Middleware Deployment Planning for Heterogeneous Sensor Networks
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Solution Overview
Problem
In heterogeneous environments, such as smart item systems, the processing and transmission of large amounts of real-time data from sensors are hindered by limited resources, intermittent connectivity, and the need for efficient data processing and storage, leading to potential data loss and inefficiencies in business operations.
Innovation Solution
A middleware system with a request handling layer and a device handling layer is implemented, featuring a distribution manager that determines deployment plans for component services based on resource consumption and connection availability, allowing for pre-processing of data at smart items and efficient data transmission.
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 connectivity
Solution Approach 1:
The patent applies preliminary action by performing data pre-processing, filtering, and aggregation at the smart item edge devices before transmission to backend systems. This prepares data in advance for efficient transmission, reducing the volume of data that needs to be sent over the network and minimizing data loss during intermittent connectivity periods.
Solution Approach 2:
The patent introduces an intermediary layer (edge computing infrastructure) between smart items and backend systems. This intermediary performs data processing, filtering, and buffering operations, mediating the data flow to reduce network bandwidth consumption while ensuring data availability even when connectivity is intermittent.
2Productivity
If more processing power is allocated at smart items for real-time data processing, then data processing efficiency is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the data processing workload into multiple stages distributed across different locations: initial processing at smart items, further processing at edge servers, and final processing at backend systems. This segmentation improves overall processing efficiency while keeping individual device complexity manageable.
Solution Approach 2:
The patent transitions from a single-dimension processing model (all processing at one location) to a multi-dimensional distributed processing architecture. By adding spatial distribution as a new dimension, the system achieves higher processing efficiency without concentrating all complexity in single devices.
3Reliability
If data is stored locally at smart items for later transmission, then data availability is maintained during intermittent connectivity, but storage capacity requirements increase
Solution Approach 1:
The patent extracts only the essential and processed data for local storage at smart items, rather than storing all raw data. By taking out only the necessary information that has been pre-processed and filtered, the system maintains data availability during connectivity interruptions while minimizing storage capacity requirements.
Solution Approach 2:
The patent applies discarding and recovering by selectively discarding redundant or non-critical data at the source before storage, and recovering only essential data for transmission when connectivity is available. This approach maintains reliability while optimizing storage capacity utilization.
4Loss of information
If backend systems process all incoming data, then data processing completeness is improved, but system load and response time worsen
Solution Approach 1:
The patent applies preliminary action by performing data filtering, aggregation, and preprocessing at edge locations before data reaches backend systems. This advance preparation ensures that when data arrives at the backend, it is already organized and ready for final processing, maintaining completeness while significantly reducing response time.
Solution Approach 2:
The patent introduces edge computing intermediaries that handle initial data processing and filtering operations. This intermediary layer protects backend systems from processing raw, unfiltered data, thereby reducing system load and improving response time while maintaining data processing completeness through coordinated multi-level processing.
Data Source
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AI summary
Deployment plans, to service execution environments, of component services associated with a composite service associated with an analysis of data generated by one or more data sources, may be determined, the composite service including an ordering of execution of the associated component services for the analysis of the data. An evaluation of each of the deployment plans of the component services may be determined based on a first metric associating one or more weighted values with a consumption by the each deployment plan of one or more respective resources associated with each of the first and second network nodes and on a second metric associating one or more weighted values with a measure of connection availability of one or more network links included in a communication path between the first and second network nodes. A recommendation including one or more of the deployment plans may be determined based on the evaluation.