Abstract

Technology is evolving towards a higher degree of automation and connectivity, with concepts of Pervasive Computing, Smart Factories, Cyber-Physical Systems (CPS) and the Internet of Things (IoT)promising to integrate countless communicating devices into objects around us at home, in the streets, and on industrial sites. These embedded devices are often very small in size, autonomously-powered, and have restricted computational and communicational capabilities. Low-power and Lossy Networks (LLNs) are multi-hop, typically wireless, self-organising networks aimed at interconnecting hundreds or thousands of such embedded devices. They inherit many techniques from wireless sensor networks, though going beyond their original task of collecting sensor readings. New applications comprising actuators, control loops, user interface devices and requiring connectivity of every ``smart thing'' with the Internet, pose new challenges to the network protocol stacks. These stacks should not only efficiently support data collection from numerous low-power sensors, but provide scalable data forwarding in the opposite direction, making every single device in the LLN addressable and reachable from a central controller or from the Internet. This type of forwarding is needed to send commands to wireless actuators in the LLN or to enable request-response communication between a low-power device and a remote server. Control loops additionally require real-time guarantees from the communication system. We demonstrate in this thesis that reconciling the battery lifetime with high reliability and low latency is still a challenge for existing protocols even at the scale of few hundreds of network nodes. Moreover, current techniques have a significant performance gap between their data collection and actuation forwarding components on memory-constrained platforms. This gap limits the applicability of the stacks, as the overall performance is determined by the weaker component. Motivated by two real-life applications, we first study novel techniques that eliminate the performance gap in the standard IPv6 stack for LLNs, making the actuation traffic forwarding as performant as the data collection one in networks that are five times larger than what the original standard stack is able to support. Second, we demonstrate that the reliability of packet delivery in the standard-compliant solution is limited in practice at around 99% while its routing overhead causes significant inefficiency in energy consumption. Therefore, we change focus to a forwarding mechanism based on the principle of synchronous transmissions, made popular by Glossy. It is a recent and, thus, non-standard technique, known for excellent reliability, speed and energy efficiency of the flooding-based data dissemination service it provides. This service is a perfect match for actuation, but a similarly efficient data collection protocol did not exist. To close this gap, we design Crystal, a novel data collection protocol based on the same core principle of synchronous transmissions. We show that, depending on the application, Crystal reaches per-mille or even parts-per-million radio duty cycle. It does that with a packet loss rate lower than 10e-5 under external Wi-Fi interference of a noisy office building, and provides a much higher reliability and energy efficiency than the state of the art under even stronger interference generated by JamLab. We thoroughly evaluate the proposed solutions both in realistic simulations and two large-scale testbeds. We follow a principled approach based on understanding of the environment and the properties of the network topologies. The latter are acquired by our connectivity assessment tool Trident, which itself is one of the contributions of this thesis. Through these contributions, this thesis pushes forward the applicability of LLNs, by improving their scalability, reliability, latency, energy efficiency and interference resilience, both in the context of an existing standard and in a clean-slate design. Further, by achieving this superior performance via network stacks that natively support both collection and actuation traffic, this thesis provides a stepping stone for applications that strongly rely on both, notably including the low-power wireless control applications.