IoT Device Offline Detection Using Linked Hash Map
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Solution Overview
Problem
The existing framework for detecting offline IoT devices in groupings is inefficient due to its computational load, which increases with the number of devices and inversely with the check interval, leading to scalability and efficiency issues.
Innovation Solution
Implementing a linked hash map data structure for managing heartbeat messages, allowing for O(1) time complexity for insertion, deletion, and lookup, and maintaining insertion order, which reduces the computational cost of offline detection to be proportional to the number of groupings rather than individual devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a table data structure is maintained with an entry for each IoT device to track heartbeat messages, then the management service can detect offline devices, but the computational load increases linearly with the number of devices
Solution Approach 1:
The patent segments the device tracking system by organizing devices into groupings rather than maintaining individual device entries. The management service traverses groupings and identifies offline devices within each grouping, reducing the traversal scope from all individual devices to smaller grouping subsets.
Solution Approach 2:
The patent introduces a new organizational dimension by creating groupings that categorize devices based on shared characteristics (location, function, ownership). This adds a hierarchical layer between individual devices and the management service, allowing efficient offline detection through grouping-level traversal.
2Measurement precision
If the management service checks every device in a grouping periodically to detect offline status, then accurate offline detection is achieved, but the time required increases with the number of devices
Solution Approach 1:
The patent divides the device population into smaller grouping segments, allowing the management service to check offline status within each grouping separately. This segmentation reduces the time required for each check while maintaining comprehensive coverage of all devices.
Solution Approach 2:
The patent performs preliminary organization of devices into groupings based on shared characteristics before offline detection. This preliminary action enables the management service to quickly identify which groupings to check and reduces the scope of each offline status verification.
3Reliability
If individual device tracking is implemented for all IoT devices, then complete device monitoring is achieved, but scalability becomes problematic with large device numbers
Solution Approach 1:
The patent segments the monitoring system into grouping-level and device-level components. The management service monitors groupings rather than individual devices directly, reducing system complexity while maintaining complete device monitoring through the grouping hierarchy.
Solution Approach 2:
The patent creates groupings that serve multiple functions: organizing devices by shared characteristics, enabling efficient offline detection, and providing a scalable structure for large device populations. This universal grouping mechanism addresses multiple system requirements simultaneously.
Data Source
AI summary
Disclosed are various examples for offline detection of Internet of Things (IoT) devices or any type of devices that report a heartbeat or status indication. A linked hash map be utilized as a data structure in which heartbeat message are stored. The oldest heartbeat message is stored at the head of the data structure so that only the head of the data structure requires checking if the oldest node is designated as online.