No abstract

Nowadays, the current vehicles are incorporating control systems in order to improve their stability and handling. These control systems need to know the vehicle dynamics through the variables (lateral acceleration, roll rate, roll angle, sideslip angle, etc.) that are obtained or estimated from sensors. For this goal, it is necessary to mount on vehicles not only low-cost sensors, but also low-cost embedded systems, which allow acquiring data from sensors and executing the developed algorithms to estimate and to control with novel higher speed computing. All these devices have to be integrated in an adequate architecture with enough performance in terms of accuracy, reliability and processing time. In this article, an architecture to carry out the estimation and control of vehicle dynamics has been developed. This architecture was designed considering the basic principles of IoT and integrates low-cost sensors and embedded hardware for orchestrating the experiments. A comparison of two different low-cost systems in terms of accuracy, acquisition time and reliability has been done. Both devices have been compared with the VBOX device from Racelogic, which has been used as the ground truth. The comparison has been made from tests carried out in a real vehicle. The lateral acceleration and roll rate have been analyzed in order to quantify the error of these devices.

This paper presents results of the living labs methodology as applied to rural development. Within the C@R project, seven living labs have been launched in various regions in Europe and South-Africa. A collaborative platform has been developed to support rural communities' collaboration. The architecture of this platform is based on open service oriented architecture and allows for reusing and sharing services and applications. The paper demonstrates the validation and use of this platform, integrating various tools for various user communities, in different living labs settings. A common methodology has been designed to support the living labs launch, operation, experimentation and monitoring process. The paper presents results and evaluation of the living labs methodology. Particular focus is on the impacts of the living labs approach on the innovation process and on rural development in the different rural settings.

Gamification is a research field that is intended to increase motivation, so it is especially indicated in human capital intensive environments such as the software industry. Within Software Engineering, one of the main issues regarding software process improvement (SPI) is personnel motivation in specific SPI initiatives. These issues are stronger in small and medium software development companies where employees have to deal with the pressure of deadlines and occasional work overload. To address the adoption of SPI initiatives, the researchers implemented a defined gamification framework for deployment in SPI efforts in order to increase motivation among software workers and to enhance SPI results. The framework was rolled out in a small Spanish software development organisation, which is conducting internal SPI initiatives. To validate the effectiveness of the implemented framework, a controlled experiment was carried out in which an experimental group adopted SPI improvements using a gamification approach. The implementation results show that the application of the framework does not increase personnel motivation in SPI tasks although it contributes to enhancing the SPI tasks performance. This study discusses the limitations and recommendations to implement appropriately the SPI-gamification framework in the scope of small and medium software development companies.

The high rate of vehicle-crash victims has a fatal economic and social impact in today’s societies. In particular, road crashes where heavy vehicles are involved cause more severe damage because they are prone to rollover. For this reason, many researches are focused on developing RSC Roll Stability Control (RSC) systems. Concerning the design of RSC systems with an adequate performance, it is mandatory to know the dynamics of the vehicle. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill hard real-time processing constraints, achieving high level of accuracy during circulation of a vehicle in real situations. In order to address this issue, this study has two main goals: (1) Design and develop an IoT based architecture, integrating ANN in low cost kits with different hardware architectures in order to estimate under real-time constraints the vehicle roll angle. This architecture is able to work under high dynamic conditions, by following specific best practices and considerations during its design; (2) assess that the IoT architecture deployed in low-cost experimental kits achieve the hard real-time performance constraints estimating the roll angle with the required calculation accuracy. To fulfil these objectives, an experimental environment was set up, composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model Band the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations highly approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risk situations, fulfilling real-time operation restrictions stated for this problem.