EFFICIENT IOT-ENABLED HIGH SPEED AND ENERGY EFFICIENT EARLY LANDSLIDE DETECTION AND MONITORING SYSTEM BASED ON GEOTECHNICAL PARAMETERS

Landslides are a growing threat in steep regions of the world, taking lives and damaging property. The recent damages caused by landslides demand that authorities pay attention to catastrophe risk mitigation strategies. One crucial risk reduction strategy is the creation of an efficient landslide early warning system (LEWS), which will allow authorities and the public at large to be informed in advance of any landslide incidents. In order to construct a system of early warning for landslides, a wireless sensing network may collect data on the geological features and a few physical surroundings characteristics. The recommended system's primary objective is to predict when a landslide could happen and alert authorities to prevent or at least minimize casualties. In this study, we show how Internet of Things-based sensors (temperature, soil moisture ,humidity) may be used to observe and alert authorities to impending landslide danger. An advanced landslide tracking system built on Internet of Things infrastructure is shown here. The system is comprised of a group of self-sufficient wearable sensors, each of which wears a sensor costume designed for tracking landslides, and a microprocessor that aggregates data from a wide variety of sensors

Design and Build of IoT Based Flood Prone Monitoring System at Semani’s Pump House Drainage System

Floods are a common disaster in watersheds, and flood control is difficult. However, losses can be reduced by quickly disseminating alert status information. This paper proposes a prototype of a monitoring system that can determine the status of flood alerts in real time and quickly disseminating to the community, allowing people to be better prepared for flood disasters. The system was developed using the RD method and consists of hardware and software development. The hardware comprises several sensor modules to read the discharge, temperature, humidity, and water level and to transmit the readings to the software. The software is divided into two applications: a website application and a Telegram application. The public can find the flood alert status history data from the website and obtain flood alert status warning messages and the latest alert status from Telegram. The results of the tests indicated that the sensors were very accurate, with a MAPE value of less than 10%. The software test also showed that the input and output were according to design. The proposed system can potentially reduce flood losses by providing early warning information to the community. The system is also scalable and adaptable to other watersheds

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

peer reviewedThe 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