An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards—Case Study: Workplace Reopening

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

GEOSPATIAL SENSOR WEB ADAPTOR FOR INTEGRATING DIVERSE INTERNET OF THINGS PROTOCOLS WITHIN SMART CITY

In recent years, the multi-scale comprehensive perception is central to smart city development. We propose an "adaptor" for geospatial sensor web as an integrated sensory system that can integrate access to geodetic equipment based on the Internet of Things technology with multiple platforms and protocols. At the same time, the acquisition, fusion, and processing of sensory resources can perform. The geospatial adaptor can access and process sensors of different IoT protocols to different conditions simultaneously. Grace to this geospatial adaptor, a considerable number of the sensor based on IoT in the community, can achieve distributed access, ensuring the better robustness of the geospatial sensor web. This paper describes the system architecture of the geospatial sensor web adapter. Furthermore, from the perspective of protocol access, it introduces the access capabilities of geospatial sensor web adapter to the standard IoT interface protocols. By comparing the geospatial sensor web adapter with traditional observation methods by experiments and acquisition of test data. The results show that the geospatial sensor web adapter can achieve powerful access capabilities and network stability, and it is a better solution for heterogeneous sensing platform access in smart cities.

Serverless GEO Labels for the Semantic Sensor Web

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

Pemantauan Kualitas Udara Terintegrasi dengan Semantic Web Of Thing

Abstrak: Udara merupakan salah satu sumber daya alam yang paling penting bagi keberadaan makhluk hidup di bumi ini. Semua organisme hidup membutuhkan kualitas udara yang baik bebas dari gas berbahaya untuk melanjutkan hidup mereka. Beberapa organisasi telah membuat sistem monitoring dengan struktur data yang berbeda tanpa adanya standar penyamaan. Di sisi lain, manusia masih membutuhkan waktu untuk menafsirkan data-data sensor untuk mendapatkan informasi. Linked Data merupakan metode untuk merepresentasikan dan menghubungkan data terstruktur pada web. Data terstruktur tersebut diintegrasikan dengan Semantic Sensor Web (SSW) yang dipublikasikan pada beberapa format sehingga mudah dibaca mesin dan dapat dihubungkan ke data terstruktur lainnya. Kemudian, untuk menyajikan data yang aktual, sistem monitoring didesain untuk menerima data secara terus-menerus, diquery secara real-time dan dibagikan melalui sosial media. Kata kunci: Linked Data, Pemantauan Kualitas Udara, Semantic Web, Sosial Media. Abstract: Air is one of the most essential natural resources for the existence and survival of the entire life on this planet. all living organisms need good quality of air which is free of harmful gases to continue their life. Some organizations have set up monitoring systems with different data structures without an equalization standard. On the other hand, humans still need time to interpret sensor data to get information. Linked Data is a method for representing and connecting structured data on the web. The structured data is integrated with the Semantic Sensor Web (SSW) which is published in several formats so that it is easy to read and can be connected to other structured data. Then, to present the actual data, the monitoring system is designed to receive data continuously, queried in real time and shared through social media Keywords: Air Quality Monitoring, Linked Data, Semantic Web, Social Media

Multi-platform volcanological imaging: Two decades of thermal infrared data from the ASTER and MODIS sensors

<p>For the past 20 years, the ASTER and MODIS instruments on Terra have acquired thermal infrared (TIR) data of the world’s volcanoes. These observations have improved our knowledge of long-term volcanic behavior, eruption monitoring, and post-eruption change. MODIS acquires images twice per day (later doubling this after the launch of Aqua) with 1 km TIR and mid-IR resolution. The volcano data from MODIS were later organized into global automated observation programs such as MODVOLC (USA) and later MIROVA (IT). These systems continually detect and track the amount of emitted energy at each active volcano, resulting in vast databases over time that are critically important for ongoing eruptions. Unlike MODIS, ASTER is scheduled and acquires TIR data at 90 m spatial resolution nominally every 5 – 16 days depending on the latitude. This can be improved to hours with proper scheduling and orbital dependencies using its expedited data system. For the past 15 years, an ASTER program called the Urgent Request Protocol (URP) has combined the rapid detection capability of MODIS with the high resolution expedited observations of ASTER in a sensor-web approach. The URP is operated by the University of Pittsburgh in conjunction with (and the support of) the Universities of Alaska, Hawaii, Turin (IT), Clermont Auvergne (FR), and Bristol (UK) as well as the USGS, the LP DAAC and the ASTER science team. The data are used for: operational response to new eruptions; determining thermal trends months prior to an eruption; inferring the emplacement of new lava lobes; and mapping the constituents of volcanic plumes, to name a few. This ASTER TIR archive of volcanic data is now being mined to provide statistics for future TIR orbital concepts being considered by NASA. As TIR instruments get smaller and more numerous with the use of uncooled detectors, they will become CubeSat compatible and could operate in a multi-platform, sensor-web architecture. This would improve response times to volcanic crises and enable new measurements such as the global inventory of volcanic degassing, thermal precursory trends at every volcano, and active flow temperatures at the minute timescale required for predictive flow and hazard assessment models. The combined spatial, spectral and temporal resolutions of ASTER and MODIS enabled a new multi-platform, multi-scale approach to volcanic remote sensing, a model which could be greatly improved depending on future instrument/mission selections.</p>

Serverless GEO Labels for the Semantic Sensor Web

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

A Sensor Web Prototype for Cabled Seafloor Observatories in the East China Sea

Seafloor observatories enable continuous power supply and real-time bidirectional data transmission, which marks a new way for marine environment monitoring. As in situ observation produces massive data in a constant way, the research involved with data acquisition, data transmission, data analysis, and user-oriented data application is vital to the close-loop operations of seafloor observatories. In this paper, we design and implement a sensor web prototype (ESOSW) to resolve seafloor observatory information processing in a plug-and-play way. A sensor web architecture is first introduced, which is information-oriented and structured into four layers enabling bidirectional information flow of observation data and control commands. Based on the layered architecture, the GOE Control Method and the Hot Swapping Interpretation Method are proposed as the plug-and-play mechanism for sensor control and data processing of seafloor observatory networks. ESOSW was thus implemented with the remote-control system, the data management system, and the real-time monitoring system, supporting managed sensor control and on-demand measurement. ESOSW was tested for plug-and-play enablement through a series of trials and was put into service for the East China Sea Seafloor Observation System. The experiment shows that the sensor web prototype design and implementation are feasible and could be a general reference to related seafloor observatory networks.

Applying OGC Sensor Web Enablement Standards to Develop a TDR Multi-Functional Measurement Model

Time-domain reflectometry (TDR) is considered as a passive monitoring technique which reveals multi-functions, such as water level, bridge scour, landslide, and suspended sediment concentration (SSC), based on a single TDR device via multiplexing and related algorithms. The current platform for revealing TDR analysis and interpreted observations, however, is complex to access, thus a coherent data model and format for TDR heterogeneous data exchange is useful and necessary. To enhance the interoperability of TDR information, this research aims at standardizing the TDR data based on the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. To be specific, this study proposes a TDR sensor description model and an observation model based on the Sensor Model Language (SensorML) and Observation and Measurement (O&M) standards. In addition, a middleware was developed to translate existing TDR information to a Sensor Observation Service (SOS) web service. Overall, by standardizing TDR data with the OGC SWE open standards, relevant information for disaster management can be effectively and efficiently integrated in an interoperable manner.