Fog Computing Resource Allocation for Healthcare Data Security
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Solution Overview
Problem
The healthcare industry faces challenges in securely managing and processing healthcare data due to inadequate data security and increased risks of accidents related to complex medical systems, with existing technologies failing to effectively utilize IoT devices efficiently and securely.
Innovation Solution
A fog computing system that includes an application manager, resource utilization predictor, and resource manager to dynamically allocate computing resources and schedule fog applications, using convolution neural networks for predicting availability and executing service requests, while ensuring data security and reducing unsecured device usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fog computing is used to process healthcare data closer to IoT devices, then data security and processing efficiency are improved, but system complexity increases
Solution Approach 1:
The system segments the healthcare network into multiple hierarchical layers including IoT devices, fog computing nodes, intermediate computing nodes, and cloud data centers. Each layer performs specific functions - IoT devices collect data, fog nodes provide edge processing and initial security validation, intermediate nodes handle aggregation and coordination, and cloud centers perform comprehensive analysis. This segmentation improves data security by enabling localized processing and reduces transmission risks while managing complexity through clear functional boundaries at each segment.
Solution Approach 2:
The patent introduces intermediate computing nodes as mediators between fog computing nodes and cloud data centers. These intermediate nodes aggregate service requests from multiple fog nodes, perform preliminary processing, and coordinate resource allocation. This intermediary layer simplifies the overall system by reducing direct communication overhead between fog nodes and cloud centers, while enhancing security through additional validation layers and centralized resource management.
2Productivity
If more fog computing nodes are deployed to handle increased service requests, then processing capacity and user experience are improved, but resource management complexity and costs increase
Solution Approach 1:
The system implements dynamic resource allocation where fog computing nodes and intermediate computing nodes can be activated or deactivated based on real-time service request volumes and availability predictions. The resource manager continuously monitors system state and dynamically adjusts the number and location of active computing nodes. This dynamic approach allows the system to scale processing capacity up or down as needed, improving productivity during peak demand while reducing resource management complexity during low-demand periods.
Solution Approach 2:
The patent incorporates a feedback mechanism where the resource manager receives information about service request patterns, node availability, and processing performance. This feedback is used to predict future resource needs and proactively adjust resource allocation. The convolutional neural network analyzes historical data and current system state to forecast service request volumes, enabling the system to pre-position computing resources before demand peaks, thereby improving processing capacity while avoiding over-provisioning and reducing management complexity.
3Measurement precision
If convolutional neural networks are used to predict node availability, then resource allocation accuracy is improved, but computational overhead and energy consumption increase
Solution Approach 1:
The system segments the prediction task across multiple hierarchical levels. Convolutional neural networks are deployed only at intermediate computing nodes rather than at every fog computing node or IoT device. Each intermediate node predicts the availability of its associated fog nodes based on local observations and historical data. This segmentation reduces the overall computational burden and energy consumption while maintaining prediction accuracy, as the neural networks process smaller, localized datasets rather than global system state.
Solution Approach 2:
The patent implements local quality by having each intermediate computing node perform predictions and resource allocation decisions for its specific subset of fog nodes and service requests. Rather than using a centralized system that processes all data globally, each intermediate node maintains local knowledge about its neighboring fog nodes' performance patterns, hardware characteristics, and current workload. This local approach reduces computational overhead and energy consumption at each node while collectively achieving system-wide prediction accuracy through the aggregation of multiple local predictions.
Data Source
AI summary
A system and computer-implemented method for managing a smart devices network using fog computing is provided. The system comprises an application manager configured to receive service requests from devices in a smart devices network and collect data related to fog computing nodes and intermediate computing nodes and a resource utilization predictor configured to predict availability of the fog computing nodes and the intermediate computing nodes. Furthermore, the system comprises a resource manager configured to dynamically allocate at least one of: a specific fog computing node and a specific intermediate computing node, schedule triggering of fog applications based on the predicted availability, trigger, at the specific fog computing node and the specific intermediate computing node, the fog applications for executing the received service requests corresponding to the devices and perform actions corresponding to the executed one or more service requests.