Abstract
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.
Highlights
Cognitive Radio (CR) constitutes a growing technology to overcome the increased spectrum occupancy for telecommunication services [1], [2]
It is comprised of small devices to support CR capabilities, namely (SUs); devices with legacy rights on spectrum usage called (PUs); and a fusion center (FC), who merges the received information from SUs to have a final decision about spectrum bands availability through (CSS)
We model a dynamic Cognitive Radio Sensor Networks (CRSN) taking into account the random movement behavior of nodes
Summary
Cognitive Radio (CR) constitutes a growing technology to overcome the increased spectrum occupancy for telecommunication services [1], [2]. Remaining nodes will be on sleep mode to reduce energy consumption and extend the network lifetime These solutions are based on computing the minimum number of awake nodes to run CSS and simultaneously satisfying a given detection performance. These solutions are repeatedly applied in time-slots to obtain an optimal solution in an attempt to discretize the time evolution regarding the network dynamics These concerns encourage the further extent of energy-consumption based strategies to properly consider the randomness of dynamic CRSNs. Since distances will not be fixed but inherently random in dynamic networks, sensor selection strategies for mobile nodes can be addressed through stochastic programming techniques by means of two approaches: ‘‘wait-and-see’’ [26] and ‘‘here-and-know’’ [27].
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