Olivier Debauche

Web-based cattle behavior service for researchers based on the smartphone inertial central

Abstract Smartphones, particularly iPhones, can be relevant instruments for researchers in animal behavior because they are readily available on the planet, contain many sensors and require no hardware development. They are equipped with high performance inertial measurement units (IMU) and absolute positioning systems analyzing users’ movements, but they can easily be diverted to analyze likewise the behaviors of domestic animals such as cattle. The study of animal behavior using smartphones requires the storage of many high frequency variables from a large number of individuals and their processing through various relevant variables combinations for modeling and decision-making. Transferring, storing, treating and sharing such an amount of data is a big challenge. In this paper, a lambda cloud architecture and a scientific sharing platform used to archive and process high-frequency data are proposed. An application to the study of cattle behavior on pasture on the basis of the data recorded with the IMU of iPhones 4S is exemplified. The package comes also with a web interface to encode the actual behavior observed on videos and to synchronize observations with the sensor signals. Finally, the use of fog computing on the iPhone reduced by 42% on average the size of the raw data by eliminating redundancies.

Web Monitoring of Bee Health for Researchers and Beekeepers Based on the Internet of Things

Abstract The Colony Collapse Disorder (CCD) also entitled ‘Colony Loss’ has a significant impact on the biodiversity, on the pollination of crops and on the profitability. The Internet of Things associated with cloud computing offers possibilities to collect and treat a wide range of data to monitor and follow the health status of the colon. The surveillance of the animals’ pollination by collecting data at large scale is an important issue in order to ensure their survival and pollination, which is mandatory for food production. Moreover, new network technologies like Low Power Wide Area (LPWAN) or 3GPP protocols and the appearance on the market easily programmable nodes allow to create, at low-cost, sensors and effectors for the Internet of Things. In this paper, we propose a technical solution easily replicable, based on accurate and affordable sensors and a cloud architecture to monitor and follow bees’ behavior. This solution provides a platform for researchers to better understand and measure the impacts factors which lead to the mass extinction of bees. The suggested model is also a digital and useful tool for beekeepers to better follow up with their beehives. It helps regularly inspect their hives to check the health of the colony. The massive collection of data opens new research for a better understanding of factors that influence the life of bees.

Monitoring System Using Internet of Things For Potential Landslides

Abstract The North-Western RIF of Morocco is considered as one of the most mountainous zone in the Middle East and North Africa. This area is more serious in the corridor faults region, where the recent reactivation of those tectonic layering may greatly contribute to the triggering of landslides. The consequences of this phenomenon can be enormous property damage and human casualties. Furthermore, this disaster can disrupt progress and destroy developmental efforts of government, and often pushing nations back by many years. In our previous works of Tetouan-Ras-Mazari region, we identified the areas that are prone to landslides by different methods like Weights of Evidence (WofE) and Logistic Regression (LR). In fact, these zones are built and susceptible. Undoubtedly, the challenge to save human lives is vital. For this reason, we develop a robust monitoring model as part of an alert system to evacuate populations in case of imminent danger risks. This model is ground-based remote monitoring system consist of more than just field sensors; they employ data acquisition units to record sensor measurements, automated data processing, and display of current conditions usually via the Internet of Things (IoT). To sum up, this paper outlines a new approach of monitoring to detect when hillslopes are primed for sliding and can provide early indications of rapid and catastrophic movement. It reports also continuous information from up-to-the-minute or real-time monitoring, provides prompt notification of landslide activities, advances our understanding of landslide behaviors, and enables more effective engineering and planning efforts.