Itziar G. Alonso-González

A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization

Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.

Adaptive tuning mechanism for EDCA in IEEE 802.11e wireless LANs

The emerging IEEE 802.11 family of wireless technologies has shown tremendous growth and acceptance as a last hop wireless solution in Local Area Networks (LANs). The use of multimedia networking applications has brought more requirements to the network, creating a need for end-to-end quality of service (QoS). In this context, the standard introduces one mechanism, called EDCA which provides a prioritized traffic to guarantee the QoS involving four different Access Category (ACs). The main problem of the original EDCA is that the values of the main parameters of each AC queue (such as contention window limits) are static and do not take into account wireless channel conditions. In this paper, we present a new approach to split the contention windows per AC into different sub-windows, ensuring that this partition decreases channel collisions while maintaining a low delay and high throughput. Our technique select on the fly, the sub-windows per AC taking into account both applications requirements and network conditions. Preliminary results on ad-hoc and infrastructure wireless networks show that the presented technique enhances the channel utilization, decreasing the delay and increasing the throughput.

Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results.

A low consumption real time environmental monitoring system for smart cities based on ZigBee wireless sensor network

Nowadays, there is an increasing interest in wireless sensor networks (WSN) for environmental monitoring systems because it can be used to improve the quality of life and living conditions are becoming a major concern to people. This paper describes the design and development of a real time monitoring system based on ZigBee WSN characterized by a lower energy consumption, low cost, reduced dimensions and fast adaptation to the network tree topology. The developed system encompasses an optimized sensing process about environmental parameters, low rate transmission from sensor nodes to the gateway, packet parsing and data storing in a remote database and real time visualization through a web server. A monitoring system integrating the outlined system has been deployed and tested for monitoring the level of dust particles in the air, acoustic levels in different places of a city, ambient temperature and relative humidity. A calibration process of a low cost audio sensor was performed to measure the acoustic level from different noise sources, hence, it is not necessary to use an expensive sound level meter at each node. Furthermore, experimental results show autonomy nodes can be about three months.

Analysis of the effects of the hidden node problem in IEEE 802.15.7 uplink performance

IEEE 802.15.7 is a physical and MAC layer standard for visible light communication (VLC). A slotted CSMA/CA MAC procedure is defined in the standard to coordinate optical channel access for multiple wireless devices. This CSMA/CA process does not provide any hidden node avoidance mechanisms, thus potentially leading to severe performance degradation in the presence of hidden nodes due to collisions. In this paper, we analyze the effect of hidden nodes on a VLC network in the star topology. We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and packet loss rate. The impact of the hidden node problem in network performance is evaluated by comparing simulation results.

Implementing an IEEE802.15.7 Physical Layer Simulation Model with OMNET

Visible Light Communications (VLC) uses visible light spectrum as transmission medium for communications. VLC has gained recent interest as a favorable complement to radio frequency (RF) wireless communications systems due to the ubiquity and wide variety of applications. In 2011 the Institute of Electrical and Electronic Engineers published the standard IEEE 802.15.7 [1]. Nowadays, simulation tools are widely used to study, understand and achieve better network performance. This paper describes the design and implementation of a physical layer model based in IEEE802.15.7 standard using OMNET++ simulation tool [2]. This software is a popular tool for building networks’ and modeling their behavior. The main goal of this paper is to introduce the developing and implementing of a software module to simulate the Physical Layer (PHY) based on IEEE802.15.7. The developed module, called simVLC will let researchers and students to study and simulate different scenarios in this standard.

Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication

Indoor localization estimation has become an attractive research topic due to growing interest in location-aware services. Many research works have proposed solving this problem by using wireless communication systems based on radiofrequency. Nevertheless, those approaches usually deliver an accuracy of up to two metres, since they are hindered by multipath propagation. On the other hand, in the last few years, the increasing use of light-emitting diodes in illumination systems has provided the emergence of Visible Light Communication technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. This brings a brand new approach to high accuracy indoor positioning because this kind of network is not affected by electromagnetic interferences and the received optical power is more stable than radio signals. Our research focus on to propose a fingerprinting indoor positioning estimation system based on neural networks to predict the device position in a 3D environment. Neural networks are an effective classification and predictive method. The localization system is built using a dataset of received signal strength coming from a grid of different points. From the these values, the position in Cartesian coordinates (x,y,z) is estimated. The use of three neural networks is proposed in this work, where each network is responsible for estimating the position by each axis. Experimental results indicate that the proposed system leads to substantial improvements to accuracy over the widely-used traditional fingerprinting methods, yielding an accuracy above 99% and an average error distance of 0.4 mm.