Thesis: Software-Defined Radio Techniques for Resource Optimization in Cognitive Wireless Sensor Networks
Posted on by Ramiro Utrilla

Author: Ramiro Utrilla Gutiérrez

Advisor: Alvaro Araujo Pinto

Synopsis: Due to the spectrum scarcity problem, mostly in license-free ISM bands, and the forecasts regarding the increasing adoption of wireless communications, especially in scenarios like cities, it is essential to optimize the use of the spectrum to ensure the proper functioning of services and devices in the near future.

As the characteristics of the spectrum, by their own physical nature and its use, are very dynamic and vary constantly, devices must be able to intelligently adapt to these changes, as the Cognitive Radio paradigm proposes. Moreover, this adaptation should be done quickly in order to be effective and it should minimize the impact on the use of the spectrum.

Because of that, this work is going to be mainly focused on the development and evaluation of cognitive strategies with zero or minimum communication overhead. In other words, the aim of the research is to evaluate the degree of optimization of resources that can be achieved in a Cognitive Wireless Sensor Network (CWSN) by doing the cognitive cycle (spectrum sensing, learning and adaptation) mostly at node-level. To better exploit the cognitive radio capabilities of these networks, and thanks to the current development of wireless and processing technology, Software-Defined Radio (SDR) techniques are going to be used in sensor nodes for that purpose. This approach supposes a new paradigm in CWSNs which implies new challenges to be faced.

At this point, it appears to be necessary to evaluate some issues about the future of wireless communications. Will someday the need for cognition to use the spectrum outweigh the current energy constraints? In other words, will it be possible to achieve efficient and reliable wireless communication without cognitive capabilities in the near future? Answering this question will reveal whether it still make sense to compare the power consumption of SDR solutions with other platforms based on COTS radio transceivers or, conversely, the addition of cognitive capabilities will cease to pose a challenge to maximize systems’ efficiency and become a key point for their proper operation.

 

Implementation, analysis and evaluation of a localization algorithm for WSNs
Posted on by Ramiro Utrilla

Wireless Sensor Networks (WSNs), formed by low-cost, small size, and low power consumption nodes, have a growing presence around us monitoring a wide range of parameters. In most cases the acquired data must be geo-tagged to provide meaningful information. However, Global Navigation Satellite Systems (GNSS) is not a feasible solution for these networks because of its impact on the nodes’ features previously seen. Besides, in many situations, the application features and the deployment method make it impossible to pre-program the nodes’ location. Hence, it raises the paradigm of localization in WSNs and the need of finding suitable methods for identifying the nodes’ position.

The goal of this work is to implement and evaluate an absolute localization algorithm for WSNs that can be used both in simulation and in real deployment scenarios. The implemented procedure establishes a series of proximity relationships, based on RSSI values, between the sensor nodes of the network and a group of anchor nodes that are aware of their own positions. Then, applying the fuzzy set theory to the extracted information, the algorithm is able to estimate the position of the sensor nodes without any previous characterization of the environment, even in the presence of radio irregularities. This algorithm is deeply analyzed through simulations to evaluate its performance and the effect of its variables on the results. Several self-configuration approaches based on the cognitive radio paradigm are proposed, optimizing its capabilities to the characteristics of the environment.

The execution of this Action Plan has led to the successful implementation of a localization algorithm for WSNs, which will serve as a tool for further research and related work on this field. It can be highlighted as the most significant result the reduction of the average localization error in a 100-meter long square area between 5% and 12.5%, because of the proposed self-configuration approaches. Thus, the nodes’ location are estimated with an accuracy of 4 meters under isotropic conditions (DOI=0), up to 7 meters for moderately irregular radio propagation conditions (DOI=0,1), and up to 10 meters when such irregularities are very significant (DOI=0,2).

SpatialDistLocalizationError_FRORFvsFRORF-PLENA-AP