Enabling Energy-Efficient Data Communication with Participatory Sensing and Mobile Cloud: Cloud-assisted crowd-sourced data-driven optimization
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This thesis proposes a novel power management solution for the resource-constrained devices in the context of Internet of Things (IoT). We focus on smartphones in the IoT, as they are getting increasingly popular and equipped with strong sensing capabilities. Smartphones have complex and chaotic asynchronous power consumption incurred by heterogeneous components including their onboard sensors. Their interaction with the cloud can support computation offloading and remote data access via the network. In this work, we aim at monitoring the power consumption behaviours of smartphones and profiling individual applications and platform to make better decisions in power management. A solution is to design architecture of cloud orchestration as an epic predictor of the behaviours of smart devices with respect to time, location, and context. We design and implement this architecture to provide an integrated cloud-based energy monitoring service. This service enables the monitoring of power consumption on smartphones and support data analysis on massive data logs collected by a large number of users.
Place, publisher, year, edition, pages
2016. , 47 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-274875OAI: oai:DiVA.org:uu-274875DiVA: diva2:897798
Master Programme in Computer Science
Dey, SubhrakantiPearson, Justin