Swarm robotics focuses on designing and coordinating large groups of relatively simple robots to perform tasks in a decentralised and collective manner. The swarm provides a resilient and flexible solution for many applications. However, contemporary swarm robots have a significant power problem in that secondary (i.e. rechargeable) batteries are slow to charge and offer lifetimes of only a few years, increasing maintenance costs and pollution due to battery replacement. We imagine a different future, wherein battery-free robots powered by supercapacitors can be recharged in seconds, offer long-life autonomous operation and can rapidly pass charge between one another using trophallaxis. In pursuit of this vision, we contribute the CapBot, a battery-free swarm robot equipped with Mecanum wheels, a Cortex M4F application processor and Bluetooth Low Energy networking. The CapBot fully recharges in 16 s, offers 51 min of autonomous operation at top speed, and can transfer up to 50 % of its available charge to a peer via trophallaxis in under 20 s. The CapBot is fully open source and all software and hardware source is available online.

A growing range of networked embedded devices are moving away from batteries and towards super-capacitor charge storage. However, mobile robots remain largely dependent upon batteries with slow recharge cycles and limited lifetimes. In this demonstration paper, we introduce a novel battery-free platform for swarm robotics which features: 24 minutes of operation running at its top speed of 1.24 km/h, a carrying capacity of over 2.5kg, full recharge cycles of under 12 seconds and rapid peer-to-peer charge transfer or trophallaxis in the field. This is supported by an nRF52840 Cortex-M4F equipped with BLE/ANT/802.15.4 transceiver. Notably, while the autonomy of FreeBots is limited compared to battery-powered robots, their operational vs charging duty-cycle is significantly higher at over 99%.

Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.

Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential to greatly expand the range and scope of vibration monitoring applications by delivering long-life wireless sensors that can be cost-effectively embedded in hard to reach places such as; within machines, infrastructure or the built environment. However, achieving this vision is difficult due to the stringent resource constraints of contemporary IoT devices and networks. This has led the research community to develop a creative range of application-specific near-sensor processing firmware. However, systematic support for generic vibration monitoring on resource-poor IoT networks remains an open problem. We tackle this challenge by introducing ReFrAEN, a software framework that efficiently enables a wide range of vibration monitoring applications on IoT networks. ReFrAEN achieves this through a deeply configurable combination of compression techniques and data processing algorithms. These features allow end-users to effectively trade-off between resource consumption and data resolution in order to meet battery life constraints while preserving sufficient data quality to support the target application. Our evaluation shows that ReFrAEN is capable of identifying bearing faults, while dramatically improving battery lifetime and reducing latency in comparison to prior approaches.

Recent innovations in energy harvesting promise extended operational life and reduced maintenance costs for the next generation of Internet of Things (IoT) platforms. However, energy management in these platforms remains problematic due to dynamism in energy supply and demand, inefficiency in storing and converting energy and a lack of per-task charge isolation. This paper tackles this problem by proposing a software defined charge storage module called Morphy, which combines a polymorphic capacitor array with intelligent power management software. Morphy delivers energy to application tasks in a flexible, efficient, and isolated manner. Morphy provides two software extensions to the Operating System scheduler: the energy semaphore blocks the execution of tasks until sufficient charge is available to safely run them, and the energy watchdog monitors and mitigates energy management bugs. We have realized a prototype of Morphy with the hardware form factor of a standard 9V (PP3) battery package and a software library that integrates with the FreeRTOS scheduler. Our evaluation shows that, in comparison to standard energy storage and management approaches, our prototype reaches an operational voltage more quickly, sustains operation longer in the case of power failure and effectively isolates charge storage for dedicated tasks with minimal compute, memory and energy overhead.

Accurate indoor positioning remains an open research question. Existing solutions are either expensive, short-range, or inaccurate. A new approach is therefore required to cost-effectively and accurately support localization in large indoor environments. We tackle this problem by introducing a practical optical localization scheme, called OSLo, that cost-effectively scales to support large buildings. OSLo uses a meshed network of low-cost cameras as localization anchors and smart LEDs as tags, which transmit their IDs and context sensor data over the meshed cameras using optical communication. OSLo is capable of localizing dense deployments of tags with an accuracy of under 1 meter at a distance of 35 meters from the localization anchor. Furthermore, smart LED tags can be manufactured for less than $1. We systematically evaluate the performance of OSLo in the context of a real-world car localization use-case at Ford Motor Company in Germany and demonstrate promising results in terms of detection distance, and localization accuracy.

The Internet of Things (IoT) is being deployed in an ever-growing range of applications, from industrial monitoring to smart buildings to wearable devices. Each of these applications has specific computational requirements arising from their networking, system security, and edge analytics functionality. This diversity in requirements motivates the need for adaptable end-devices, which can be re-configured and re-used throughout their lifetime to handle computation-intensive tasks without sacrificing battery lifetime. To tackle this problem, this article presents Chimera, a low-power platform for research and experimentation with reconfigurable hardware for the IoT end-devices. Chimera achieves flexibility and re-usability through an architecture based on a Flash Field Programmable Gate Array (FPGA) with a reconfigurable software stack that enables over-the-air hardware and software evolution at runtime. This adaptability enables low-cost hardware/software upgrades on the end-devices and an increased ability to handle computationally-intensive tasks. This article describes the design of the Chimera hardware platform and software stack, evaluates it through three application scenarios, and reviews the factors that have thus far prevented FPGAs from being utilized in IoT end-devices.

The Internet of Things (IoT) is being deployed at large scale in a wide range of long-life applications. Examples range from Industry 4.0 to smart lighting systems. These applications have diverse requirements of non-volatile storage. However, the flash memory that is used in today's IoT devices offers limited write endurance and must therefore be carefully managed if applications are to deliver on their promises of multiyear lifetimes. Managing the health of flash memory is difficult for application developers, as it requires in-depth hardware and software knowledge, which often needs to the problem being neglected. While various techniques have been proposed to preserve the health of flash memory, prior work tends to focus on a single hardware platform and data type. Furthermore, prior work does not provide lifetime guarantees. This paper tackles this problem by proposing MicroVault, a simple and unified interface for reliable non-volatile data storage on resource-constrained IoT devices. MicroVault enforces developer-specified lifetime guarantees through a range of lifetime extension techniques, which are adaptively applied based upon the needs of the application. Evaluation shows that MicroVault dramatically extends the lifetime of flash memory while minimising overhead.

The performance dichotomy between wired and wireless networks for the Internet of Things primarily arises from the inherent complexity and inefficiency of networking abstractions such as routing, medium access control and store-and-forward packet switching. This paper aims to enable a new class of latency-sensitive applications by breaking all three of these abstractions to deliver a performance envelope that resembles that of a wired bus in terms of deterministic latency and throughput. The essence of this approach is a novel networking paradigm for optical wireless communication, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmit LED-based signals. This paper realises the paradigm within a platform called Zero-Wire and evaluates it on a 25-node testbed under laboratory conditions. Key end-to-end performance measurements on this physical prototype include 19 kbps of contention-agnostic goodput, interface-level latency under 1 ms for two-byte frames across four hops, jitter on the order of 10s of μs, and a base reliability of 99%. These first results indicate a bright future for the under-explored area of optical wireless mesh networks in delivering ubiquitous connectivity through a simple and low-cost physical layer.

Despite the ubiquitous deployment and development of wireless technology for the Internet of Things (IoT), contemporary radio frequency (RF)-based solutions still cannot match the performance of a "wire" in terms of latency and throughput. This abstract presents a demonstration of Zero-Wire, a novel optical wireless approach that addresses this gap to enable latency-sensitive IoT applications. The essence of this approach is a new networking paradigm, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmits optical signals. The demonstration setup is composed of 25 Zero-Wire nodes, forming a mesh network, and the demo showcases the network's behavior during a series of transmissions. End-to-end performance measurements include 19 kbps of contention-agnostic goodput, latency under 1 ms for two-byte frames, jitter on the order of 10s of μs, and a base reliability of 99%.

Creating, deploying and configuring applications for Internet of Things (IoT) scenarios today remains complex and costly for many users. The MicroPnP platform addresses this complexity problem and provides a true zero-configuration and standards-based solution that radically reduces the cost of acquiring, building, and operating wireless sensing and actuation IoT systems at scale. MicroPnP combines true Plug-and-Play integration of sensing and actuation peripherals with ultra-reliable wireless mesh networking and extreme battery lifetimes. MicroPnP was awarded in an international IoT competition, and is currently being successfully used in commercial IoT scenarios.

Internet of Things (IoT) applications require diverse sensors and actuators. However, contemporary IoT devices provide limited support for the integration of third-party peripherals. To tackle this problem, we introduce μPnP: a hardware and software solution for plug-and-play integration of embedded peripherals with IoT devices. μPnP provides support for: driver development, automatic integration of third-party peripherals, discovery and remote access to peripheral services. This is achieved through a low-cost hardware identification approach, a lightweight driver language and a multicast network architecture. Evaluation shows that μPnP has a minimal memory footprint, reduces development effort and provides true plug-and-play integration at orders of magnitude less energy than USB.