Dynamic Customer Premises Context Collection for Predicted Emergencies
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Solution Overview
Problem
Existing systems fail to effectively leverage the connected nature of customer premises to facilitate emergency response by dynamically provisioning and causing equipment to collect and report context information in anticipation of an emergency event.
Innovation Solution
A cloud-based prediction system predicts an upcoming emergency event and signals customer premises equipment to collect and report context information, such as the number of people present and utility status, by selecting a coordinating device to manage data collection and reporting, even in the event of internet or power loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If context information is collected continuously at customer premises, then emergency response information availability is improved, but device complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-provisioning equipment with instruction sets and collecting context information in advance of predicted emergency events. The cloud-based system predicts emergencies and triggers information collection before the event occurs, ensuring data is ready when needed without requiring continuous monitoring.
Solution Approach 2:
The system dynamically adjusts information collection based on predicted emergency events. Instead of continuous collection, the system activates context information gathering only when a predicted emergency is forecasted, making the collection process adaptive and event-driven rather than static and continuous.
2Device complexity
If context information is collected only when emergencies occur, then device complexity is reduced, but information availability time is worsened
Solution Approach 1:
The cloud-based prediction system forecasts emergency events in advance and triggers context information collection before the emergency occurs. This preliminary action ensures that valuable context data is captured at the moment of emergency rather than lost due to device failure or lack of power during the event.
Solution Approach 2:
The system uses feedback from predicted emergency events to trigger context information collection. When the cloud-based system predicts an emergency, it sends instructions to collect and report context information, creating a feedback loop that ensures information is gathered at the right time based on external predictions.
3Loss of information
If multiple devices collect context information, then information completeness is improved, but device complexity and coordination requirements increase
Solution Approach 1:
The cloud-based prediction system acts as an intermediary that coordinates information collection across multiple devices. It sends instructions to various customer premises equipment, collects the context information from each device, and consolidates the data, eliminating the need for complex peer-to-peer coordination between devices.
Solution Approach 2:
Multiple types of customer premises equipment (routers, set-top boxes, smart home devices) are leveraged to collect different types of context information. Each device uses its existing capabilities to gather relevant data, and the cloud system aggregates this diverse information, making the system universally applicable across different device types without requiring specialized coordination protocols.
4Reliability
If context information collection is activated proactively, then emergency response effectiveness is improved, but energy consumption increases
Solution Approach 1:
The system activates context information collection in advance of predicted emergencies rather than continuously. By using cloud-based predictions to trigger collection only when needed, the system ensures energy is consumed only for meaningful data gathering events, balancing reliability with energy efficiency.
Solution Approach 2:
Instead of continuous operation, the system uses periodic action triggered by predicted emergency events. The cloud-based system forecasts emergencies and activates information collection at specific intervals based on these predictions, reducing overall energy consumption while maintaining emergency response effectiveness.
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.