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.This work is supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI-01-0145-FEDER-006941

Development and test of an intra-vehicular network based on bluetooth low energy

This paper presents the development and test of an intra-vehicular system, 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 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, which collect relevant sensor information from the battery system and the traction system of an 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 limitations of the BLE platform.This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941.info:eu-repo/semantics/publishedVersio

Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics

In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness imposed on critical sensors within the vehicle. As opposed to point-to-point network topologies, multi-hop networking allows for an enhanced communication reliability at the cost of an additional processing overhead. In this context this manuscript poses a novel bi-objective optimization problem aimed at jointly minimizing (1) the average Bit Error Rate (BER) of sensing nodes under a majority fusion rule at the central data collection unit; and (2) the mean delay experienced by packets forwarded by such nodes due to multi-hop networking, frequency channel switching time multiplexing at intermediate nodes. The formulated paradigm is shown to be computationally tractable via a combination of evolutionary meta-heuristic algorithms and Dandelion codes, the latter capable of representing tree-like structures like those modeling the multi-hop routing approach. Simulations are carried out for realistic values of intra-vehicular radio channels and co-channel interference due to nearby IEEE 802.11 signals. The obtained results are promising and pave the way towards assessing the practical performance of the proposed scheme in real setups

Quality of Assessment in Connected Vehicles

In recent years, there has been a huge interest in Machine-to-Machine connectivity under the umbrella of Internet of Things (IoT). With the UK Government looking to trial autonomous (driverless) cars this year, connected vehicles will play a key part in improving and managing existing road safety and congestion, leading to a new generation of intelligent transport systems. This is also well aligned to the current initiatives by the automotive industry to improve the driver’s experience on-board. However, the wireless channels most suitable for this application have not been standardized. In this paper, we review the wireless channels suitable for vehicle-2-vehicle (V2V) and Vehicle–to-x (V2x) connectivity. We further present preliminary analysis on the factors that impact the Quality of Service (QoS) of connected vehicles. We use the open access GEMV2 data to carry out Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) on the link quality and found that both line of sight and non line of sight has a significant impact on the link quality. The work presented here will help in the development of connected vehicle network (CVN) prediction model and control for V2V and V2x connectivity. It will further contribute towards unfolding and testing key research questions in the context of connected vehicles which may otherwise be overlooked

Quality of Assessment in Connected Vehicles

In recent years, there has been a huge interest in Machine-to-Machine connectivity under the umbrella of Internet of Things (IoT). With the UK Government looking to trial autonomous (driverless) cars this year, connected vehicles will play a key part in improving and managing existing road safety and congestion, leading to a new generation of intelligent transport systems. This is also well aligned to the current initiatives by the automotive industry to improve the driver’s experience on-board. However, the wireless channels most suitable for this application have not been standardized. In this paper, we review the wireless channels suitable for vehicle-2-vehicle (V2V) and Vehicle–to-x (V2x) connectivity. We further present preliminary analysis on the factors that impact the Quality of Service (QoS) of connected vehicles. We use the open access GEMV2 data to carry out Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) on the link quality and found that both line of sight and non line of sight has a significant impact on the link quality. The work presented here will help in the development of connected vehicle network (CVN) prediction model and control for V2V and V2x connectivity. It will further contribute towards unfolding and testing key research questions in the context of connected vehicles which may otherwise be overlooked