Wireless Mesh Network Deployment Service Using Environmental Simulation

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Solution Overview

Problem

Deploying a wireless mesh network that is optimized for a specific environment and customer is challenging due to issues like channel interference, inefficient node utilization, and the difficulty in forming a proper mesh topology, especially since current standards like Wi-SUN do not cover field deployments and runtime environments vary.

Innovation Solution

A method and system that identify a geographical area for deployment, collect environmental information, and define network characteristics for an optimum deployment scheme, using techniques like Monte Carlo simulations to determine the best deployment scheme based on environmental and network characteristics, ensuring efficient node placement and topology formation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If a wireless mesh network is deployed without environmental analysis and simulation, then deployment speed is improved, but network performance and reliability deteriorate due to channel interference and improper topology formation

Engineering Contradiction:

Improvedeployment timeVSAvoidnetwork performance

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system performs preliminary environmental information collection, Monte Carlo simulations, and deployment scheme identification before actual network deployment. This advance planning allows optimization of node placement and topology formation, ensuring reliable network performance while maintaining efficient deployment timelines.

Inventive Principle:

Principle #10Preliminary action

2Area of stationary object

If more nodes are deployed to ensure network coverage, then network coverage is improved, but cost and node redundancy increase

Engineering Contradiction:

Improvenetwork coverage areaVSAvoidnumber of nodes

Core Design Contradiction:

Area of stationary objectVSQuantity of substance

Solution Approach 1:

The system uses Monte Carlo simulations to analyze various deployment parameters including node density, transmission power, and spatial distribution. By optimizing these parameters, the system determines the minimum number of nodes required to achieve complete area coverage, eliminating unnecessary nodes while ensuring full network coverage.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system creates virtual copies of the wireless network environment through Monte Carlo simulations, allowing multiple deployment scenarios to be tested and compared without deploying physical nodes. This enables identification of the optimal node configuration that achieves coverage with minimum nodes.

Inventive Principle:

Principle #26Copying

3Ease of operation

If deployment is performed without environmental information collection, then deployment simplicity is improved, but deployment accuracy and optimization deteriorate

Engineering Contradiction:

Improvedeployment simplicityVSAvoiddeployment accuracy

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The system automatically collects environmental information including geographical data, existing wireless infrastructure, and physical obstacles. This self-service approach eliminates manual site surveys while providing accurate environmental data that enables precise deployment scheme identification and optimization.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system introduces an intermediary deployment planning layer that processes environmental information and generates optimized deployment schemes. This intermediary layer translates complex environmental data into actionable deployment instructions, maintaining simplicity for end users while achieving high deployment accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Adaptability or versatility

If standard Wi-SUN protocols are used without field deployment coverage, then standardization compliance is improved, but adaptability to specific environments deteriorates

Engineering Contradiction:

Improveenvironmental adaptabilityVSAvoiddeployment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system tailors deployment schemes to specific local environmental conditions by analyzing geographical features, existing infrastructure, and physical obstacles. This local customization approach adapts standard Wi-SUN protocols to specific environments, ensuring optimal performance while maintaining protocol compliance without requiring complex custom hardware or protocols.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS11653220B2Cloud-based deployment service in low-power and lossy network

Publication Date: 2023.05.16 CISCO TECHNOLOGY INC

AI summary

Systems, methods, and computer-readable media for identifying a deployment scheme for forming a wireless mesh network based on environmental characteristics and an optimum deployment scheme. In some examples, a geographical area for deployment of a wireless mesh network is identified. Additionally, environmental information of the geographical area can be collected. Network characteristics of an optimum deployment scheme for forming the wireless mesh network can be defined. As follows, a deployment scheme for forming the wireless mesh network can be identified based on the network characteristics of the optimum deployment scheme and the environmental information of the geographical area.