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Hector Kaschel, Karel Toledo de la Garza, Jorge Torres Gómez and Maria Julia Fernández-Getino García, "Energy-Efficient Cooperative Spectrum Sensing based on Stochastic Programming in Dynamic Cognitive Radio Sensor Networks," IEEE Access, vol. 9, pp. 720–732, January 2021.


Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability- the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.

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Hector Kaschel
Karel Toledo de la Garza
Jorge Torres Gómez
Maria Julia Fernández-Getino García

BibTeX reference

    author = {Kaschel, Hector and Toledo de la Garza, Karel and Torres G{\'{o}}mez, Jorge and Fern{\'{a}}ndez-Getino Garc{\'{i}}a, Maria Julia},
    doi = {10.1109/ACCESS.2020.3046466},
    title = {{Energy-Efficient Cooperative Spectrum Sensing based on Stochastic Programming in Dynamic Cognitive Radio Sensor Networks}},
    pages = {720--732},
    journal = {IEEE Access},
    issn = {2169-3536},
    publisher = {IEEE},
    month = {1},
    volume = {9},
    year = {2021},

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