Efficient Geospatial Data Collection in IoT Networks for Mobile Edge Computing
Xiaofei Cao, Sanjay Madria · 2019
The Mobile Edge Computing (MEC) paradigm changes the role of edge devices from data producers and service requesters to data consumers and processors. MEC mitigates the bandwidth constraint between the edge server and the cloud by directly processing the large data created by the sheer volume of IoT devices in the edge locally. An efficient data-gathering scheme is crucial for providing quality of service (QoS) within MEC. In this paper, we proposed an efficient data collection scheme that only gathers the necessary data from IoT devices like wireless sensors along a trajectory for local services based on geospatial constraints. We only use a vector of the minimal distance of hops (DV-Hop) to the anchor nodes selected by the fog server, instead of using GPS data. The proposed scheme includes a lossy compression algorithm that could compress each routing message, thus reducing the response time. In this paper, the experiments are conducted to evaluate the performance of our data collection using the encoded trajectory routing scheme compared with others using a TOSSIM simulator, and also using the powerTOSSIM-Z with real sensor motes. Our scheme performs better in terms of latency, reliability, coverage, and energy usage compared to other state-of-the-art schemes.