Abstract
Localization and prediction of movement of miners in underground mines have been a constant problem more so during a mine disaster. Due to the unavailability of GPS signals, the pillars are used as a method to locate these miners, and thus, location prediction is also carried out with reference to these pillars. In this work, we demon- strate a Delay-tolerant Network (DTN) system called Miner-Finder that leverages Machine Learning (ML) framework (GAE-LSTM) that works on edge devices (e.g., mobile phones, tablets) to predict the location of miners in an underground mine. The information such as speed, angle, time, nearest pillar is first sensed by the mo- bile devices which is then sent to the GAE-LSTM framework. This framework then uses the predicted location of the miners at differ- ent times to route important messages from DTN nodes itself. For this, the system generates a routing table based on the predicted locations with their respective times and forms a contact graph for routing. The DTN system is decentralized and does not need any central/base server and the location prediction is performed locally at individual devices in a federated fashion.
Recommended Citation
A. Goyal et al., "Demo-Abstract: A DTN System For Tracking Miners Using GAE-LSTM And Contact Graph Routing In An Underground Mine," MobiWac 2023 - Proceedings of the Internatinal ACM Symposium on Mobility Management and Wireless Access, pp. 129 - 132, Association for Computing Machinery, Oct 2023.
The definitive version is available at https://doi.org/10.1145/3616390.3618290
Department(s)
Computer Science
Second Department
Mining Engineering
Keywords and Phrases
contact graph routing; miner location prediction
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Association for Computing Machinery, All rights reserved.
Publication Date
30 Oct 2023
