Personalized Hazard Warning System Using IoT Segmentation
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Solution Overview
Problem
Conventional hazard warning systems are aesthetically unappealing, over-inclusive, difficult to comprehend, and reactive, failing to effectively warn individuals of potential hazards in a personalized and proactive manner.
Innovation Solution
A potential hazard warning system architecture that utilizes a cloud service provider to aggregate and classify crowdsource data from IoT devices and client devices, employing machine learning to generate personalized warnings through various media channels, such as augmented reality, based on user preferences and contextual attributes, while adapting to real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional physical signage is used for hazard warnings, then the warning can be displayed publicly, but it is aesthetically unappealing and over-inclusive
Solution Approach 1:
The system segments the hazard warning function by identifying individual users at risk through device tracking and classifying hazards by type (environmental, crime, illegal materials, language mismatch). This allows personalized warnings to be sent to specific users via their devices rather than using general public signage, achieving personalization without requiring complex infrastructure changes.
Solution Approach 2:
The system introduces mobile devices and communication networks as intermediaries between hazard detection and user warning. Instead of direct physical signage, the system uses smartphones, tablets, or computers as mediators to deliver personalized hazard information to users, improving aesthetics and personalization while maintaining system manageability.
2Adaptability or versatility
If physical signage is used for hazard warnings, then the warning can be displayed to the public, but it is difficult to comprehend by foreign visitors
Solution Approach 1:
The system applies local quality by tailoring warning content to individual user characteristics, including language preferences and cultural background. Each user receives customized hazard information in their preferred language and format, ensuring comprehension while maintaining public safety awareness.
Solution Approach 2:
The system incorporates feedback mechanisms where users can provide input about hazard types they care about, and the system adjusts warnings accordingly. This feedback loop ensures that information is delivered in comprehensible formats suited to each user's needs and language preferences.
3Reliability
If physical signage is used for hazard warnings, then the warning can be displayed statically, but it is reactive rather than proactive
Solution Approach 1:
The system performs preliminary action by continuously monitoring hazard conditions and pre-warning users before hazards occur. Instead of reacting to hazards after they manifest, the system detects potential hazards (such as environmental conditions or crime risks) and sends advance warnings to at-risk users, improving reliability while reducing response time.
Solution Approach 2:
The system maintains continuous monitoring and communication with users, ensuring that hazard detection and warning delivery are ongoing rather than intermittent. This continuous action ensures that users receive timely warnings as conditions change, improving both reliability and responsiveness.
4Adaptability or versatility
If conventional signage is used for hazard warnings, then the warning can be displayed to all passersby, but it is over-inclusive and not relevant to all individuals
Solution Approach 1:
The system segments the general population into specific user groups based on location, preferences, and risk factors. By dividing users into targeted segments, the system can deliver relevant hazard warnings to specific individuals without processing all possible passersby, reducing computational complexity while improving relevance.
Solution Approach 2:
The system applies partial action by focusing hazard monitoring and warning resources on specific users and locations where hazards are most likely to occur, rather than attempting to cover all possible scenarios. This selective approach reduces processing complexity while maintaining high relevance for affected users.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that conducts a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user, personalize a warning of the hazard condition to the user based on one or more user preferences, and send the personalized warning to a client device associated with the user. In one example, the technology conducts a rule based classification of the crowdsource data, wherein the rule based inference analysis is conducted based on the rule based classification.