Spatial Indexing for Data Searching in Mobile Sensing Environments

Abstract

Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

A Storage Method for Remote Sensing Images Based on Google S2

“…For the model based on the Z-order curve, we choose the Geohash code as the geolocation code. Genhash is a practical application of the Z-order curve that is widely used to index spatiotemporal data [31]- [35].…”

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

“…With the increasing deployment of mobile sensor nodes in typical smart city implementations, data source detection and data collection mechanisms need to evolve to manage the resultant mobility issues. An interesting search mechanism for retrieving observation data from mobile sources has been detailed in [62]. As pointed out in [98], data collection methods need to leverage Big Data techniques and distributed intelligence to be able to facilitate cooperative sensing and transparent access to data processing mechanisms.…”

“…The corresponding data are stored in databases and retrieved in XML format, containing the following fields: bus ID, bus line, temperature, humidity, speed, latitude, longitude, tested data, tested time and illumination data. Mobile sensing gives an opportunity for large scale environmental monitoring with limited number of sensors, however, it also needs a proper approach for managing the fast changes in the locations of data and data sources [62]. sensors involved in the water distribution network system [71] include the water sensors, which monitor the flow and pressure, and water quality sensors.…”

“…The corresponding data collection mechanisms cover a wide range, including data upload and retrieval from sensor gateways through Representational State Transfer (RESTful) Application Programming Interface (API) [60] or Constrained Application Protocol (CoAP) [61], opportunistic data transmission from mobile sensors [15,62], such as those mounted on public transport vehicles. User-contributed data involves citizens explicitly uploading data from their sensor-rich mobile phones as well as urban data information extracted from online social networks.…”

High‐performance implementation of a two‐bit geohash coding technique for nearest neighbor search

“…In addition to the human biometric data, location data is also collected from wearable sensors of wireless body area networks with which a geohash based spatial index is built to support location‐aware health monitoring in a NoSQL database that makes location‐based query processing efficient 30 . It is used for efficient query processing in mobile sensing environments, 31 corporate GIS database, 32 and big data stored in a distributed file system 33 . An adaptive Hilbert‐geohash coding method is being used in high‐performance GIS to speed up the execution of range and neighbor queries.…”