Hub-Cloud Platform for Reliable IoT Asset Tracking
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Solution Overview
Problem
Current systems for monitoring objects in geographical areas lack efficient methods to collect and process Internet-of-Things (IoT) data from mobile assets, leading to incomplete or unreliable tracking and monitoring services, especially in areas with unreliable network connectivity.
Innovation Solution
A hub-cloud platform system that collects IoT data from network nodes, processes it using service provisioning data, and provides monitoring services across geographical areas, enabling reliable tracking and monitoring of mobile assets even in areas with limited internet connectivity by using a distributed architecture with local data processing and synchronization with the cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a centralized cloud-based system is used for monitoring mobile assets, then data processing capability is improved, but network dependency and service reliability deteriorate in areas with unreliable connectivity
Solution Approach 1:
The system divides the monitoring functionality into distributed hub devices deployed across multiple geographical locations. Each hub device operates independently to collect and process IoT data locally, eliminating single-point failure and maintaining service continuity even when cloud connectivity is lost.
Solution Approach 2:
Hub devices pre-provision service data and operational parameters before connecting to the cloud. This allows them to continue providing monitoring services using cached service provisioning data even when cloud connectivity is unavailable, ensuring uninterrupted service delivery.
2Area of stationary object
If more network nodes are deployed to improve coverage, then monitoring coverage is improved, but system complexity and infrastructure costs increase
Solution Approach 1:
Each hub device is designed as a multi-functional unit that can serve multiple network nodes and handle various IoT data types. This universal design allows the system to expand coverage by simply adding more identical hub devices rather than creating complex heterogeneous infrastructure.
Solution Approach 2:
The system uses identical replicated hub devices across different locations instead of custom-designed nodes. Each hub device is a copy of the standardized design, simplifying deployment, maintenance, and scaling while extending monitoring coverage to new geographical areas.
3Measurement precision
If continuous cloud synchronization is implemented to maintain data consistency, then data accuracy is improved, but network resource consumption increases
Solution Approach 1:
Instead of continuous synchronization, hub devices perform periodic cloud synchronization at scheduled intervals or when triggered by specific events. This reduces network resource consumption while maintaining data consistency through regular updates rather than constant communication.
Solution Approach 2:
The system implements feedback mechanisms where hub devices synchronize with the cloud based on data change thresholds or event triggers. Synchronization occurs only when necessary to maintain data accuracy, avoiding unnecessary network transactions and reducing resource consumption.
Data Source
AI summary
A method for monitoring an object in a geographical area. The method involves obtaining, from a cloud computing device, service provisioning data for a pre-determined service of the object, receiving, from a first plurality of network nodes disposed about a first segment of the geographical area, a plurality of monitored data items, wherein the plurality of monitored data items are generated based on an Internet-of-things (IoT) signal received by the first plurality of network nodes from a tag sensor disposed on the object, and processing, based on the service provisioning data, the plurality of monitored data items to provide the pre-determined service of the object across the first segment of the geographical area.