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Efficient Data Collection in IoT Networks using Trajectory Encoded with Geometric Shapes
IEEE Transactions on Sustainable Computing
  • Xiaofei Cao
  • Sanjay K. Madria, Missouri University of Science and Technology
Abstract

The Mobile Edge Computing (MEC) mitigates the bandwidth limitation between the edge server and the remote cloud by directly processing the large amount of data locally generated by the network of the internet of things (IoT) at the edge. To reduce redundant data transmission, this paper proposes a data collection scheme that only gathers the necessary data from IoT devices along a trajectory. Instead of using and transmitting location information (to preserve location anonymity), a virtual coordinate system called "distance vector of hops to anchors" (DV-Hop) is used. The proposed trajectory encoding algorithm uses ellipse and hyperbola constraints to encode the position of interest (POI) and the trajectory route to the POI. Sensors make routing decisions only based on the geometric constraints and the DV-Hop information, both of which are stored in their memory. The proposed DV-Hop updating algorithm enables the users to collect data in an IoT network with mobile nodes. The experiments show that in heterogeneous IoT networks, the proposed data collection scheme outperforms two other state-of-the-art topology-based routing protocols, called ring routing, and nested ring. The results also show that the proposed scheme has better latency, reliability, coverage, energy usage, and provide location privacy compared to state-of-the-art-schemes.

Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Publication Status
Early Access
Comments

Published online: 14 Dec 2020

Keywords and Phrases
  • Broadcasting,
  • Data collection,
  • DV-Hop,
  • Edge computing,
  • ellipse,
  • Encoding,
  • GPS-free,
  • hyperbola,
  • IoT,
  • Routing,
  • Routing,
  • Routing protocols,
  • Sensor,
  • Sensors,
  • Trajectory,
  • Trajectory,
  • Wireless sensor networks
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
12-14-2020
Publication Date
14 Dec 2020
Disciplines
Citation Information
Xiaofei Cao and Sanjay K. Madria. "Efficient Data Collection in IoT Networks using Trajectory Encoded with Geometric Shapes" IEEE Transactions on Sustainable Computing (2020) ISSN: 1939-1374
Available at: http://works.bepress.com/sanjay-madria/123/