Sistemas Operativos para Redes de Sensores Inalámbricas

En los últimos años ha habido un gran crecimiento en el desarrollo y despliegue de Redes de Sensores Inalámbricas (WSN). Para ello se han utilizado multitud de plataformas hardware para cada aplicación específica, lo cual imposibilita la compatibilidad software entre aplicaciones.

Por ello hemos decidido comenzar una línea de investigación en sistemas operativos (OS) para redes de sensores inalámbricas, donde los recursos son muy limitados. Asimismo se pretende mejorar la eficiencia de las aplicaciones en redes de sensores con las herramientas que proporciona un sistema operativo.

Actualmente existen varios sistemas operativos orientados a redes de sensores inalámbricas y el objetivo es utilizarlos como base de cara a mejorar su funcionalidad para desarrollos futuros. Hay sin embargo multitud de retos para investigar en este campo, como pueden ser: gestión orientada a bajo consumo, interfaces de usuario para desarrolladores, compartición de recursos entre nodos, implementación de múltiples protocolos radio, algoritmos de tiempo real, personalización del sistema por el desarrollador, optimización automática en tiempo de ejecución…

Por ello, estamos trabajando con el sistema operativo Contiki OS para investigar estos retos que se plantean y desarrollar nuevas funcionalidades que se puedan aplicar a futuros despliegues de redes de sennsores.

 

WSN hardware platforms
WSN hardware platforms

An ultra-low wake-on radio receiver for Wireless Sensor Networks

An ultra-low power wake-on receiver has been designed, simulated, implemented and tested. This receiver has been developed for Wireless Sensor Network exploting the sleep mode of the nodes to reduce the average power consumption.

There are some wake-on devices in the literature, but they most lack on flexibility and power consumption. Therefore this receiver has been designed for multiple scenarios to make it easily integrable in any Wireless Sensor Network. A prototype was implemented in PCB using standard components and testing the prototype provides very good operating results, reducing the power consumption of a node up to 1000 times.

Wake-on receiver

 

Implementation, analysis and evaluation of a localization algorithm for WSNs

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

 

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

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