Many sensor networks have been deployed to monitor Earth’s environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.

Recently, researchers are focusing on a new use of the Internet called the Internet of Things (IoT), in which enabled electronic devices can be remotely accessed over the Internet. As the realization of IoT concept is still in its early stages, manufacturers of Internet-connected devices and IoT web service providers are defining their proprietary protocols based on their targeted applications. Consequently, IoT becomes heterogeneous in terms of hardware capabilities and communication protocols. Addressing these heterogeneities by following open standards is a necessary step to communicate with various IoT devices. In this research, we assess the feasibility of applying existing open standards on resource-constrained IoT devices. The standard protocols developed in this research are OGC PUCK over Bluetooth, TinySOS, SOS over CoAP, and OGC SensorThings API. We believe that by hosting open standard protocols on IoT devices, not only do the devices become self-describable, self-contained, and interoperable, but innovative applications can also be easily developed with standardized interfaces. In addition, we use memory consumption, request message size, response message size, and response latency to benchmark the efficiency of the implemented protocols. In all, this research presents and evaluates standard-based solutions to better understand the feasibility of applying existing standards to the IoT vision.

The world-wide sensor web has become a very useful technique for monitoring the physical world at spatial and temporal scales that were previously impossible. Yet we believe that the full potential of sensor web has thus far not been revealed. In order to harvest the world-wide sensor web’s full potential, a geospatial cyberinfrastructure is needed to store, process, and deliver large amount of sensor data collected worldwide. In this paper, we first define the issue of the sensor web long tail followed by our view of the world-wide sensor web architecture. Then, we introduce the Geospatial Cyberinfrastructure for Environmental Sensing (GeoCENS) architecture and explain each of its components. Finally, with demonstration of three real-world powered-by-GeoCENS sensor web applications, we believe that the GeoCENS architecture can successfully address the sensor web long tail issue and consequently realize the world-wide sensor web vision.

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.

The increasing availability of sensor devices has resulted in important volumes of sensor data, which has raised the issue of making these data fully discoverable and interpretable by applications and end-users. The idea of OGC Sensor Web Enablement (SWE) has addressed this issue by proposing a set of standards to enable accessibility of sensor data over the Web. Similarly, there is a growing interest in volunteered geographic information (VGI). Considering that several researchers have highlighted the potential of this new type of information as a complement to existing, “traditional” data, it becomes important to develop frameworks to support the integration of VGI from several sources and with other types of data. In this paper, we make a first step in this direction by proposing a framework for the semantic interoperability of sensor data and VGI. After having performed an investigation of the types of VGI applications, we have developed a conceptual model of VGI aligned with relevant ISO standards for describing geospatial features. The purpose of this model is to support the generation of common descriptions for VGI applications, which will act as interfaces to higher-level services, such as discovery and reasoning services, in order to be exploited in conjunction with sensor data by client applications. This process is described through architecture for semantic interoperability of sensor data and VGI that we have developed and that we intend to use to set guidelines for future research on integration of VGI in sensor data cyberinfrastructures. We illustrate the possibilities created by the proposed framework with a description of the various services and interfaces required to implement the framework.

With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users’ contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%.