IoT Data Partitioning via Node Availability and Link Reliability
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Solution Overview
Problem
Conventional data partitioning in IoT networks is ineffective as it solely relies on processing capability, neglecting the dynamic availability and communication link reliability of heterogeneous computing nodes, making it challenging to optimize data processing time and cost, especially for legacy applications and intermittently available devices.
Innovation Solution
The data partitioning is achieved by determining the availability and computational capacity of both computing nodes and communication links, using a data analytics system that dynamically schedules tasks based on these factors to reduce overall processing time and cost, employing a scheme that considers both time reduction and cost reduction, or a combination thereof.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data partitioning is performed based solely on processing capability, then computational tasks can be distributed, but the dynamic availability and communication link reliability of heterogeneous computing nodes are neglected, reducing overall system effectiveness
Solution Approach 1:
The patent changes the partitioning parameters from solely processing capability to a multi-dimensional parameter set including availability status, computational capacity, and communication link reliability. This allows the system to adaptively select computing nodes based on current network conditions and node states, thereby improving both productivity and reliability simultaneously
Solution Approach 2:
The patent introduces dynamic availability assessment and real-time monitoring of computing nodes and communication links. The partitioning scheme is no longer static but adapts to changing network conditions, node availability, and communication reliability, making the system more robust and effective in heterogeneous IoT environments
2Productivity
If conventional data partitioning is used, then data can be distributed for processing, but it fails to optimize data processing time and cost for legacy applications and intermittently available devices
Solution Approach 1:
The patent introduces time and cost as explicit optimization parameters in the partitioning decision-making process. By considering processing time requirements and operational costs alongside computational capacity, the system can prioritize tasks, select appropriate nodes for different data subsets, and minimize both processing time and expenses while maintaining high productivity
Solution Approach 2:
The patent performs preliminary assessment of computing node availability, computational capacity, and communication link reliability before data partitioning. This advance planning allows the system to pre-identify suitable computing nodes and optimize task distribution, reducing actual processing time and avoiding costly reassignments during execution
Data Source
AI summary
A method for data partitioning in an internet-of-things (IoT) network is described. The method includes determining number of computing nodes in the IoT network capable of contributing in processing of a data set. At least one capacity parameter associated with each computing node in the IoT network and each communication link between a computing node and a data analytics system can be ascertained. The capacity parameter can indicate a computational capacity for each computing node and communication capacity for each communication link. An availability status, indicating temporal availability, of each of computing nodes and each communication link is determined. The data set is partitioned into subsets, based on the number of computing nodes, the capacity parameter and the availability status, for parallel processing of the subsets.