Spatial Indexing for Data Searching in Mobile Sensing Environments.

Area of Science:

• Computer Science
• Data Science
• Ubiquitous Computing

Background:

• Web of Things (WoT) applications require efficient data searching and retrieval from large sensor stream datasets.
• Opportunistic sensing with mobile sensors presents unique challenges due to variable intervals and changing geographical locations, unlike fixed sensors.
• Existing spatial indexing techniques are often inadequate for the dynamic nature of mobile sensing data.

Purpose of the Study:

• To develop an efficient spatial indexing technique for searching integrated data from heterogeneous sources in mobile sensing environments.
• To address the challenges posed by mobile sensors reporting data at variable intervals and changing locations.
• To facilitate effective data retrieval for Web of Things applications utilizing opportunistic sensing.

Main Methods:

• Development of the Geohash-Grid Tree, a specialized spatial indexing technique.
• Integration of data from heterogeneous sources within a mobile sensing context.
• Experimental evaluation using a real-world dataset from the SmartSantander smart city testbed.

Main Results:

• The Geohash-Grid Tree demonstrates efficient spatial search capabilities.
• The index structure supports efficient range and time window queries.
• Effective performance was validated on a large time series database from a smart city testbed.

Conclusions:

• The Geohash-Grid Tree is a viable solution for spatial indexing in mobile sensing environments.
• This technique enhances data searching and retrieval for Web of Things applications.
• The proposed method effectively handles data from heterogeneous and dynamic sources.