Efficient Geospatial Data Collection in IoT Networks for Mobile Edge Computing

The emerging Mobile Edge Computing(MEC) paradigm changes the role of the edge devices from data producer and service requester to be the data consumers and data processors. The MEC mitigate the bandwidth constraints between the edge to the cloud by directly processing the tremendous data created by the sheer volume of IoT devices in the edge locally. An efficient data collection scheme is crucial for the quality service(QoS) for the MEC applications. In this project, we design an efficient data collection scheme that only gathers the necessary data from IoT devices like wireless sensors for the local services based on the geospatial constraints. The scheme design will includes a lossy compression algorithm that could compress the routing message to reduce the response time. The sensing data of the IoT devices also be compressed and aggregated by using the Z-compression.

Researcher

Xiaofei Cao