Spatial Search Index for Sensor Data Web Portal Latency
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Solution Overview
Problem
Sensor data web portals experience time delays due to the continuously changing nature of sensor readings and unavailability of data, necessitating an improvement in data collection and presentation methods.
Innovation Solution
The implementation of a spatial search technique that uses a data tree index structure to identify a subset of sensors with a similar spatial distribution to the original set, reducing the number of sensors that need to be probed by leveraging cached data and layered sampling algorithms, thereby enhancing query efficiency and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensors are probed to ensure complete data coverage, then data completeness is improved, but latency increases due to the large number of sensors
Solution Approach 1:
The patent segments the full set of sensors into multiple subsets, where each subset spatially represents the complete sensor network. Instead of probing all sensors, the system probes only one subset, significantly reducing the number of probes needed while maintaining data representativeness. This segmentation resolves the contradiction by dividing the large sensor set into smaller manageable groups that can be sampled efficiently.
Solution Approach 2:
The patent applies partial action by probing only a portion of the sensor network (one subset out of multiple) rather than all sensors. The subset is carefully designed to spatially represent the entire network, so that partial probing yields sufficient data completeness. This partial action reduces latency while maintaining acceptable data coverage through statistical representation.
2Loss of time
If a subset of sensors is probed to reduce latency, then latency is reduced, but data completeness may be compromised
Solution Approach 1:
The sensor network is segmented into multiple subsets, each designed to spatially represent the complete network. By probing one subset, the system achieves sufficient data completeness without needing to probe all sensors. The segmentation ensures that each subset contains spatially distributed sensors that collectively represent the entire network's data characteristics.
Solution Approach 2:
Each subset acts as a representative copy or sample of the complete sensor network. Instead of probing the full network, the system probes a smaller copy (subset) that replicates the spatial distribution and data characteristics of the entire network. This copying approach maintains data completeness while reducing the probing burden and latency.
3Loss of energy
If the number of sensors to be probed is reduced, then communication overhead is reduced, but the accuracy of spatial representation may deteriorate
Solution Approach 1:
The patent segments the sensor network into multiple subsets with specific spatial distribution properties. Each subset is designed to maintain accurate spatial representation of the complete network, ensuring that reducing the number of probed sensors does not compromise spatial accuracy. The segmentation strategy optimizes the balance between communication overhead and spatial representation fidelity.
Data Source
AI summary
Techniques for collecting and displaying sensor data captured by a spatially representative sample of sensors requested in a search query are described. The sensors are represented in an index structure (e.g., a data tree). In response to a query, the index structure is leveraged to identify a subset of sensors that exhibits a similar spatial distribution to the original full set of sensors. Sensor data is then collected from the subset of sensors by probing the sensors or retrieving recently cached data located by the index and returned to satisfy the query. In this manner, the number of sensors to be probed is reduced, thereby reducing latency involved with polling a large number of sensors and making the search process more efficient.