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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Online Power Optimization in Feedback-Limited, Dynamic and Unpredictable IoT Networks

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Author(s):
Marcastel, Alexandre [1] ; Belmega, Elena Veronica [1] ; Mertikopoulos, Panayotis [2] ; Fijalkow, Inbar [1]
Total Authors: 4
Affiliation:
[1] Univ Cergy Pontoise, Univ Paris Seine, CNRS, ENSEA, ETIS, F-95302 Cergy Pontoise - France
[2] Univ Grenoble Alpes, CNRS, LIG, INRIA, F-38334 Grenoble - France
Total Affiliations: 2
Document type: Journal article
Source: IEEE TRANSACTIONS ON SIGNAL PROCESSING; v. 67, n. 11, p. 2987-3000, JUN 1 2019.
Web of Science Citations: 0
Abstract

One of the key challenges in Internet of Things (IoT) networks is to connect many different types of autonomous devices while reducing their individual power consumption. This problem is exacerbated by two main factors: first, the fact that these devices operate in and give rise to a highly dynamic and unpredictable environment where existing solutions (e.g., water-filling algorithms) are no longer relevant; and second, the lack of sufficient information at the device end. To address these issues, we propose a regret-based formulation that accounts for arbitrary network dynamics: this allows us to derive an online power control scheme that is provably capable of adapting to such changes, while relying solely on strictly causal feedback. In so doing, we identify an important tradeoff between the amount of feedback available at the transmitter side and the resulting system performance: if the device has access to unbiased gradient observations, the algorithm's regret after T stages is O(T-1/2) (up to logarithmic factors); on the other hand, if the device only has access to scalar, utility-based information, this decay rate drops to O(T-1/4). The above is validated by an extensive suite of numerical simulations in realistic channel conditions, which clearly exhibit the gains of the proposed online approach over traditional water-filling methods. (AU)

FAPESP's process: 18/12579-7 - ELIOT: enabling technologies for IoT
Grantee:Vitor Heloiz Nascimento
Support Opportunities: Research Projects - Thematic Grants