Energy Efficient Communication Scheduling for IoT-based Waterbirds Monitoring: Decentralized Strategies
Abstract: Monitoring waterbirds have several benefits, including analyzing the number of endangered species, giving a reliable indication of public health, etc. Monitoring waterbirds in their habitat is a challenging task since the location is distant, and the collection of monitoring data requires large bandwidth. A promising technology to tackle these challenges is thought to be Wireless Multimedia Sensor Networks (WMSN). These networks are composed of small energy-constrained IoT devices that communicate together to collect data or monitor a given location. Performances in such networks are impacted by not only upper-layer protocols (transmission, routing, application layer) but also Medium Access Control (MAC) Layer. Therefore, improvement in this layer can increase the performance considerably. Traditional contention-based MAC modes like CSMA have large energy expenditure even though they have a good network performance profile. Energy-constrained devices cannot have a long lifespan with this type of MAC layer technology. Therefore, the IEEE 802.15.4e amendment proposed TSCH MAC mode which takes advantage of time-slotted access and channel hopping techniques. IETF integrated TSCH protocol into IPv6-based wireless sensor networks and standardized it as 6TiSCH which is a unique protocol stack for Low-Power and Lossy Networks (LLN). WMSN applications (e.g. Waterbirds Monitoring Application) generates heterogeneous traffic. Heterogeneous traffic can be defined as a mixture of different traffic types (light: temperature, humidity, etc. and heavy: audio, picture, video, etc.). TSCH-based WMSNs are considered a fit for this kind of traffic since they provide better performance and low power usage. Yet, the 6TiSCH Working Group left open the scheduling of TSCH communication for industries to make TSCH more easily adaptable to any kind of application. Until now, there have been a huge number of scheduling algorithms from industries and academia. Each scheduling algorithm has a different objective that maximizes the network performance of a specific application. This thesis work studies the most recent state-of-the-art scheduling algorithms (protocols) and compares them in a unique simulation environment with heterogeneous traffic to find out which protocol performs well while maintaining low energy consumption. Particularly, this work studies a new approach in TSCH scheduling which is Reinforcement Learning based scheduling. We implemented one of the state-of-the-art RL-based schedulers in Contiki-NG and included it in our comparison of TSCH schedulers. The experiment results showed that the RL-based scheduler implemented in this work demonstrated better performance in PDR and latency compared to other scheduling protocols. However, it presented high energy usage. On the other hand, Orchestra performed well while keeping the energy expenditure of nodes at a low level.
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