trust and reputation system that continuously evaluates agent behaviour. A key advantage over traditional methods is our support for asynchronous verification, which enhances both scalability and efficiency by removing centralised bottlenecks. Extensive simulations in a warehouse setting demonstrate that our framework effectively detects and mitigates malicious activities, thereby improving the security and performance of MVAS. The broader applicability of our approach is further illustrated through a conceptual case study in a smart city parking scenario.

Achieving a high-fidelity digital twin of buildings is desirable but often requires a substantial influx of real-time data, necessitating a dense network of environmental sensors. Additionally, high hardware expenses, deployment costs, constraints, and sensor malfunctions can impede the realization of such digital twins. In this paper, we introduce TwinSense, a pioneering system that harnesses data-driven virtual sensing within a real-time high-fidelity 3D digital twin of the building environment. Our innovative method utilizes machine learning-based inference to accurately estimate real-time sensor variables, such as temperature and CO2 levels, across both two dimensional (varying rooms) and three-dimensional (diverse elevations) domains, with limited reliance on physical sensors. By extending sensing coverage through ML driven virtual smart sensing, we create a more accurate digital twin for advanced building management. Our case study results across different seasons indicate an average mean absolute percentage error of 2% for temperature and approximately 5% for air quality parameters, such as CO2, when compared against ground truth physical sensors deployed in the multi-room segmented building. Furthermore, we propose a modified 3D-Inverse Distance Weighting (IDW) thermal interpolation method. Leveraging the comprehensive multi-camera-angle visualization capabilities facilitated by the Unreal Engine, our analysis revealed a potential anomaly within the air conditioning system. The building management team validated this observation during the real-world trial, affirming the initial efficacy of our solution.

UMBRELLA ^a is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and address holistically real-world technological challenges in a System-of-Systems (SoS) fashion. UMBRELLA provides open access to real-world devices and infrastructure, enabling researchers and the industry to evaluate solutions for Smart Cities, Robotics, Wireless Communications, Edge Intelligence, and more. Key features include over 200 multi-sensor nodes installed on public infrastructure, a robotics arena with 20 mobile robots, a 5G network-in-a-box solution, and a unified backend platform for management, control and secure user access. The heterogeneity of hardware components, including diverse sensors, communication interfaces, and GPU-enabled edge devices, coupled with tools like digital twins, allows for comprehensive experimentation and benchmarking of innovative solutions not viable in lab environments. This paper provides a comprehensive overview of UMBRELLA’s multi-domain architecture and capabilities, making it an ideal playground for Internet of Things (IoT) and Industrial IoT (IIoT) innovation. It discusses the challenges in designing, developing and operating UMBRELLA as an open, sustainable testbed and shares lessons learned to guide similar future initiatives. With its unique openness, heterogeneity, realism and tools, UMBRELLA aims to continue accelerating cutting-edge technology research, development and translation into real-world progress.

The latest 6TiSCH standard enables highly reliable industrial monitoring and control applications through deterministic wireless communications and efficient allocation of radio resources among wireless nodes. However, much of these benefits are tied to the ability of the scheduler in orchestrating communication over individual links in an efficient and a non-conflicting manner. State-of-the-art 6TiSCH scheduling algorithms only tackle the scheduling problem at the MAC layer with an assumption of having an optimal routing layer. Moreover, to the best of our knowledge, no solution so far has taken content-specific scheduling into account. This paper proposes CONCISE, which is a content-centric cross-layer scheduling solution. CONCISE creates content-independent routing topologies and schedulers, resulting in an overlaid routing structure and multiple content-based schedules. Further, CONCISE addresses in-network processing and data aggregation with the objective of reducing network traffic. Performance evaluation demonstrates that CONCISE can effectively reduce inner layer network traffic by up to 65%. As a result, it can provide up to 50% delay reduction and achieve significantly higher packet delivery ratio, compared to existing scheduling solutions. Initial proof of concept is implemented and evaluated in Contiki OS.

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.

Transporting raw data which is usually correlated over multi-hop wireless links can be costly both in terms of time as well as resources. This is likely to be a pressing problem particularly in the emerging Machine-to-Machine (M2M) communication paradigm wherein the amount of data is expected to proliferate. With in-network processing and data aggregation, the total amount of forwarding traffic on multi-hop routes can be significantly reduced by pre-processing of correlated information. However, a suitable data forwarding scheme is needed as a prior condition in order to efficiently compute and relay data. By taking advantage of distributed processing, this paper provides a content centric and load balancing aware distributed data routing solution for large-scale multi-hop M2M wireless networks. Independent routing decisions are made by each node to refine its next hop data forwarding node selection using only local information. Hence, it is highly adaptive to dynamic environments. A hybrid objective function is proposed for route selection which includes two main parts: 1) reduce the communication traffic by aggregating similar type of data, hence increasing the processing gain; 2) balance the energy-consumption among neighbouring nodes taking into account heterogeneous node residual energy levels so as to reduce both computation and communication costs and at the same time avoid early energy depletion of hot-spot nodes. We show that the proposed algorithm is able to extend the network lifetime and provides significant energy saving in complex large-scale Multihop wireless networks.