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.

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.

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.

This work aims to monitor air quality in places where humans spend most of their time, such as workplaces and homes. Radon gas is a naturally occurring, colourless, odourless and tasteless gas that accumulates in enclosed spaces. It is a radioactive element produced by the decay of its natural parent elements, uranium and thorium, which is harmful to our respiratory system when inhaled. The Internet of Things (IoT) is the key to the problems of contemporary life; we are witnessing an emerging connected world, and these architectures have the potential by using sensors to take data from the physical world, transfer it over the network and store it for further decision making or action. The proposal of this work is based on a radon sensor connected to an IoT device, the Message Queuing Telemetry Transport protocol (MQTT), the Node-RED for managing data flows and a database management system on a web server. The information collected by the sensor is sent by the IoT device to be processed by Node-RED. The obtained data is stored in a database to be represented on a web server. Therefore, this work includes a case study where the technologies involved in the indoor radon gas monitoring system are presented. It is a way to perform radon gas measurements automatically. The final application would allow: displaying radon concentrations on a map with placemarks and updating the information in real-time. The database could record data from other radon sensors that any user wants to associate with this website.

Ambient Assisted Living (AAL) has become an attractive research topic due to growing interest in remote monitoring of older people. Development in sensor technologies and advances in wireless communications allows to remotely offer smart assistance and monitor those people at their own home, increasing their quality of life. In this context, Wireless Acoustic Sensor Networks (WASN) provide a suitable way for implementing AAL systems which can be used to infer hazardous situations via environmental sounds identification. Nevertheless, satisfying sensor solutions have not been found with the considerations of both low cost and high performance. In this paper, we report the design and implementation of a wireless acoustic sensor to be located at the edge of a WASN for recording and processing environmental sounds which can be applied to AAL systems for personal healthcare because it has the following significant advantages: low cost, small size, audio sampling and computation capabilities for audio processing. The proposed wireless acoustic sensor is able to record audio samples at least to 10 kHz sampling frequency and 12-bit resolution. Also, it is capable of doing audio signal processing without compromising the sample rate and the energy consumption by using a new microcontroller released at the last quarter of 2016. The proposed low cost wireless acoustic sensor has been verified using four randomness tests for doing statistical analysis and a classification system of the recorded sounds based on audio fingerprints.