occupancy sensor accuracy IoT
AI Search Visibility Analysis
Analyze how brands appear across multiple AI search platforms for a specific query
Total Mentions
Total number of times a brand appears
across all AI platforms for this query
Platform Presence
Number of AI platforms where the brand
was mentioned for this query
Linkbacks
Number of times brand website was
linked in AI responses
Sentiment
Overall emotional tone when brand is
mentioned (Positive/Neutral/Negative)
Brand Performance Across AI Platforms
| BRAND | TOTAL MENTIONS | PLATFORM PRESENCE | LINKBACKS | SENTIMENT | SCORE |
|---|---|---|---|---|---|
1OCTIOT | 0 | 2 | 95 | ||
2Milesight | 0 | 1 | 72 | ||
3InnerSpace | 0 | 1 | 55 |
Strategic Insights & Recommendations
Dominant Brand
OCTIOT emerges as a standout brand with their sensors claiming 99% accuracy, while Milesight's VS330 sensor achieves industry-leading 99.5% accuracy using combined ToF and PIR technologies.
Platform Gap
ChatGPT provides the most technical depth with specific accuracy ranges and sensor fusion details, while Google AIO offers practical implementation insights, and Perplexity focuses on privacy-compliant solutions.
Link Opportunity
There's significant opportunity to link to sensor manufacturers like OCTIOT and Milesight, as well as research publications from MDPI and IEEE that provide technical validation of accuracy claims.
Key Takeaways for This Query
IoT occupancy sensors achieve accuracy rates ranging from 75% to 99.9% depending on the technology used.
Advanced sensors combining ToF and PIR technologies can exceed 99.5% accuracy in optimal conditions.
AI-powered video sensors provide over 98% accuracy while maintaining GDPR privacy compliance.
Sensor fusion and proper placement are critical factors for maximizing occupancy detection accuracy.
AI Search Engine Responses
Compare how different AI search engines respond to this query
ChatGPT
BRAND (2)
SUMMARY
ChatGPT provides a comprehensive technical overview of occupancy sensor accuracy in IoT systems, detailing various sensor types including PIR (75-98% accuracy), ultrasonic, Time-of-Flight (99.5% accuracy), Wi-Fi-based systems, and BLE devices (97.97% accuracy). The response emphasizes how accuracy depends on sensor placement, environmental conditions, and occupant behavior, with sensor fusion improving overall performance.
REFERENCES (5)
Perplexity
BRAND (2)
SUMMARY
Perplexity provides specific accuracy ranges (98-99.9%) for IoT occupancy sensors, focusing on bi-directional people counting sensors and AI-powered video sensors. The response emphasizes privacy compliance (GDPR), discusses how combining multiple sensors enhances accuracy, and highlights real-time applications for smart building management including energy efficiency and space utilization.
REFERENCES (6)
Google AIO
BRAND (1)
SUMMARY
Google AIO focuses on practical accuracy metrics, highlighting OCTIOT sensors with 99% accuracy and video counting systems achieving over 98%. The response covers how different sensor types (PIR, ultrasonic) have varying accuracy levels, discusses environmental factors affecting performance, and emphasizes the benefits of accurate occupancy detection for energy savings and space optimization.
REFERENCES (20)
Share Report
Share this AI visibility analysis report with others through social media