Distributed Sensor Network Center Estimation Algorithm
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Solution Overview
Problem
Existing decentralized wireless sensor networks face challenges in estimating the network center and area efficiently, as sensor nodes only have local information and require high computational complexity methods like Delaunay triangulations and Voronoi diagrams, which are not scalable or accurate.
Innovation Solution
A fully distributed algorithm using soft-max approximation and stochastic gradient descent or diffusion adaptation methods to estimate the network center and radius, allowing nodes to communicate locally without a fusion center, formulating the problem as a convex optimization problem and using max consensus for radius estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Delaunay triangulations and Voronoi diagrams are used for network center and area estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex geometric computation methods (Delaunay triangulations and Voronoi diagrams) with a simplified iterative optimization algorithm. Instead of performing heavy geometric constructions, the system uses an objective function based on distance measurements from nodes to estimate network center and area, significantly reducing computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The patent transforms the estimation problem by changing the approach from direct geometric calculation to iterative parameter optimization. The system defines an objective function with parameters representing network center coordinates and area, then iteratively adjusts these parameters to minimize the function, achieving accurate estimation with lower computational burden.
2Adaptability or versatility
If decentralized algorithms are used for network estimation, then adaptability is improved, but measurement precision worsens
Solution Approach 1:
The patent implements a feedback mechanism where each node continuously refines its estimation of network center and area based on distance measurements from neighboring nodes. The iterative optimization process allows nodes to adjust their estimates based on local information and feedback from the network, achieving both high adaptability to distributed configurations and accurate measurement precision.
Data Source
AI summary
Some embodiments include a wireless sensor network including a plurality of sensor nodes each comprising: a signal receiver configured to receive intermediate information from at least one of one or more neighboring nodes of the plurality of sensor nodes, one or more processors configured to receive the intermediate information and update the intermediate information based on a soft-max approximation function, and a transmitter configured to send the intermediate information, as updated, to at least one of the one or more neighboring nodes of the plurality of sensor nodes. For each sensor node of the plurality of sensor nodes: the sensor node can store local location coordinates for the sensor node, and the sensor node can be devoid of receiving location coordinates for any other of the plurality of sensor nodes. The plurality of sensor nodes can be configured to communicate in a distributed manner for a first plurality of iterations until a final iteration of the first plurality of iterations when a predetermined stopping condition is satisfied. The plurality of sensor nodes can be further configured to generate an estimated center of the wireless sensor network based on the intermediate information updated in the final iteration of the first plurality of iterations. The wireless sensor network can be devoid of a fusion center. Other embodiments are disclosed.