Premises Context Collection Before Emergency Connectivity Loss
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Solution Overview
Problem
Existing systems fail to effectively leverage connected devices in customer premises to proactively collect and report context information in response to predicted emergency events, such as natural disasters, which can disrupt internet connectivity and communication.
Innovation Solution
A cloud-based prediction system dynamically provisions internet-connected devices at customer premises to collect and report context information before, during, and after an emergency event by selecting a coordinating device based on capabilities and ensuring data collection continues even with loss of WAN connectivity or power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If devices continuously monitor and report context information, then emergency response effectiveness is improved, but energy consumption increases and device battery life is depleted
Solution Approach 1:
The system performs preliminary actions by dynamically provisioning devices to enter monitoring mode only when an emergency event is predicted. The cloud-based prediction system activates devices before the actual emergency occurs, allowing them to collect and report context information during critical periods without continuous operation. This resolves the contradiction by limiting energy consumption to only when emergency response is most needed.
Solution Approach 2:
The system implements dynamic state changes where devices transition between dormant and active monitoring states based on emergency predictions. The cloud-based prediction system adjusts device provisioning dynamically, allowing devices to collect information during predicted emergency windows while remaining inactive otherwise. This dynamic approach optimizes the balance between continuous emergency response capability and energy conservation.
2Reliability
If devices collect comprehensive context information, then emergency response quality is improved, but data collection complexity and system overhead increase
Solution Approach 1:
The system applies local quality by having each device collect only the specific context information most relevant to its local environment and function. Instead of uniform comprehensive monitoring, devices provisioned during predicted emergencies collect tailored information (e.g., occupancy sensors count people, environmental sensors monitor temperature) appropriate to their role. The cloud-based prediction system coordinates this localized data collection, reducing overall system complexity while maintaining response quality.
Solution Approach 2:
The system segments the data collection function across multiple specialized devices rather than requiring a single complex centralized system. The cloud-based prediction system coordinates distributed devices (occupancy sensors, environmental sensors, communication devices) that each collect specific information types. This segmentation reduces individual device complexity while achieving comprehensive emergency response through coordinated distributed measurement.
3Duration of action of stationary object
If the system operates during WAN connectivity loss, then emergency response continuity is improved, but communication reliability deteriorates
Solution Approach 1:
The system performs preliminary actions by provisioning devices to collect and cache context information before and during predicted emergencies. When WAN connectivity is lost, devices that have been pre-provisioned with emergency response capabilities can continue collecting and storing local data without requiring continuous external communication. The cloud-based prediction system coordinates this preliminary data collection, ensuring response continuity during connectivity outages.
Solution Approach 2:
The system uses local device memory and processing capability as intermediaries between the emergency event and external communication systems. When WAN connectivity fails, cached context information stored locally serves as an intermediary that preserves emergency response functionality. The cloud-based prediction system coordinates this local caching mechanism, allowing devices to maintain operational independence during communication outages while still enabling eventual data synchronization when connectivity restores.
Data Source
AI summary
A method and system for collecting context information in response to prediction of an emergency event. A cloud-based computing system could determine that an emergency event is predicted to impact a customer premises at an upcoming time. Responsive to that determination, and before the upcoming time, the cloud-based computing system could then cause or more on-premises computing devices at the customer premises to collect and report context information, such as a count of people present at the customer premises and/or an operational state of one or more utilities or other systems at the customer premises, that may assist in responding to the emergency event. Further, the cloud-based computing system could select a given such device at the customer premises to function as a coordinating device to work with one or more other devices at the customer premises to collect and report the context information.