Middleware Layer for Smart Item Data Distribution
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Solution Overview
Problem
Smart item technologies face challenges in efficiently processing and transmitting large amounts of real-time data from sensors due to limited memory and processing capacity at device level, especially in scenarios with intermittent network connections, leading to data loss and compromised integrity.
Innovation Solution
A system with a middleware layer including a request handling layer and a device handling layer is implemented, which determines optimal distributions of component services across network nodes for data analysis, using weighted resource metrics to evaluate and recommend service execution environments, allowing for pre-processing of data at device and middleware levels before transmitting results to backend systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is transmitted from smart items to backend systems in real-time, then data availability is improved, but network bandwidth consumption increases and data loss occurs due to intermittent connections
Solution Approach 1:
The patent applies preliminary action by performing data pre-processing, filtering, and aggregation at the smart item device level before transmission. This prepares the data in advance, transforming raw sensor data into processed results that can be transmitted efficiently during intermittent network availability, preventing data loss without requiring continuous high-bandwidth transmission.
Solution Approach 2:
The patent segments the data processing function across multiple levels: local pre-processing at smart items, intermediate processing at middleware, and final processing at backend systems. This segmentation allows each layer to handle appropriate data transformations, reducing the overall data transmission burden while maintaining data availability.
2Productivity
If data pre-processing is performed at smart items, then data transmission burden is reduced, but device memory and processing capacity are exceeded
Solution Approach 1:
The patent divides data processing complexity across multiple hierarchical levels: simple filtering at smart items, aggregation at middleware, and analysis at backend systems. This segmentation enables pre-processing to reduce transmission burden without overwhelming individual device capabilities, as each layer handles processing appropriate to its resources.
Solution Approach 2:
The patent introduces middleware as an intermediary layer between smart items and backend systems. This intermediary performs additional processing, aggregation, and buffering, offloading complex processing tasks from resource-constrained smart items while maintaining the benefits of reduced data transmission to backend systems.
3Productivity
If component services are distributed across network nodes, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments processing functions into distributed component services deployed across network nodes at different hierarchical levels. This segmentation improves processing efficiency by enabling parallel processing and localized data handling, while the modular service architecture manages system complexity through standardized interfaces and clear separation of concerns.
Data Source
AI summary
One or more distributions, to service execution environments, of component services associated with a composite service associated with an analysis of data generated by one or more sensors, may be determined, the composite service including an ordering of execution of the associated component services for the analysis of the data, at least one of the service execution environments located at a first network node associated with a device layer and at least one other one of the service execution environments located at a second network node associated with a middleware layer. An evaluation of each of the distributions of the component services may be determined based on a metric associating weighted values with a consumption by each distribution of respective resources associated with each of the first and second network nodes. A recommendation including one or more of the distributions may be determined based on the evaluation.