IoT Device Placement Optimization Using Covariance Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing IoT device placement methods often result in excessive device deployment, leading to redundant coverage and inefficient use of resources, as they assume regular and homogeneous coverage ranges without accounting for actual detection capabilities and geospatial variations.
Innovation Solution
A computer-implemented method using a covariance model to determine the minimum number of IoT devices required for full coverage by training a detector to assess detection scores and optimizing device placement, thereby minimizing overlap and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IoT devices are deployed to ensure full coverage, then coverage reliability is improved, but device quantity and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal device placements using simulation and covariance models before actual deployment. The coverage area is divided into clusters, and detector training is conducted in advance to assess detection scores, allowing the minimum necessary devices to be identified and positioned beforehand to ensure full coverage without redundancy.
Solution Approach 2:
The system changes key parameters including device placement coordinates, detection score thresholds, and coverage range assumptions. By using a covariance model to analyze relationships between device positions and coverage effectiveness, the system optimizes these parameters to determine the minimum device quantity needed while maintaining reliable full coverage.
2Measurement precision
If detection score thresholds are lowered to capture more devices, then detection sensitivity is improved, but false positive rate increases
Solution Approach 1:
The system implements feedback mechanisms through iterative detector training and covariance model analysis. Detection scores are continuously evaluated against training data, and the model adjusts thresholds based on observed performance. This feedback loop allows the system to maintain high detection sensitivity while filtering out false positives by comparing predicted detection scores against actual coverage outcomes.
Solution Approach 2:
The system applies partial action by using detection score thresholds that are not overly sensitive, accepting that some edge cases may be missed rather than risking false positives. The covariance model compensates for this by analyzing the distribution of detection scores and adjusting placement recommendations to ensure adequate coverage without relying on excessively low thresholds.
3Ease of manufacture
If coverage range is assumed to be regular and homogeneous, then placement calculation is simplified, but actual detection capability is not accurately reflected
Solution Approach 1:
The system segments the coverage area into multiple clusters rather than treating it as a single homogeneous region. This segmentation allows different spatial zones to have different coverage characteristics, enabling more accurate detection of actual device capabilities while maintaining computational feasibility through localized analysis of each cluster.
Solution Approach 2:
The system applies local quality by allowing coverage range assumptions to vary across different spatial locations. Instead of using a single uniform coverage model, the system adjusts coverage parameters based on local detection scores and covariance analysis, recognizing that detection capability differs by location while keeping calculations manageable through structured regional analysis.
Data Source
AI summary
A computer-implemented method includes: receiving, by a computing device, information regarding a range in which an Internet-of-Things (IoT) network is to be implemented; determining, by the computing device, respective detection scores for a plurality of IoT devices for each of a plurality of proposed congregation of IoT devices; determining, by the computing device, a minimum number of the plurality of IoT devices to cover the range by incorporating the detection scores into a covariance model; and outputting, by the computing device, information identifying the minimum number of the plurality of IoT devices for designing the IoT network.