IoT Message Bus Architecture for Scalable Data Processing
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Solution Overview
Problem
Current IoT systems lack scalability and efficiency in hosting applications, failing to effectively integrate and process data from diverse resources and devices in a distributed computing environment, leading to inefficiencies in data interpretation and action initiation.
Innovation Solution
A scalable system architecture that includes a platform gateway module for data reception and message formation, a message collector module for data storage, a tenant handler module for tenant-specific business object creation, and a near real-time processor module for event formation and transmission, enabling contextual data analysis and action initiation based on pre-defined models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from diverse resources is integrated and processed in current IoT systems, then data interpretation capability is improved, but system complexity and processing inefficiency increase
Solution Approach 1:
The patent introduces a message bus as an intermediary component that mediates between diverse data sources and processing applications. The message bus standardizes data flow, providing a unified interface for receiving, routing, and distributing messages from multiple resources. This intermediary layer abstracts the complexity of integrating diverse data sources, allowing applications to process information without directly managing the complexity of multiple input channels.
Solution Approach 2:
The system architecture is segmented into distinct functional modules: resource adapters for data collection, message bus for routing, persistence layer for storage, and analytics engine for processing. Each module handles specific aspects of data flow independently, reducing overall system complexity. The segmentation allows each component to be optimized and maintained separately while working together to achieve comprehensive data interpretation.
2Adaptability or versatility
If scalable system architecture is implemented for IoT applications, then hosting capability is improved, but implementation complexity increases
Solution Approach 1:
The patent implements a universal message bus that handles multiple types of data sources and destinations through a single standardized interface. The same message bus infrastructure supports various resource adapters, persistence mechanisms, and analytics applications simultaneously. This multi-functional design allows the system to host diverse IoT applications without requiring separate specialized infrastructure for each, thereby improving hosting capability while keeping implementation manageable through standardization.
Solution Approach 2:
The system architecture is designed to be dynamic and configurable. Resource adapters, message routing rules, and analytics configurations can be modified at runtime without restructuring the entire system. The message bus dynamically routes messages based on configurable rules, and new resource adapters or applications can be integrated by simply registering them with the existing message bus, enabling scalable hosting capability with manageable implementation complexity.
3Speed
If contextual data analysis is performed in real-time, then decision-making speed is improved, but processing resource consumption increases
Solution Approach 1:
The analytics engine performs contextual analysis selectively based on message priorities and configured rules. Not all messages undergo full contextual analysis - the system applies partial processing to routine messages and reserves comprehensive real-time analysis for high-priority or anomalous data. This partial action approach maintains fast decision-making for critical events while reducing overall processing resource consumption by avoiding unnecessary full-analysis cycles for every message.
Solution Approach 2:
The system performs preliminary filtering and basic processing of messages before they reach the full analytics engine. The message bus pre-routes messages based on simple criteria, and resource adapters perform initial data validation and formatting. This preliminary action reduces the burden on the real-time analytics engine, allowing it to focus computational resources on contextual analysis only for messages that require deep processing, thereby improving decision-making speed for critical events while controlling overall resource consumption.
Data Source
AI summary
A machine implemented method and system, including: receiving at a near real-time processor module, one or more tenant-specific business objects from a message handler module; receiving at the near real-time processor module, contextual data related to the received one or more tenant-specific business objects from a platform analytics module; forming at the near real-time processor module, one or more events by applying one or more pre-defined analytic models to the received contextual data and the received one or more tenant-specific business objects; receiving at a message publisher module, one or more events from the near real-time processor module; and transmitting the received one or more events to one or more subscribers for the one or more events.