Literature Database Entry

milojevic2017early

M. Milojevic and J. A. Barria, "Early warnings dissemination for urban micro-scale monitoring using vehicular sensor network," Proceedings of 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2017), Naples, Italy, June 2017, pp. 244–249.

Abstract

This paper presents the beaconless multi-hop Decentralised Dissemination of Warnings (DDW) mechanism for micro-scale monitoring applications in urban environments. It is designed for a Vehicular Sensor Network (VSN) based on mobile nodes with limited resources. Mobile nodes sense and generate high pollution levels early warnings in near real-time. The warning messages are disseminated toward Static Monitoring Units used by the local authorities to monitor onsets of harmful pollution level episodes. The DDW is a distance-based mechanism where a receiving node calculates the waiting time before rebroadcasting the message based on the distance from the sending node and the distance to the Static Monitoring Units. The DDW mechanism performance is evaluated in an urban environment with different number of mobile nodes in the network. Results show that the proposed mechanism collects more non-duplicated warnings at the Static Monitoring Units than other evaluated dissemination protocols. It is also shown the DDW mechanism reduces the amount of duplicated warning messages sent in the network, which is especially important when mobile nodes are resource constrained.

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M. Milojevic
J. A. Barria

BibTeX reference

@inproceedings{milojevic2017early,
    author = {Milojevic, M. and Barria, J. A.},
    doi = {10.1109/MTITS.2017.8005674},
    title = {{Early warnings dissemination for urban micro-scale monitoring using vehicular sensor network}},
    pages = {244--249},
    publisher = {IEEE},
    address = {Naples, Italy},
    booktitle = {5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2017)},
    month = {6},
    year = {2017},
   }
   
   

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