Spectral Sensing Method in the Radio Cognitive Context for IoT Applications

This paper describes a spectral sensing method in the radio cognitive context for IoT applications. Due to the increasing number of Internet connected devices there are big challenges in terms of scalability, adaptability, connectivity, accessibility and reliability. The necessity to generate efficient methods to access into the wireless medium is currently a big concern. Several works have addressed this issue but few works details how spectrum sensing could really help to allocate dynamically the unlicensed frequency bands for IoT applications reducing the congestion and enabling the IoT technologies. The presented work shows the results obtained of the noise floor characterization in the frequency band for an IoT service such as the case of SigFox and gives insights about how SDR systems can be applied in the cognitive context for wireless networks in IoT.

[1]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[2]  Paul Thomas,et al.  Low Throughput Networks for the IoT: Lessons learned from industrial implementations , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[3]  Ángel G. Andrade,et al.  Adaptive energy detector for spectrum sensing in cognitive radio networks , 2016, Comput. Electr. Eng..

[4]  Joseph Mitola,et al.  Software radio architecture: a mathematical perspective , 1999, IEEE J. Sel. Areas Commun..

[5]  Lucas J. van Vliet,et al.  The digital signal processing handbook , 1998 .

[6]  W. Gardner.  Exploitation of spectral redundancy in cyclostationary signals , 1991, IEEE Signal Processing Magazine.

[7]  Ian F. Akyildiz,et al.  Cooperative spectrum sensing in cognitive radio networks: A survey , 2011, Phys. Commun..

[8]  Dominik Knoll,et al.  An implementation of Software Defined Radios for federated aerospace networks: Informing satellite implementations using an inter-balloon communications experiment , 2016 .

[9]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[10]  Anantha Chandrakasan,et al.  Energy efficient protocols for low duty cycle wireless microsensor networks , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[11]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[12]  Wael Guibène,et al.  An evaluation of low power wide area network technologies for the Internet of Things , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[13]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[14]  Ahmed Nait-Sidi-Moh,et al.  Using Internet of Things Technologies for a Collaborative Supply Chain: Application to Tracking of Pallets and Containers , 2015, FNC/MobiSPC.

[15]  Eleonora Borgia,et al.  The Internet of Things vision: Key features, applications and open issues , 2014, Comput. Commun..

[16]  Zhen Hu,et al.  Software-Defined-Radio-Based Wireless Tomography: Experimental Demonstration and Verification , 2015, IEEE Geoscience and Remote Sensing Letters.

[17]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[18]  Anum Ali,et al.  Energy efficient techniques for M2M communication: A survey , 2016, J. Netw. Comput. Appl..