A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks
The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their l...
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doaj-f290115a713447f0bd2936dd77e96fe92020-11-24T20:59:16ZengHindawi LimitedJournal of Sensors1687-725X1687-72682016-01-01201610.1155/2016/96045269604526A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor NetworksMohammed Hawa0Khalid A. Darabkh1Raed Al-Zubi2Ghazi Al-Sukkar3Electrical Engineering Department, The University of Jordan, Amman 11942, JordanComputer Engineering Department, The University of Jordan, Amman 11942, JordanElectrical Engineering Department, The University of Jordan, Amman 11942, JordanElectrical Engineering Department, The University of Jordan, Amman 11942, JordanThe fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, called S-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer.http://dx.doi.org/10.1155/2016/9604526 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohammed Hawa Khalid A. Darabkh Raed Al-Zubi Ghazi Al-Sukkar |
spellingShingle |
Mohammed Hawa Khalid A. Darabkh Raed Al-Zubi Ghazi Al-Sukkar A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks Journal of Sensors |
author_facet |
Mohammed Hawa Khalid A. Darabkh Raed Al-Zubi Ghazi Al-Sukkar |
author_sort |
Mohammed Hawa |
title |
A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks |
title_short |
A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks |
title_full |
A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks |
title_fullStr |
A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks |
title_full_unstemmed |
A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks |
title_sort |
self-learning mac protocol for energy harvesting and spectrum access in cognitive radio sensor networks |
publisher |
Hindawi Limited |
series |
Journal of Sensors |
issn |
1687-725X 1687-7268 |
publishDate |
2016-01-01 |
description |
The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, called S-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer. |
url |
http://dx.doi.org/10.1155/2016/9604526 |
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