Improving End-to-end Quality of Service for Low-power Wireless Sensor Networks
18. September 2017, 16:00 , 17:30
Nowadays, smart cities and intelligent transportation are infrastructure systems connecting human beings and are increasingly changing our daily life. Such systems are commonly defined as the Internet of Things (IoT) or Cyber-Physical Systems (CPS), where the entire physical world is closely associated with sensors, ma-chines, networked embedded devices and so forth. A wireless sensor network (WSN), a network that com-prises a large number of sensor nodes, has risen as a promising technology for IoT and CPS. In particular, such networks have to be energy-efficient and should provide sufficient quality of services (QoS), such as reliability, timeliness, security to name a few. However, QoS provision in WSNs is an extremely challenging task. This is because of, for instance, multihop communication over lossy low-power wireless channels, un-predictable and dynamic changes in the environment. Another reason is the bounded resources of the de-ployed devices with respect to computation capability, memory capacity, and energy budget. Besides, these unpredictable and non-deterministic QoS metrics are typically contradicting each other.
To address these challenges, this talk presents three novel techniques for low-power WSNs, as well as their analytical study and real-world implementations. Firstly, we provide a comprehensive survey of the state-of-the-art protocols that aim to optimize QoS for WSNs. Besides, we introduce a novel strategy, lifetime plan-ning (LP), to achieve best-effort application-level performance, that deliberately bounds the operational life-time to the expected task lifetime of WSNs. Moreover, we come up with a robust, reliable and energy-efficient multichannel opportunistic routing (MOR) primitive, that employs both opportunistic routing and opportun-istic multichannel hopping strategies, improving the robustness of the network to interference in WSNs. Fur-thermore, we propose less is more (LiM), an energy-efficient flooding protocol for WSNs, equipping itself with a machine learning capability to progressively reduce redundancy while maintaining high reliability. In addition, we implemented our protocols in Contiki OS and further evaluated them through extensive simula-tions and real-world experiments in testbed, i.e. FlockLab. By experimental results we show that our proposed protocols highly improve the multi-objective QoS metrics in WSNs, with respect to reliability, timeliness, and energy efficiency
Betreuer: Prof. Dr.-Ing. Oliver Theel