IoT Reporting Engine Throttling Cellular Sessions
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Solution Overview
Problem
IoT systems face inefficiencies and high costs due to excessive reporting of known anomalies, overwhelming data centers and incurring significant cellular network charges, while safety and testing requirements prolong software update cycles in the automotive industry.
Innovation Solution
Implementing a reporting engine within IoT devices that maintains a dynamic reporting policy, adjusting the probability of reporting based on the frequency and persistence of anomalies, and using a token bucket mechanism to manage reports, thereby reducing unnecessary cellular communications and prioritizing updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IoT devices continuously report all anomalies to the data center, then anomaly detection reliability is improved, but cellular network charges and data center workload increase significantly
Solution Approach 1:
The reporting policy dynamically adjusts the probability of reporting anomalies based on real-time data center workload conditions. When workload is high, the system lowers reporting probability to reduce cellular charges; when workload is low, it increases reporting probability to maintain detection reliability. This dynamic adaptation resolves the contradiction by making the reporting behavior flexible rather than static.
Solution Approach 2:
The system changes the reporting parameter (probability of reporting) based on workload conditions. By modulating this parameter between 0 and 1, the system can control the balance between sending enough reports to maintain reliability while reducing sufficient reports to lower cellular charges, thus resolving the technical contradiction.
2Speed
If IoT devices report all anomalies immediately, then response time to anomalies is improved, but data center workload increases excessively
Solution Approach 1:
The system dynamically adjusts reporting probability based on data center workload conditions. When workload is high, it reduces reporting probability to prevent excessive workload; when workload is low, it increases reporting probability to maintain fast response. This dynamic behavior resolves the contradiction between speed and productivity.
Solution Approach 2:
The system receives feedback about data center workload conditions and adjusts its reporting behavior accordingly. This feedback mechanism allows the system to maintain appropriate response times while preventing excessive workload by adapting to real-time conditions, thus resolving the contradiction.
3Reliability
If software update cycles are extended to meet safety and testing requirements, then update reliability is improved, but system responsiveness to anomalies decreases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring anomaly patterns and proactively identifying when updates are needed. By analyzing anomaly data in advance and predicting when updates should be deployed, the system can prepare update packages and coordinate deployments more efficiently, reducing the time loss while maintaining safety and testing requirements.
Solution Approach 2:
The system uses feedback from anomaly reporting to trigger and prioritize software updates. When anomaly patterns indicate a need for updates, the system can accelerate the update cycle while still maintaining safety standards, thus reducing the time loss without compromising update reliability.
Data Source
AI summary
In one embodiment, methods, systems, and apparatus are described in which data to be used by a processor is stored in a memory. Network communications with a data center are enabled via a network interface. The processor maintains a reporting policy for reporting anomalous events to the data center, the reporting policy having at least one rule for determining a reporting action to be taken by the processor in response to an anomalous event. The processor further monitors the IoT device for a report of an occurrence of the anomalous event. The processor performs the reporting action according to the at least one rule, in response to the report of the occurrence of the anomalous event. An episodic update to the reporting policy from the data center may be received at the processor, which modifies the reporting policy in accordance with the update. Related methods, systems, and apparatus are also described.