The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

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.

High-performance real-time wireless connectivity is at the heart of the Industrial Internet of Things (IIoT). Realizing wireless closed-loop control is crucial for various mission-critical IIoT systems. The existing wireless technologies fall short of meeting the stringent performance requirements of the closed-loop control. This article presents a novel wireless solution, called generalized closed-loop control of processes (GALLOP), for realizing closed-loop control over multihop or single-hop networks. GALLOP adopts a pragmatic design approach for addressing the challenges of wireless closed-loop control. The key design aspects of GALLOP include control-aware bidirectional scheduling for cyclic exchange, robust retransmission techniques based on cooperative multiuser diversity, and low-overhead signaling for scalable operation. GALLOP has been specifically designed for control loops that are closed over the whole network with dynamics on the order of a few milliseconds. The performance evaluation based on extensive system-level simulations and hardware implementation on a Bluetooth 5 testbed demonstrates that GALLOP provides high-performance connectivity with very low and deterministic latency, very high reliability and high scalability, to meet the stringent requirements of wireless closed-loop control for versatile IIoT applications.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This paper fills this gap and provides the first extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by de-synchronization and beating on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based flooding protocols in the presence of radio interference on a large-scale, and derive important insights on how the used physical layer affects their dependability.

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.

High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people. However, these technologies can be used to lend their highly accurate localisation capabilities to low-cost, commodity, and less-accurate technologies. In this paper, we bridge this link by proposing a technology-agnostic calibration framework based on artificial intelligence to assist such low-cost technologies through highly accurate localisation systems. A single-layer neural network is used to calibrate a less accurate technology using a more accurate one such as Bluetooth Low Energy (BLE) using Ultra-Wideband (UWB) and UWB using a professional motion tracking system. On a real indoor testbed, we demonstrate an improvement in accuracy of approximately 70% for BLE and 50% for UWB. Not only does the proposed approach require a very short measurement campaign, the low complexity of a single-layer neural network makes it ideal for deployment on constrained devices typically used for localisation purposes.