Approximate event detection over multi-modal sensing data

Jing Gao (), Jianzhong Li () and Yingshu Li ()
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Jing Gao: Harbin Institute of Technology
Jianzhong Li: Harbin Institute of Technology
Yingshu Li: Georgia State University

Journal of Combinatorial Optimization, 2016, vol. 32, issue 4, No 3, 1002-1016

Abstract: Abstract Composite event detection is one of the fundamental tasks for heterogeneous wireless sensor networks (WSNs). Multi-modal data generated by heterogeneous sensors bring new challenges for composite event monitoring in heterogeneous WSNs. By exploiting the correlations between different types of data, the approximate composite event detection problem is investigated in this paper. The optimal transmitting scheme problem is proposed to calculate the optimal transmitting scheme with minimum cost on the constraint that the confidence of the composite event must exceed the threshold. The optimal transmitting scheme problem is proved to belong to NP-complete. A dynamic programming based algorithm is presented for simple linear confidence combination operators, which runs in pseudo-polynomial time. The greedy based approximate algorithm is also designed for general confidence combination operators and the approximate ratio is proved to be 2 for “+” as the confidence combination operator. The simulation results show that our algorithms can reduce energy consumption significantly.

Keywords: Multi-modal data; Event detection; Composite event (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10878-015-9847-0

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