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

Thesis: Cognitive strategies for reducing energy consumption in Wireless Sensor Networks
Posted on by elena

 

Author: Elena Romero Perales

Advisor: Alvaro Araujo Pinto

Synopsis: Global data traffic in telecommunication annually grows with a rate higher than 50%. While the growth in traffic is stunning, the rapid adoption of wireless technology over the complete globe and the penetration through all layers of society is even more amazing. Over the span of 20 years, wireless subscription has risen to 40% of the world population, and is expected to grow to 70% by 2015. Overall mobile data traffic is expected to grow to 6.3 exabytes per month by 2015, a 26-fold increase over 2010. This expansion leads to an increase of the energy consumption by approximately 10% per year. A major portion of this expanding traffic has been migrating to mobile networks and systems. Due to this growth in wireless data traffic, the associated consumption to it becomes very important. Up to now, wireless network power consumption has not been an important issue because it was insignificant in comparison with wired network consumption. Nevertheless, over the recent years, wireless and mobile communications are increasingly becoming popular with consumer. Take into account the wireless traffic prediction the current rate of power consumption per unit of data cannot be sustained.

One of the most important trends related with the expansion of wireless networks is the significant increase of ubiquitous computing. WSNs give technological solution to this challenge, so its growth is closely linked to these data. Typical ubiquitous applications include security and surveillance (sensor nodes and video streams transmitted by Wi-Fi), health care (medical information transmitted by sensor nodes) or vehicular networks. Due to the number of nodes, its wireless nature, and its deployment in difficult access areas, WSN nodes should not require any maintenance. In terms of consumption this means that the sensors must be energetically autonomous and therefore the batteries cannot be changed or recharged.

The increasing demand for wireless communication presents an efficient spectrum utilization challenge. To address this challenge, Cognitive Radio (CR) has emerged as the key technology, which enables opportunistic access to the spectrum. In this way, the cooperation between devices introducing by CR regarding information sharing and taking decisions allows better spectrum use, lower energy consumption and better data reliability. The introduction of Cognitive Radio capabilities in WSN provides a new paradigm for power consumption reduction offering new opportunities to improve it, but also implies some challenges to face. Talking in detail about power consumption, sensing state, collaboration among devices (that requires communication) and changes in transmission parameters are not free in terms of consumption. In this way, all steps must be taken into account for a holistic optimization. Reducing power consumption requires optimization across all the layers of the communication systems.

 The final goal is to reduce energy consumption in WSN exploiting the new capabilities introduced by the cognitive radio concept.

PFC: Design of a Cognitive New Generation Device
Posted on by elena

The objective of this project is the development of a new Cognitive Wireless Sensor Device. This device must provide functionality to develop and evaluate Cognitive Radio techniques adapted to Wireless Sensor Networks. These techniques could include: spectrum sensing, collaboration and learning and cognitive network optimization.

Related Technologies

  • Cognitive Radio
  • Wireless Sensor Networks
  • Hardware design
  • C programming

Task

  • State of the art study in cognitive networks
  • Requirements definition
  • Architecture design
  • Hardware design
  • Software implementation
  • Tests and results

Requirements

  • Dedication: 4 hours/day.

Tutor

Elena Romero <elena@die.upm.es>

State

In progress

Thesis: Smart Energy Harvesting strategies for Wireless Sensor Networks
Posted on by b105

Post-web2

 

Author: Elena Real López

Advisor: Alvaro Araujo Pinto

In recent years we have attended to the development of Wireless Sensor Networks (WSN) and their inclusion in many areas of our daily life. Since nodes are wireless to ensure the ubiquity of the network and in many cases they are also mobile, it is essential to power them with batteries. Moreover, these batteries must be small to fit the size of the device. What’s more, it is customary that the location of the node is inaccessible, so changing batteries is considerably complicated.

This, coupled with the requirements specified above, makes it almost essential to use energy harvesting techniques for ensuring the device power.

The goal of this thesis is the study and the development of various smart energy harvesting techniques to improve the energy supply of wireless devices. In addition, this smart energy harvesting should adapt to the specific needs of the network and to the environment in which it is placed, with the aim to achieve an optimal behavior and a higher benefit.

Thesis: Strategies to maximize the lifetime of wireless sensor networks with a cross-layer approach
Posted on by albarc

lifetimeLogo

Author: Alba Rozas Cid

Advisor: Álvaro Araujo Pinto

Synopsis: Wireless Sensor Networks (WSNs) are one of the fastest growing sectors in the field of wireless and mobile communications. A WSN consists of spatially distributed autonomous sensor nodes deployed to monitor physical or environmental conditions, over a certain area. Each sensor node has a radio system and a small processor, and it is powered from a limited energy source, generally a small battery. Given this limited energy source, reducing consumption is a crucial matter in WSNs. An important term in this field of research is the “network lifetime”. It is roughly defined as the period in which the network is operational. As can be seen, the notion of operation is not objective, and it strongly depends on the application for which the network is intended. However, most definitions of the term fail to take the application into account.

Not only is it interesting to increase the network lifetime, but there are also certain applications in which it would be very desirable for the user to have control in the process of degradation that comes at the end of it. This leads us to the introduction of a new paradigm: the controlled degradation of the network.

In this thesis we want to link the definition of the network lifetime to the specific application of each WSN. We believe this, along with having control in the eventual degradation, can lead to better algorithms and optimizations focusing both on energy consumption and quality of service. Also, given this group’s experience with Cognitive Wireless Sensor Networks (CWSNs), we would like to use the cognitive paradigm as much as possible. We believe that it can lead to promising results and it would enable a cross-layer approach, mainly through spectrum sensing and cooperation between nodes.