Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O3 analyzer at the station along with a network of three O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O3 site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O3 trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site

A vertical wind turbine monitoring system using commercial online digital dashboard

The output of a green energy generator is required to be monitor continuously. The monitoring process is important because the performance of the energy gen- erator needs to be known and evaluate. However, monitoring the generator manu- ally and efficiently is troublesome. Moreover, when most of the energy generator located at uneasy to reach or at a very remote place. Added to the cost, human intervention for the monitoring process contributes to the unnecessary bill. All the highlighted limitations can be overcome using an internet cloud base system and application. Most of the existing data logging instruments use a memory card or personal computer in their operation. The stored data is accessible only at a dedicated computer alone. This work presented a complete energy generator interface with a commercial online digital dashboard. The digital dashboard, parameters of the wind turbine, such as the amount of power generates and the magnitude of instantaneous voltage can be monitored, and the recorded data can be accessed quickly, at any time and anyplace

Interoperability enhancement of IoT devices using open web standards in a smart farming use case

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSince its rst appearance the Internet of Things has been subject to constant evolution, development and change. Now it has stepped out of its infancy with billions of devices embedded in the world wide web. However, IoT providers mostly de ne their own data formats and protocols and there is still a lack of a common standard that connects these devices in an interoperable manner. There are several organisations dedicated to developing common standards for IoT devices and research is focusing on de ning an e ective standard to be used by embedded devices. Unsurprisingly, IoT has also found its way into the spatial web and into environmental monitoring and sensing platforms connected over the web by wireless sensor networks are now a common way to monitor natural phenomena. This study compares three open Web Standards in the use case of SEnviro for Agriculture, a full stack IoT for monitoring vineyards. The interoperability potential of the OGC's Sensor Observation Service and SensorThings API are evaluated by integrating Web Standard implementations for each standard and contrasting their qualitative and quantitative traits. In a further step the Mozilla Corporation's Web Thing API was implemented and evaluated in an environmental monitoring and Smart Farming context. The results of the study show that the SensorThings API proves to be the most adequate Web Standard for SEnviro and IoT applications for environmental monitoring and Smart Farming in terms of interoperability. It outperforms the contesting Web Standards in terms of exibility and scalability, which strongly impacts on developer and user experience

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security