Title

Efficient Geospatial Data Collection in IoT Networks for Mobile Edge Computing

Abstract

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.

Meeting Name

18th IEEE International Symposium on Network Computing and Applications, NCA 2019 (2019: Sep. 26-28, Cambridge, UK)

Department(s)

Computer Science

Comments

This research is partly supported by a NSF grant CNS 1461914 and DOE grant P200A180051.

Keywords and Phrases

Data acquisition; Data handling; Edge computing; Quality of service; Sensor nodes, Bandwidth constraint; Data collection scheme; Geo-spatial data; Lossy compressions; Minimal distance; Service requesters; State-of-the-art scheme; Wireless sensor, Internet of things

International Standard Book Number (ISBN)

978-172812522-0

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Sep 2019

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