M. Moltafet, M. Leinonen, M. Codreanu and N. Pappas, "Power Minimization in Wireless Sensor Networks With Constrained AoI Using Stochastic Optimization," 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2019, pp. 406-410.
Power minimization in wireless sensor networks with constrained AoI using stochastic optimization
|Author:||Moltafet, Mohammad1; Leinonen, Markus1; Codreanu, Marian2;|
1Centre for Wireless Communications – Radio Technologies University of Oulu, Finland
2Department of Science and Technology Linköping University, Sweden
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003319801
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-03-31
In this paper, we consider a system where multiple low-power sensors communicate timely information about a random process to a sink. The sensors share orthogonal subchannels to transmit such information in the form of status update packets. Freshness of the sensors’ information at the sink is characterized by the Age of Information (AoI), and the sensors can control the sampling policy by deciding whether to take a sample or not. We formulate an optimization problem to minimize the time average total transmit power of sensors by jointly optimizing the sampling action of each sensor, the transmit power allocation, and the subchannel assignment under the constraints on the maximum time average AoI and maximum power of each sensor. To solve the optimization problem, we use the Lyapunov drift-plus-penalty method. Numerical results show the performance of the proposed algorithm versus the different parameters of the system.
Asilomar Conference on on Signals, Systems & Computers
|Pages:||406 - 410|
53rd Annual Asilomar Conference on Signals, Systems, and Computers 2019. Pasific Grove, USA, Nov 3-6, 2019
|Host publication editor:||
Matthews, Michael B.
Asilomar Conference on Signals, Systems, and Computers
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
112 Statistics and probability
213 Electronic, automation and communications engineering, electronics
This research has been financially supported by the Infotech Oulu, the Academy of Finland (grant 323698), and Academy of Finland 6Genesis Flagship (grant 318927). M. Codreanu would like to acknowledge the support of the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 793402 (COMPRESS NETS). M. Moltafet would like to acknowledge the support of Finnish Foundation for Technology Promotion.
|Academy of Finland Grant Number:||
323698 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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