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.

This study presents a novel approach for managing out-of-the-box multi-mobile robotic systems designed to optimise area coverage and maintain wireless mesh connectivity. We introduce an area coverage assessment scheme that enables robot swarms to adjust their numbers dynamically, enabling real-time monitoring of dynamically changing environments with sufficient robots. Through decentralized control, the robots adapt to the environment size and geometry without prior environmental knowledge while maintaining ad-hoc connectivity by regulating inter-robot distances. Simulation results validate the system’s scalability and demonstrate its capability to achieve efficient area coverage with a self-regulated swarm size. This technology has broad applications, ranging from real-time inventory management in intralogistics to urban monitoring in smart cities and rapid disaster relief.

Although desirable, achieving a high-fidelity digital twin of buildings often requires a substantial influx of real-time data, demanding a dense network of environmental sensors. Regrettably, factors such as high hardware expenses, deployment constraints, and sensor malfunctions can impede the realization of such digital twins. In this demonstration, we introduce a pioneering approach that harnesses data-driven virtual sensing within a real-time, high-fidelity 3D digital twin of a building environment. Our innovative method provides accurate machine learning-based inference of real-time sensor variables across both two-dimensional (varying rooms) and three-dimensional (diverse elevations) domains, with limited reliance on physical sensor inputs. Thus, by extending the sensing coverage through the use of ML-driven virtual sensing, we are able to create a more accurate digital twin. Our initial results indicate a mean absolute percentage error of2, when compared against the ground truth physical sensors.

Internet of Things (IoT) networks require regular firmware updates to ensure enhanced security and stability. As we move towards methodologies of codifying security and policy decisions and exchanging them over IoT large-scale deployments (security-as-a-code), these demands should be considered a routine operation. However, rolling out firmware updates to large-scale networks presents a crucial challenge for constrained wireless environments with large numbers of IoT devices. This paper initially investigates how the current state-of-the-art protocols operate in such adverse conditions by measuring various Quality-of-Service (QoS) Key Performance Indicators (KPIs) of the shared wireless medium. We later discuss how Concurrent Transmissions (CT) can extend the scalability of IoT protocols and ensure reliable firmware roll-outs over large geographical areas. Measuring KPIs such as the mesh join time, the throughput, and the number of nodes forming a network, we provide great insight into how an IoT environment will behave under a large-scale firmware roll-out. Finally, we conducted our performance investigation over the UMBRELLA platform, a real-world IoT testbed deployed in Bristol, UK. This ensures our findings represent a realistic IoT scenario and meet the strict QoS requirements of today’s IoT applications.

This paper provides details of the collaborative robotics testbed platform that has been developed within the UMBRELLA project. The testbed is part of a larger open Industrial IoT testbed which is currently being deployed in the UK. The aim of the testbed is to permit flexible experimentation using different end devices (including smart city sensing and robot nodes) to evaluate algorithms or new practical application scenarios. For the collaborative robotics testbed this relates to warehouse robotics, which can move pallets of different sizes and shapes. The testbed also includes simulator facilities for validation of algorithms prior to deployment on the robot nodes. The nodes support a rich set of sensors, actuators, and wireless communication technologies. The software architecture is based on Docker containers and ROS2 DDS middleware for flexible and extensible evolution to support future sensors or network technologies. This will be provided as an open testbed to support research, experimentation and evaluation of swarm robotics and other Industrial IoT use-cases.

Various legacy and emerging industrial control ap-plications create the requirement of periodic and time-sensitive communication (TSC) for 5G/6G networks. State-of-the-art semi-persistent scheduling (SPS) techniques fall short of meeting the requirements of this type of critical traffic due to periodicity misalignment between assignments and arriving packets that lead to significant waiting delays. To tackle this challenge, we develop a novel recursive periodicity shifting (RPS)-SPS scheme that provides an optimal scheduling policy by recursively aligning the period of assignments until the timing mismatch is minimized. RPS can be realized in 5G wireless networks with minimal modifications to the scheduling framework. Performance evaluation shows the effectiveness of the proposed scheme in terms of minimizing misalignment delay with arbitrary traffic periodicity.

Recent applications in large-scale wireless mesh networks (WSN), e.g., Advanced Metering Infrastructure (AMI) scenarios, expect to support an extended number of nodes with higher throughput, which cannot be sufficiently supported by the current WSN protocols. Two prior protocols, Wi-SUN Field Area Network (Wi-SUN FAN) and IETF 6TiSCH standards, are popularly used that are respectively based on asynchronous Carrier Sense Multiple Access / Collision Avoidance (CSMA/CA) and IEEE 802.15.4 Time Scheduled Channel Hopping (TSCH). However, the former one with CSMA/CA can be prone to the Hidden Node Problem (HNP) that leads to a degradation of reliability, while the latter one with TSCH avoids HNP by using synchronously scheduling but requires massive control signalling that degrades the upper-bound of throughput. Accordingly, this paper tackles the challenge of how to improve the upper-bound of throughput without loss of reliability. To do so, we first present an in-depth evaluation of reliability and throughput between the two existing standards via system-level simulation. We then propose a self-configurable grouping (SCG) method to cluster nodes without using location information of each node. This SCG method eliminates around 99% HNP in CSMA/CA, thus greatly improves its reliability with maintaining the relatively high upper-bound of throughput. Our results show that the SCG method almost doubles the network throughput compared with both 6TiSCH and Wi-SUN FAN in heavy traffic scenarios, while providing extremely high reliability of more than 99.999%.

RPL, the de-facto standard for low power and lossy networks, forms multi-hop routing structures between network nodes and a single root. It intrinsically minimises the number of hops across the mesh, however this can result in large distances between adjacent forwarding nodes. Consequently, during forwarding operations, the received signal strength can be close to the receiver sensitivity threshold and result in frequent packet losses due to link fluctuations. RECLAIM, our proposed approach, overcomes this challenge by transmitting routebuilding messages at a reduced power level at first while switching back to the normal transmission power level during the actual data communication phase. This results in more reliable links per hop and ensures link budget above the receiver sensitivity threshold. This however creates more hops and a longer routing path in order to reach destination. Hence, RECLAIM further applies efficient and non-conflicting 6TiSCH scheduling method to coordinate those communication events in a non conflicting manner which do not collide or cause interference to each other, resolving the issue that has traditionally prevented this approach. Our detailed simulation shows that RECLAIM drops 60 times less packets than standard RPL, and achieves 99.9999% packet delivery ratio.

When a person is performing daily activities (e.g. walking) in the context of a WBAN application, the channel quality between the worn sensor devices and the hub can vary due to the switching of Line-Of-Sight (LOS) and None-Line-Of-Sight (NLOS) statuses among the sender and the receiver. Therefore, motion aware wireless MAC protocols are designed in order to enhance communication reliability and to avoid wasting energy on unnecessary wireless re-transmissions during most of the NLOS communications. Despite its importance, the prerequisite step of accurate and energy efficient motion detection remains as an assumption in most of the existing motion aware WBAN protocols. Hence in this paper, we propose an optimized real-time gesture detection method and implement it in a motion aware WBAN communication protocol. Wireless communications only take place when the channel condition is good, while data is buffered otherwise. It is lightweight and tailored to perform fast with accurate detection that suits embedded devices with limited memory size and relatively low MCU processing speeds. Experiments are conducted using both simulation and real-life hardware devices with 6 volunteers. The proposed motion detection method showed 99.28% off-line accuracy and 92.5% online accuracy, respectively. Thanks to which, the communication results yielded a promising 82% improvement in packet drop reduction and 32.8% improvement in energy efficiency compared to conventional methods.

The Time Synchronised Channel Hopping (TSCH) MAC, adopted in popular WirelessHART, ISA100.11a and IEEE802.15.4e, is emerging as the de-facto technique for critical wireless industrial monitoring and control applications. However, much of its benefits are tied to the ability of the protocols and the scheduling techniques to optimally route traffic and orchestrate communication over individual links in a non- conflicting manner. Existing routing and scheduling approaches employ complex algorithms requiring excessive signalling overhead. While some of them can build optimal solutions under static conditions, they lack the agility to adapt to changes in network topology, traffic, and link qualities. To overcome these limitations, we propose BOOST, a protocol that blends TSCH with opportunistic routing and an effortless scheduling technique. BOOST complements the native support of spatial and channel diversity of the former with receiver diversity of the latter, pushing the envelope of network reliability to over 99.99%. Its ultra-lightweight autonomous scheduling mechanism adapts the communication schedule to network changes instantly with almost zero signalling overhead when other solutions struggle with tens of seconds or even minutes of delay with low reliability during this period.

Internet of Things (IoT) has become a hot research topic recently. IoT networks are expected to integrate billions of small devices to the Internet enabling countless applications ranging from automation of cities to home based healthcare solutions for elderly and vulnerable population. Such wide range of application pose unique challenges such as the need for high reliability, ultra low power and low delay communications. In addition to these challenges, such Internet enabled small devices will have to be equipped with the necessary security suits to cope with security challenges posed by the Internet. Furthermore, these solutions have to address such unique challenges via a micro controller generally with a limited processing power and memory consuming as little energy as possible. This paper presents an extension to the secure bootstrapping mechanism of the newly introduced IETF 6TiSCH protocol where the authentication keys are distributed within the trusted nodes of the IoT network to enable an efficient authentication and bootstrapping process. Using a distributed approach, we aim to reduce the communication overhead of the standard IETF 6TiSCH authentication mechanism and improve energy efficiency of the network by keeping authentication tokens at the edge of the IoT network.

Time-Slotted Channel Hopping (TSCH) is a part of an emerging IEEE 802.15.4e standard to enable deterministic low-power mesh networking. It promises to pave the way to the future Internet of (Important) things by offering high reliability and low latency for wireless industrial applications. Nonetheless, the standard only provides a framework but it does not mandate a specific scheduling mechanism. In this paper, we propose a centralized Adaptive MUlti-hop Scheduling method (AMUS) based on the latest TSCH MAC. AMUS first enables sequential multi-hop scheduling to provide low latency guarantee for time-critical applications. Secondly, the novel tentative cell allocation method allocates additional resources to vulnerable links such that possible MAC retransmissions can be accommodated within the same slotframe, hence significantly reducing the delay caused by interference or collisions. Last but not least, the battery power of the node can be further conserved by adopting the proposed End-of-Q notification mechanism. Preliminary simulation results have confirmed that AMUS outperforms other popular scheduling algorithms in the literature.

With the advent of Machine to Machine (M2M) communication, we are witnessing an increased interest towards technologies that will enable efficient and reliable operation of Wireless Sensor Networks (WSN). Such networks are expected to include a large number of sensor devices which will generate large body of M2M traffic. To reduce the impact of this M2M traffic, efficient storage and retrieval methods should be employed in a distributed manner for the successful deployment of this technology. In this paper, a distributed storage solution is presented with the aim of reducing the impact of M2M traffic on data centres and the network backbone. The reliable and efficient storage of the sensor data is established by taking advantage of codes with Maximum Distance Separable (MDS) properties. The solution is implanted in Contiki OS using RPL protocol [1] and its performance is evaluated through simulations. Furthermore, to realise such a system, a centralised clique finding algorithm is demonstrated and benchmarked against a solution that uses a brute force approach.

In low-power and lossy wireless networks, message delivery could be jeopardized by factors such as channel conditions, noise, interference etc. This can lead to communication retransmissions, resulting in significant energy depletion and consequently consumes limited on-node energy resources. This paper elaborates on a two-pronged approach to improve energy efficiency and reliability of communications: a) the application of content-centric data aggregation (pre-processing of correlated information) at each node to reduce traffic volume, and b) the use of link quality information to estimate local network lifetime while routing traffic. A novel objective function is proposed which ties the two together and findings from a performance evaluation are presented. It is shown that the proposed mechanism is able to achieve significant energy savings and prolongs the network lifetime in energy constrained multi-hop networks with lossy wireless links.

Selfishness and altruism are two distinctive node behaviors in wireless networks. Users of network nodes, e.g. mobile phones, would like their individual devices to be operational for as long as possible. On the other hand, users and operators of wireless networks may wish for an overall long network lifetime via collaborative node operations. In contrast to the most of the work done till now, in this paper, we consider a node-centric scenario, where a node can balance its energy consumption according to its needs to prolong the node lifetime. More specifically, the energy balancing problem is defined as an inventory control problem, where the node may choose to be selfish or unselfish with various degrees having in mind a range of parameters, such as its battery state and the existence and rate of energy harvesting. Each node can use the proposed solution to allocate the available energy between internal (node itself) and external (collaborative) tasks subject to dependencies among application and communication layers and the node operating system. We also discuss a number of other possible extensions of the method to include interactions among different nodes in the network. To illustrate one such an extension, a case study with a large number of nodes among two separate but co-located wireless mesh networks is presented. By applying the inventory balancing algorithm, considerable increase in the network lifetime is shown for both networks.

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.