Valentin Poirot | Searcharxiv

arXiv2021

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 backbone of the Internet of Things. Bluetooth and BLE mitigate interference in the crowded 2.4 GHz band via Adaptive Frequency Hopping (AFH), spreading communication over the entire spectrum, and further allows the exclusion of interfered channels. However, exclusion is challenging in dynamic environments or with user mobility: as a user moves around, interference affects new channels, forcing AFH to deprive itself of new frequencies, while some other excluded channels are now free of losses but remain excluded. Channel re-inclusion is a primordial, yet often left out, aspect of AFH, as it is non-trivial to assess the new situation of excluded frequencies. We present eAFH, a mechanism for channel exclusion and inclusion. eAFH introduces informed exploration to AFH: using only past measurements, eAFH assesses which frequencies we are most likely to benefit from re-including in 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.

cs.NI↗

arXiv2020

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%).

cs.NI↗