Efficient Diversified Set Monitoring for Mobile Sensor Stream Environments
Due to recent developments in sensor technologies, mobile sensor device use has become widespread, and many researchers have been attempting to leverage data collected by these devices. We call such data 'mobile sensor data'; and environments where mobile sensor data arrive continuously, 'mobile sensor stream' environments. Mobile sensor data are geo-referenced data with environmental attribute values; and they enable us to determine the geographical distribution of hot spots by retrieving data with comparatively extreme environmental attribute values (such as higher air-pollution index values). Top-k search result diversification in geographical space is valid for applications of this sort. By monitoring a diversified set over mobile sensor streams, we can trace changes in the distribution of hot spots. However, the computation costs for maintaining such diversified sets are high when we have to monitor a large amount of mobile sensor data. Thus, in this paper, we propose an efficient diversified set monitoring method for mobile sensor stream environments. Our proposed method can reduce the amount of examined data by exploiting our proposed regular grid-based data structure, and the diversified set can thereby be maintained much more efficiently. Our experimental results confirm that the proposed method involves much shorter computation time in comparison with the baseline method.
M. Yokoyama et al., "Efficient Diversified Set Monitoring for Mobile Sensor Stream Environments," Proceedings of the 2017 IEEE International Conference on Big Data (2017, Boston, MA), pp. 500-507, Institute of Electrical and Electronics Engineers (IEEE), Dec 2017.
The definitive version is available at https://doi.org/10.1109/BigData.2017.8257964
2017 IEEE International Conference on Big Data, Big Data 2017 (2017: Dec. 11-14, Boston, MA)
Intelligent Systems Center
Keywords and Phrases
Geographical distribution; Continuous result diversification; Environmental Monitoring; Hot spot detection; Mobile sensors; Participatory Sensing; Big data; Mobile sensor data
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2017