Wireless Node Position Estimation Using Four-Simplex Geometry
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for discovering the topology of connected systems, such as mesh networks in building automation, are labor-intensive and costly, and may introduce significant errors due to assumptions about node distances.
Innovation Solution
A distributed method where each node in the wireless data network estimates its spatial position by receiving information about other nodes and using a four-simplex configuration to calculate spatial distances without relying on the positions of other nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a distributed position estimation method is used, then network overload is reduced and processing resources are saved, but position estimation accuracy may be compromised without central data gathering
Solution Approach 1:
The patent divides the network into local clusters where each node performs position estimation independently using only data from its immediate neighbors. This segmentation prevents network overload by eliminating the need for centralized data collection while maintaining position estimation accuracy through localized geometric calculations based on received signal strength indicators (RSSI) and distance measurements within each cluster.
Solution Approach 2:
Each node performs position estimation using locally available information from neighboring nodes rather than requiring global network data. The estimation accuracy is maintained by using geometric relationships (triangulation/trilateration) with locally measured distances, which are sufficient for determining position without compromising overall precision while significantly reducing network communication overhead.
2Measurement precision
If manual topology discovery is performed, then accurate node positions are obtained, but installation time and cost increase significantly
Solution Approach 1:
The system enables nodes to automatically discover their positions and the network topology through distributed mutual measurements. Each node independently estimates its position by measuring distances to neighboring nodes and performing geometric calculations, eliminating the need for manual technician intervention while maintaining accurate position data for commissioning and operation.
Solution Approach 2:
The patent performs automatic topology discovery and position estimation during the initial network setup phase before full operation begins. By pre-establishing node positions through distributed measurements and geometric calculations, the system eliminates subsequent manual topology discovery requirements and enables immediate operational use without time-consuming manual configuration.
3Device complexity
If distance assumptions are made in position estimation, then calculation complexity is reduced, but significant errors are introduced in node positions
Solution Approach 1:
The system dynamically adapts the position estimation method based on available data. When direct distance measurements are available from neighboring nodes, the system uses geometric triangulation/trilateration without distance assumptions. When certain measurements are unavailable, the system flexibly switches to alternative estimation approaches, maintaining position accuracy while managing calculation complexity through adaptive algorithm selection.
Solution Approach 2:
The patent changes the estimation parameters based on local network conditions. Instead of using fixed distance assumptions, the system uses actual measured distances from RSSI and signal propagation models specific to each local environment. This parameter adaptation allows accurate position estimation that accounts for varying wireless channel characteristics without requiring overly complex calculations.
Data Source
AI summary
A method for a first node in a wireless data network to estimate its spatial position. The first node receives information including: an identifier of a second node, the distances between various combinations of nodes, including a third, fourth, and fifth node. The first node generates an estimate of its spatial distance to the second node, based on the content of a four-simplex that is defined by the distances between the various combinations of nodes. The first node then generates an estimate of the spatial distance between the first node and an anchor node, based on estimates of the spatial distances between the first and second node and between the second and anchor node. The first node then generates an estimate of its spatial position, based on the estimate of the spatial distance between the first node and the anchor node and the spatial position of the anchor node.