Data aggregation scheduling in wireless mobile multichannel sensor networks

Cheng Feng, Zhi Jun Li, Shou Xu Jiang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Real-time navigation, traffic monitoring, warning broadcasts applications in Intelligent Transportation Systems need a large number of real-time traffic volume, vehicle locations travel time through the roads and so on. Many kinds of sensors on the vehicles (e.g. GPS) can sense these data. Vehicular networks composed by vehicles and access points can transmit these data. A mass of real-time data need be transmitted. But the capacity of wireless communication cannot satisfy the transmission requirements. Moreover, the frequent change of wireless links caused by the mobility of vehicles makes wireless resource difficult to be utilized. This paper synthesizes three aspects to solve this problem: utilize multi-channel transmission of 802.11p protocol to improve the capacity of wireless communication; utilize data aggregation to decrease the amount of data transmission; utilize dynamic communication topology to schedule effective transmissions. This paper proposes data aggregation transmission scheduling, which includes two steps. The first step is to create an optimal aggregation routing tree with channel constraints. The basic idea is to choose the links with more connected timeslots as edges on the tree to optimize the space of transmission time scheduling. At the same time, the algorithm needs to satisfy that the amount of inter-tree conflicts in the interference range is less than the amount of channels in order to realize multi-channel transmission without conflicts. The second step is to schedule the optimal transmission time. The key point is to utilize limited wireless resource to aggregate data to AP as much as possible without transmission conflicts. In this part the paper proposes a dynamic programming algorithm to get optimal transmission scheduling. Finally, we conduct a mass of simulation experiments on a real taxi trajectory data set. The result demonstrates that compared with existing data aggregation algorithms, our algorithm improves nearly 1/4 in the data collection rate, and decreases the average delay.

Original languageEnglish (US)
Pages (from-to)931-945
Number of pages15
JournalJisuanji Xuebao/Chinese Journal of Computers
Volume39
Issue number5
DOIs
StatePublished - May 1 2016

Bibliographical note

Funding Information:
This research is supported by the National Natural Science Foundation of China (No.61370214).

Publisher Copyright:
© 2016, Science Press. All right reserved.

Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

Keywords

  • Data aggregation
  • Data collection
  • Dynamic programming
  • Internet of Things
  • Multi-channel
  • VANET

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