José A. Afonso

An Intra-vehicular Wireless Sensor Network Based on Android Mobile Devices and Bluetooth Low Energy

This chapter presents the development and test of an intra-vehicular wireless sensor network (IVWSN), based on Bluetooth Low Energy (BLE), designed to present to the driver, in real-time, information collected from multiple sensors distributed inside of the car, using a human-machine interface (HMI) implemented on an Android smartphone. The architecture of the implemented BLE network is composed by the smartphone, which has the role of central station, and two BLE modules (peripheral stations) based on the CC2540 system-on-chip (SoC), which collect relevant sensor information from the battery system and the traction system of a plug-in electric car. Results based on an experimental performance evaluation of the wireless network show that the network is able to satisfy the application requirements, as long as the network parameters are properly configured taking into account the peculiarities of the BLE data transfer modes and the observed limitations of the BLE platform used in the implementation of the IVWSN.

Wireless Body Area Network for Cycling Posture Monitoring

This work presents the design and implementation of a wireless body area network based on Bluetooth Low Energy (BLE) which enables the integration of multiple sensor nodes into a smartphone-based system in order to monitor the posture of cyclists. The wireless posture monitoring system presented in this chapter obtains the orientation in space of each body segment in which the sensor nodes are placed and calculates the trunk angle, the knee angle and the angle of inclination of the road. This system collects raw sensor data from accelerometers, magnetometers and gyroscopes and sends the data via BLE to an Android smartphone, which plays the role of central station and performs the data processing concerning the posture calculation. This chapter describes the development of the hardware and software of the sensor nodes, which are based on the CC2540 BLE system-on-chip, as well as the development of the Android application. Experimental results concerning the measurement of the posture of a cyclist are provided in order to validate the implementation.

Experimental Study on RSS Based Indoor Positioning Algorithms

This work compares the performance of indoor positioning systems suitable for low power wireless sensor networks. The research goal is to study positioning techniques that are compatible with real-time positioning in wireless sensor networks, having low-power and low complexity as requirements. Map matching, approximate positioning (weighted centroid) and exact positioning algorithms (least squares) were tested and compared in a small predefined indoor environment. We found that, for our test scenario, weighted centroid algorithms provide better results than map matching. Least squares proved to be completely unreliable when using distances obtained by the one-slope propagation model. Major improvements in the positioning error were found when body influence was removed from the test scenario. The results show that the positioning error can be improved if the body effect in received signal strength is accounted for in the algorithms.

An Experimental Study of ZigBee for Body Sensor Networks

We present an experimental performance evaluation of ZigBee networks in the context of data-intensive body sensor networks (BSNs). IEEE 802.15.4/ZigBee devices were mainly developed for use in wireless sensors network (WSN) applications; however, due to characteristics such as low power and small form factor, they are also being widely used in BSN applications, making it necessary to evaluate their suitability in this context. The delivery ratio and end-to-end delay were evaluated, under contention, for both star and tree topologies. The reliability of the ZigBee network in a star topology without hidden nodes was very good (delivery ratio close to 100 %), provided the acknowledgement mechanism was enabled. On the other hand, the performance in a tree topology was degraded due to router overload and the activation of the route maintenance protocol triggered by periods of high traffic load. The effect of the devices’ clock drift and hidden nodes on the reliability of the star network was modeled and validated through experimental tests. In these tests, the worst-case delivery ratio when the acknowledgment is used decreased to 90 % with two sensor nodes, while for the non-acknowledged mode the result was of 13 %. These results show that a mechanism for distributing the nodes’ traffic over the time is required to avoid BSN performance degradation caused by router overload, clock drift and hidden node issues.