Collaborative, Intelligent, and Adaptive Systems for the Low-Power Internet of Things

With the emergence of the Internet of Things (IoT), more and more devices are getting equipped with communication capabilities, often via wireless radios. Their deployments pave the way for new and mission-critical applications: cars will communicate with nearby vehicles to coordinate at intersections; industrial wireless closed-loop systems will improve operational safety in factories; while swarms of drones will coordinate to plan collision-free trajectories. To achieve these goals, IoT devices will need to communicate, coordinate, and collaborate over the wireless medium. However, these envisioned applications necessitate new characteristics that current solutions and protocols cannot fulfill: IoT devices require consistency guarantees from their communication and demand for adaptive behavior in complex and dynamic environments.In this thesis, we design, implement, and evaluate systems and mechanisms to enable safe coordination and adaptivity for the smallest IoT devices. To ensure consistent coordination, we bring fault-tolerant consensus to low-power wireless communication and introduce Wireless Paxos, a flavor of the Paxos algorithm specifically tailored to low-power IoT. We then present STARC, a wireless coordination mechanism for intersection management combining commit semantics with synchronous transmissions. To enable adaptivity in the wireless networking stack, we introduce Dimmer and eAFH. Dimmer combines Reinforcement Learning and Multi-Armed Bandits to adapt its communication parameters and counteract the adverse effects of wireless interference at runtime while optimizing energy consumption in normal conditions. eAFH provides dynamic channel management in Bluetooth Low Energy by excluding and dynamically re-including channels in scenarios with mobility. Finally, we demonstrate with BlueSeer that a device can classify its environment, i.e., recognize whether it is located in a home, office, street, or transport, solely from received Bluetooth Low Energy signals fed into an embedded machine learning model. BlueSeer therefore increases the intelligence of the smallest IoT devices, allowing them to adapt their behaviors to their current surroundings

Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks

The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community

Dimmer: Self-Adaptive Network-Wide Flooding with Reinforcement Learning

The last decade saw an emergence of Synchronous Transmissions (ST) as an effective communication paradigm in low-power wireless networks. Numerous ST protocols provide high reliability and energy efficiency in normal wireless conditions, for a large variety of traffic requirements. Recently, with the EWSN dependability competitions, the community pushed ST to harsher and highly-interfered environments, improving upon classical ST protocols through the use of custom rules, hand-tailored parameters, and additional retransmissions. The results are sophisticated protocols, that require prior expert knowledge and extensive testing, often tuned for a specific deployment and envisioned scenario. In this paper, we explore how ST protocols can benefit from self-adaptivity; a self-adaptive ST protocol selects itself its best parameters to (1) tackle external environment dynamics and (2) adapt to its topology over time. We introduce Dimmer as a self-adaptive ST protocol. Dimmer builds on LWB and uses Reinforcement Learning to tune its parameters and match the current properties of the wireless medium. By learning how to behave from an unlabeled dataset, Dimmer adapts to different interference types and patterns, and is able to tackle previously unseen interference. With Dimmer, we explore how to efficiently design AI-based systems for constrained devices, and outline the benefits and downfalls of AI-based low-power networking. We evaluate our protocol on two deployments of resource-constrained nodes achieving 95.8% reliability against strong, unknown WiFi interference. Our results outperform baselines such as non-adaptive ST protocols (27%) and PID controllers, and show a performance close to hand-crafted and more sophisticated solutions, such as Crystal (99%)

eAFH: Informed Exploration for Adaptive Frequency Hopping in Bluetooth Low Energy

With more than 4 billion devices produced in 2020, Bluetooth and Bluetooth Low Energy (BLE) have become the dominant solutions for short-range wireless communication in IoT. BLE mitigates interference via Adaptive Frequency Hopping (AFH), spreading communication over the entire spectrum. However, the ever-growing number of BLE devices and WiFi traffic in the already crowded 2.4 GHz band lead to situations where the quality of BLE connections dynamically changes with nearby wireless traffic, location, and time of day. These dynamic environments demand new approaches for channel management in AFH, by both dynamically excluding frequencies suffering from localized interference and adaptively re-including channels, thus providing sufficient channel diversity to survive the rise of new interference.We introduce eAFH, a new channel-management approach in BLE with a strong focus on efficient channel re-inclusion. eAFH introduces informed exploration as a driver for inclusion: using only past measurements, eAFH assesses which frequencies we are most likely to benefit from re-inclusion into the hopping sequence. As a result, eAFH adapts in dynamic scenarios where interference varies over time. We show that eAFH achieves 98-99.5% link layer reliability in the presence of dynamic WiFi interference with 1% control overhead and 40% higher channel diversity than state-of-the-art approaches

STARC: Decentralized Coordination Primitive on Low-Power IoT Devices for Autonomous Intersection Management

Wireless communication is an essential element within Intelligent Transportation Systems and motivates new approaches to intersection management, allowing safer and more efficient road usage. With lives at stake, wireless protocols should be readily available and guarantee safe coordination for all involved traffic participants, even in the presence of radio failures. This work introduces STARC, a coordination primitive for safe, decentralized resource coordination. Using STARC, traffic participants can safely coordinate at intersections despite unreliable radio environments and without a central entity or infrastructure. Unlike other methods that require costly and energy-consuming platforms, STARC utilizes affordable and efficient Internet of Things devices that connect cars, bicycles, electric scooters, pedestrians, and cyclists. For communication, STARC utilizes low-power IEEE 802.15.4 radios and Synchronous Transmissions for multi-hop communication. In addition, the protocol provides distributed transaction, election, and handover mechanisms for decentralized, thus cost-efficient, deployments. While STARC’s coordination remains resource-agnostic, this work presents and evaluates STARC in a roadside scenario. Our simulations have shown that using STARC at intersections leads to safer and more efficient vehicle coordination. We found that average waiting times can be reduced by up to 50% compared to using a fixed traffic light schedule in situations with fewer than 1000 vehicles per hour. Additionally, we design platooning on top of STARC, improving scalability and outperforming static traffic lights even at traffic loads exceeding 1000 vehicles per hour

Paxos Made Wireless: Consensus in the Air

Many applications in low-power wireless networks require complex coordination between their members. Swarms of robots or sensors and actuators in industrial closed-loop control need to coordinate within short periods of time to execute tasks. Failing to agree on a common decision can cause substantial consequences, like system failures and threats to human life. Such applications require consensus algorithms to enable coordination. While consensus has been studied for wired networks decades ago, with, for example, Paxos and Raft, it remains an open problem in multi-hop low-power wireless networks due to the limited resources available and the high cost of established solutions.This paper presents Wireless Paxos, a fault-tolerant, network-wide consensus primitive for low-power wireless networks. It is a new flavor of Paxos, the most-used consensus protocol today, and is specifically designed to tackle the challenges of low-power wireless networks. By building on top of concurrent transmissions, it provides low-latency, high reliability, and guarantees on the consensus. Our results show that Wireless Paxos requires only 289 ms to complete a consensus between 188 nodes in testbed experiments. Furthermore, we show that Wireless Paxos\ua0stays consistent even when injecting controlled failures

BlueSeer: AI-Driven Environment Detection via BLE Scans

IoT devices rely on environment detection to trigger specific actions, e.g., for headphones to adapt noise cancellation to the surroundings. While phones feature many sensors, from GNSS to cameras, small wearables must rely on the few energy-efficient components they already incorporate. In this paper, we demonstrate that a Bluetooth radio is the only component required to accurately classify environments and present BlueSeer, an environment-detection system that solely relies on received BLE packets and an embedded neural network. BlueSeer achieves an accuracy of up to 84% differentiating between 7 environments on resource-constrained devices, and requires only ~12 ms for inference on a 64 MHz microcontroller-unit

Poster: Learning to Shine - Optimizing Glossy at Runtime with Reinforcement Learning

Glossy is a dissemination protocol that allows a node to propagate information to the entire network through constructive interference. We present GLossAI, a new artificial intelligence-based version of Glossy. We use reinforcement learning to determine and update Glossy’s parameters at runtime. Each node individually learns the best strategy to minimize energy consumption while maintaining high reliability. Furthermore, nodes can dynamically adapt their parameters to follow the dynamics of the medium

Paxos Made Wireless: Consensus in the Air

Many applications in low-power wireless networks require complex coordination between their members. Swarms of robots or sensors and actuators in industrial closed-loop control need to coordinate within short periods of time to execute tasks. Failing to agree on a common decision can cause substantial consequences, like system failures and threats to human life. Such applications require consensus algorithms to enable coordination. While consensus has been studied for wired networks decades ago, with, for example, Paxos and Raft, it remains an open problem in multi-hop low-power wireless networks due to the limited resources available and the high cost of established solutions.This paper presents Wireless Paxos, a fault-tolerant, network-wide consensus primitive for low-power wireless networks. It is a new flavor of Paxos, the most-used consensus protocol today, and is specifically designed to tackle the challenges of low-power wireless networks. By building on top of concurrent transmissions, it provides low-latency, high reliability, and guarantees on the consensus. Our results show that Wireless Paxos requires only 289 ms to complete a consensus between 188 nodes in testbed experiments. Furthermore, we show that Wireless Paxos\ua0stays consistent even when injecting controlled failures

Energy-Efficient Multi-Connectivity for Ultra-Dense Networks

In 5G systems, two radio air interfaces, evolved LTE and New Radio (NR), will coexist. By using millimeter waves, NR will provide high throughputs, but the higher frequencies will also lead to increased losses and a worse coverage. Multi-connectivity is therefore envisioned as a way to tackle these effects by connecting to multiple base stations simultaneously, allowing users to benefit from both air interfaces’ advantages. In this thesis, we investigate how multi-connectivity can be used efficiently in ultra-dense networks, a new paradigm in which the number of access nodes exceeds the number of users within the network. A framework for secondary cell association is presented and an energy efficiency’s condition is proposed. Upper and lower bounds of the network’s energy efficiency are analytically expressed. Algorithms for secondary cell selection are designed and evaluated through simulations. Multi-connectivity showed an improvement of up to 50% in reliability and and an increase of up to 20% in energy efficiency