Cloud Node Fog Node Placement Optimization
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Solution Overview
Problem
Current technologies face challenges in optimally placing fog nodes in IoT networks, particularly in determining the minimum number of fog nodes and their locations, which is complex due to factors like low latency, geographical distribution, mobility, and heterogeneity, leading to inefficiencies in data processing and communication delays.
Innovation Solution
A method performed by a cloud node that mathematically and graphically determines the optimal placement of sensor nodes and fog nodes, using deep learning and graph creation to minimize the number of fog nodes required, thereby reducing redundant nodes and improving communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more fog nodes are deployed in the IoT network, then data processing capacity and coverage are improved, but system complexity and maintenance burden increase
Solution Approach 1:
The patent applies parameter changes by using deep learning algorithms to dynamically determine optimal fog node placement parameters (locations and quantity) based on network conditions, traffic patterns, and service requirements. This transforms the static deployment problem into a dynamic optimization problem where parameters are adjusted to achieve minimal nodes for maximal efficiency
Solution Approach 2:
The patent replaces traditional mechanical/manual fog node placement methods with intelligent algorithms (deep learning and graph creation). Instead of physically deploying nodes based on heuristic rules, the system uses computational models to automatically determine optimal configurations, substituting algorithmic intelligence for manual planning
2Area of stationary object
If fog nodes are placed to cover all areas, then network coverage is improved, but communication latency increases due to longer data paths
Solution Approach 1:
The patent applies local quality by placing fog nodes at specific strategic locations rather than uniform distribution. The deep learning model identifies high-priority areas where fog nodes provide maximum benefit, creating non-uniform coverage that optimizes for low latency in critical regions while maintaining overall network coverage
Solution Approach 2:
The patent introduces a new dimension of optimization by using graph creation to model network topology and relationships. This graphical representation adds a topological dimension to the placement problem, allowing the system to consider not just geographic coverage but also network path efficiency and data flow patterns
3Ease of manufacture
If traditional placement methods are used, then deployment is simple, but optimal location determination is complex and time-consuming
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine optimal fog node placements without manual intervention. The deep learning model and graph creation algorithm autonomously analyze network requirements and generate deployment configurations, making the complex optimization process transparent and effortless for operators
Data Source
AI summary
The embodiments herein relate to a method performed by a cloud node. The cloud node obtains measurements from at least some of the sensor nodes. The cloud node mathematically determines a minimum number of sensor nodes and their optimal locations. Based on the obtained measurements and the mathematically determined optimal locations, the cloud node graphically determines an optimal location for each of the minimum number of sensor nodes. The cloud node compares the mathematically and the graphically determined optimal locations. When the comparison indicates that the mathematically and graphically determined optimal locations are the same, the cloud node determines a minimum number of fog nodes. Based on the optimal location of sensor nodes, the cloud node determines an optimal location for each of the minimum number of fog nodes.