Energy minimization in multi-task software-defined sensor networks

Zeng, Deze, Li, Peng, Guo, Song, Miyazaki, Toshiaki, Hu, Jiankun and Xiang, Yong 2015, Energy minimization in multi-task software-defined sensor networks, IEEE Transactions on computers, vol. 64, no. 11, pp. 3128-3139.

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Title Energy minimization in multi-task software-defined sensor networks
Author(s) Zeng, Deze
Li, Peng
Guo, Song
Miyazaki, Toshiaki
Hu, Jiankun
Xiang, YongORCID iD for Xiang, Yong
Journal name IEEE Transactions on computers
Volume number 64
Issue number 11
Start page 3128
End page 3139
Total pages 12
Publisher IEEE
Place of publication New York, N.Y
Publication date 2015-11-01
ISSN 0018-9340
Keyword(s) Science & Technology
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
Software-defined sensor network
sensor activation
task mapping
sensing rate scheduling
energy efficiency
Summary Abstract—
After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.
Language eng
Field of Research 080503 Networking and Communications
0803 Computer Software
0805 Distributed Computing
1006 Computer Hardware
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
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