The latency gap between wired and wireless networks poses a challenge in the adoption of wireless technologies in latency-sensitive scenarios. The gap is especially notable in multi-hop communication typical for industrial sensor networks and robotic swarms. The main reason behind it is that commonly used wireless protocols rely on store-and-forward routing and costly overhead procedures to avoid interference. This article introduces RF-Zero-Wire, an RF-based symbol-synchronous communication protocol. Instead of relaying the whole frame per hop in a store-and-forward manner, nodes concurrently relay the frame symbol by symbol, without the need for tight time synchronization. Based on data collected in real-world experiments, we reveal that the inevitable carrier frequency offsets (CFOs) introduced by imperfect crystal oscillators cause a beating effect under concurrent symbol transmissions. This is characterized by periodic constructive and destructive interference, which significantly affects reliability. Subsequently, a thorough simulation study shows how the beating problem can be overcome with error correction codes. RF-Zero-Wire allows achieving an end-to-end latency of less than 1 ms for a small 4-byte frame transmitted across 5 hops. Moreover, latency is shown to increase only by 0.16% per extra hop for 16-byte frames, which is negligible compared to the over 100% per-hop latency increase observed in store-and-forward protocols. The trade-offs between network reliability and CFO range, communication distance, node density, and achievable data rate are studied in large-scale experiments based on simulation.

Body area networking is an important segment of the wireless landscape, connecting a growing range of devices such as fitness trackers and smart watches using short range wireless links. However, conventional wireless technologies such as BLE and ANT provide poor support for low-latency applications such as haptic feedback due to their dependence upon duty-cycling to maintain acceptable power consumption. Recent physical-layer design efforts introduce a low-power alternative by establishing capacitively coupled communication through the human body. This mitigates duty-cycling requirements, but results in channel access times similar to those found in more conventional wireless networks, failing to provide adequate latency guarantees even for modestly sized networks. We address this problem by introducing ENOCH, an ultra-low power 12 MHz receiver that relies on capacitive coupling to establish a fieldbus-like through-body network. ENOCH delivers a sub-μW receiver, 10 kbps link-layer throughput, traffic prioritisation, 97% baseline reliability and 7 ms latency in a 7-node network, regardless of contention for the channel. Our design is simple and uses only commercial off-the-shelf components. As it requires neither large antennas nor crystal oscillators, we believe that further miniaturization beyond the current 1 cm2hardware/software prototype is possible.

Today’s wireless sensor networks focus on achieving reliable data transfer over a lossy medium at the expense of latency. However, sensor data are often noisy and thus only lossily characterise real-world phenomena, rendering their exact transfer wasteful. Furthermore, many next-generation privacy-sensitive applications, such as smart grid control, real-time distributed object tracking, and inter-vehicle federated learning face latency and traffic bottlenecks due to the sheer amount of data collection required to overcome noise. We tackle this problem by introducing Twofer, a communication approach which reduces latency and traffic in high-noise or high-privacy settings by abandoning the focus on reliable networking. Twofer empowers developers to tune networks for latency-bound rather than reliability-bound performance; the system coordinates ambiguous transmissions, which are used to estimate the network-wide distribution of data, rather than to reliably communicate exact data from individual nodes. Twofer’s full-stack design maintains black-box compatibility with existing application code, but advocates for, and shows the value of, uncommon physical-layer features such as symbol-synchronous communication. The system is therefore implemented and evaluated on a prototype low-latency wireless mesh network called Zero-Wire. Experiments using state-of-the-art local differential privacy protocols show 25–75% latency reductions relative to conventional approaches. The results are also future-proof, with performance advantages increasing with the strength of the privacy guarantees that are offered.

Current physical-layer networking technology primarily optimises for reliability rather than latency, thus unnecessarily impairing latency-sensitive applications such as real-time control in embedded, wireless, and dense networks. This extended abstract introduces a set of novel low-level networking primitives that optimise such networks for latency, pushing latency-inducing failures up the stack. The development of and experiments on a 25-node prototype network show that the resulting symbol-synchronous architecture unlocks application scenarios formerly reserved for wired networks, offering sub-millisecond network latency across multiple hops for real-time control, in-the-loop classification based on distributed data from 10s of nodes in milliseconds, and a 25--75% traffic reduction when data is subject to large measurement noise.

Intelligent Environments (IEs) enrich the physical world by connecting it to software applications in order to increase user comfort, safety and efficiency. IEs are often supported by wireless networks of smart sensors and actuators, which offer multi-year battery life within small packages. However, existing radio mesh networks suffer from high latency, which precludes their use in many user interface systems such as real-time speech, touch or positioning. While recent advances in optical networks promise low end-to-end latency through symbol-synchronous transmission, current approaches are power hungry and therefore cannot be battery powered. We tackle this problem by introducing BoboLink, a mesh network that delivers low-power and low-latency optical networking through a combination of symbol-synchronous transmission and a novel wake-up technology. BoboLink delivers mesh-wide wake-up in 1.13ms, with a quiescent power consumption of 237µW. This enables building-wide human computer interfaces to be seamlessly delivered using wireless mesh networks for the first time.

Sensor networks gather data at one or more gateway devices by sequentially receiving data frames from individual sensor nodes. In cases where applications rely on data that is distributed across sev-eral nodes, the latency of such node-by-node data collection scales with the number of nodes and the frame sizes involved, impeding fast decision-making. This paper seeks to overcome that limitation for latency-sensitive classification problems by introducing One-Take, a cross-layer aggregation protocol that aims to decouple the process of data gathering from network size and frame length. The protocol leverages dominance properties between concurrently transmitted symbols to convey socalled collages, messages that de-scribe relationships between data held by several nodes, rather than local information from any single node. Collages thus transmit a hash of distributed data that is computed within the physical layer, thereby communicating more informative features than regular frames of the same length. Experiments on a recent wireless mesh network platform, called Zero-wire, demonstrate that two-byte collages are delivered correctly 99% of the time and, using public sensor network data sets, show that One-take achieves a latency reduction of circa 42% relative to node-by-node transmissions.

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%.

Applications for the Internet of Things (IoT) are often data-centric. Data-centric routing then enables messages to reach relevant consumers while avoiding flooding and explicit resource discovery. This reduces the amount of transmissions required to support relevant applications and provides energy savings as well as a convenient programming abstraction: messages can be addressed to nodes that advertise features matching a constraint. In low-power wireless mesh networks, such feature-oriented routing traditionally relies on costly and inflexible network overlays. Recent work establishes lightweight support for diverse data-centric traffic patterns, but sacrifices expressiveness of feature-oriented functionality and hence applicability. This paper overcomes that trade-off by introducing SMRFET, a multicasting system that integrates data-centric functionality into a standard low-power network stack. SMRFET thus improves over the art: it offers more expressive addressing mechanics (range queries over a node's features) at lower implementation and runtime cost (no additional networking mechanisms). Additionally, SMRFET can be configured to handle memory constraints: its performance degrades gracefully as the designated amount of memory decreases. SMRFET therefore brings lightweight and expressive group communication to wireless IoT networks.

Applications for the Internet of Things (IoT)are often data-centric. Data-centric routing then enables messages to reach relevant consumers while avoiding flooding and explicit resource discovery. This kind of routing thus provides energy savings as well as a convenient programming abstraction: messages can be addressed to nodes that advertise features matching a constraint. In low-power wireless mesh networks, such feature-oriented routing traditionally relies on costly and inflexible network overlays. Recent work establishes lightweight support for diverse data-centric traffic patterns, but sacrifices expressiveness of feature-oriented functionality and hence applicability. It is also unclear whether the energy consumption advantages offered by data-centric routing extend to this new lightweight approach. To address these concerns, this paper introduces the SMRFET system. SMRFET improves the expressiveness of state-of-the-art feature-oriented routing by supporting numeric rather than binary features. The system integrates data-centric functionality into group-based multicast and thus adds only a small amount of overhead: experiments show that SMRFET significantly reduces the required amount of message passing relative to alternative systems for group communication. Additionally, SMRFET can be reconfigured to handle memory constraints: its performance degrades gracefully as the amount of memory allocated to it decreases. SMRFET therefore brings lightweight and expressive group communication to the wireless IoT.