Abstract
Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM) focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS) for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL) SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS) used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.
Highlights
In terms of creating a better future of smart cities, it becomes crucial when considering what information is accessible to whom at a fine spatio-temporal scale where individuals can be identified [1]
The response time of the access and insertion for navigation and positioning data can be realized at the millisecond level through the proposed MongoSOS, which is a Not Only SQL (NoSQL) Sensor Observation Service (SOS) based on the MongoDB database and has the capability of distributed storage
According to the different spatio-temporal characteristics of the point observations by fixed or mobile sensors, in particular the observation results are a series of discrete points that vary over time or space, point observation data are classified into two types: the in-situ monitoring data and the navigation and positioning data, which can be viewed as the two feature types of Observations and Measurements Schema (O&M), namely, the DiscretePointCoverage and the DiscreteTimeInstantCoverage, respectively
Summary
In terms of creating a better future of smart cities, it becomes crucial when considering what information is accessible to whom at a fine spatio-temporal scale where individuals can be identified [1]. Because of the differences both in sensors and observation principles, it is hard to discover and access the consistent and instant interdisciplinary point observation data, the problem of the effective sharing for multi-source, decentralized and heterogeneous data has not been solved. To solve the above problems related to the sharing of diverse instant point observation data from different urban departments, this paper proposes both a new spatio-temporal enhanced Data Meta-Model (DMM) and a prototype system based on DMM. Considering Extensible Markup Language (XML) as the technology of choice for exchanging information on the Web, it is necessary to propose a suitable point observation data meta-model combined with existing interoperability standards through XML and XML Schema for the improvement of the spatio-temporal enhanced sharing. This section is mainly to propose and construct the DMM for interdisciplinary instant point observations sharing in smart cities
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