Rumor Source Localization Using Full-Order Neighbor Coverage
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Solution Overview
Problem
Existing source localization methods face challenges in achieving accurate and efficient localization of rumor sources in real-world scenarios due to high labor and time costs for data collection, reliance on unrealistic propagation assumptions, and low accuracy, especially under low infection rates, leading to increased localization errors and difficulty in early detection.
Innovation Solution
A source localization method based on a full-order neighbor coverage strategy, deploying sensors using a full-order neighbor coverage strategy to ensure wide deployment, calculating source likelihood scores with a formula combining 'minimum infection center' and 'time-distance ratio', and applying a penalty coefficient to non-sensor nodes to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional source localization methods are used, then localization can be performed, but accuracy is low and time consumption is high due to extensive data collection requirements
Solution Approach 1:
The patent extracts and utilizes only the essential propagation information (infection time and direction) from the complex propagation data, rather than requiring complete observation of all nodes. This extraction approach enables accurate localization with minimal data collection, significantly reducing time consumption while maintaining high accuracy.
Solution Approach 2:
The patent employs preliminary action by pre-deploying sensors according to the full-order neighbor coverage strategy before the rumor propagation occurs. This preliminary sensor deployment ensures that when propagation happens, the necessary observation data is already in position to be captured, enabling early detection and localization without requiring extensive real-time data collection.
2Loss of information
If complete observation methods are used, then comprehensive infection information can be obtained, but labor and time costs increase significantly
Solution Approach 1:
The patent extracts only the critical propagation information (infection time and direction) needed for localization, rather than collecting complete observation data from all nodes. This selective extraction maintains sufficient information for accurate localization while dramatically reducing data collection time and labor requirements.
Solution Approach 2:
The patent applies partial action by deploying sensors at specific strategic locations (full-order neighbor coverage) rather than observing all nodes completely. This partial observation approach provides sufficient information for localization without the excessive time and labor costs of complete observation.
3Productivity
If sensor-based methods with extensive sensor deployment are used, then localization efficiency improves, but device complexity and deployment cost increase
Solution Approach 1:
The patent applies local quality by deploying sensors at specific strategic locations (full-order neighbor coverage) rather than uniformly throughout the network. This targeted deployment achieves high localization efficiency at these key positions while keeping overall sensor deployment numbers low, reducing device complexity and cost.
Solution Approach 2:
The patent uses partial action by deploying a limited number of sensors at critical locations rather than extensively throughout the entire network. This partial deployment achieves sufficient localization efficiency without the excessive device complexity and deployment costs associated with comprehensive sensor coverage.
4Ease of manufacture
If simple propagation models are used, then model implementation is easy, but the models cannot reflect actual propagation situations well
Solution Approach 1:
The patent applies local quality by allowing different nodes to have different propagation characteristics (infection rates and propagation times) based on their local properties, rather than using uniform parameters throughout the network. This heterogeneous approach enables the model to reflect actual propagation situations accurately while remaining implementable through the proposed localization method.
Data Source
AI summary
Source localization method for rumor source based on full-order neighbor coverage strategy includes: constructing a network graph according to the user relationship in the actual target area; mapping an actual relationship into the network graph; determining sensors in the network graph, and deploying users corresponding to the sensors as observation users in an actual target area; executing a source inferring strategy when the number of the observation users in the actual target area who have received the rumor reaches an expected scale; calculating source likelihood score of non-sensor nodes in the network graph corresponding to the non-observation users in the actual target area; processing differentially the source likelihood scores; and outputting the non-observation user corresponding to the minimum source likelihood score as the source.