Full-Mesh IoT Gateway Cluster Head Selection Algorithm
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Solution Overview
Problem
Current edge computing in full-mesh networks faces challenges in efficiently selecting and utilizing gateways to optimize data transmission and processing, leading to increased latency, power consumption, and network traffic, especially in scenarios without cloud connectivity.
Innovation Solution
A full-mesh algorithm and architecture that dynamically selects a primary gateway based on real-time parameters such as delay, data rate, and network traffic, enabling edge computing to perform data analytics and filtering closer to data sources, reducing the need for cloud-based services and utilizing AI and ML for improved network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transmitted through multiple gateways in a full-mesh network, then network coverage and connectivity are improved, but transmission delay and latency increase
Solution Approach 1:
The patent introduces a cluster head gateway as an intermediary node that consolidates data from multiple member gateways before transmitting to the cloud. This mediator approach reduces the number of hops data must traverse, thereby decreasing transmission delay while maintaining the full-mesh network's connectivity benefits through the cluster structure.
Solution Approach 2:
The network is segmented into clusters with designated cluster head gateways. This segmentation organizes the full-mesh network into manageable groups, allowing data to be aggregated at the cluster head level rather than traversing through multiple individual gateways, thus reducing latency while preserving network reliability.
2Reliability
If data is processed and transmitted through multiple gateways, then network distribution and redundancy are improved, but power consumption increases
Solution Approach 1:
Multiple member gateways merge their data transmissions through a single cluster head gateway. This combining approach maintains the distributed nature of the network for redundancy purposes while significantly reducing the total power consumption by eliminating duplicate transmission paths and consolidating communication through the designated cluster head.
3Ease of operation
If all gateways transmit data directly to the cloud, then data transmission paths are simple, but network traffic and congestion increase
Solution Approach 1:
The patent extracts the data aggregation function from individual gateway-to-cloud transmissions and consolidates it at the cluster head level. Member gateways take out their direct cloud transmission responsibility and instead forward data to their designated cluster head, which then performs centralized aggregation before cloud transmission. This reduces overall network traffic while maintaining operational simplicity through clear hierarchical paths.
4Productivity
If edge computing is implemented at multiple gateways, then local processing capability is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by enabling edge computing capabilities specifically at cluster head gateways rather than uniformly across all gateways. This selective approach provides local processing benefits where most needed (at the aggregation points) while keeping member gateways simpler in design, thus improving productivity without universally increasing device complexity throughout the network.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. In one example, a method includes determining one or more parameters corresponding to each of two or more gateways in a first network, selecting a first gateway of the two or more gateways based at least in part on the one or more parameters, and instructing at least a second gateway of the two or more gateways to send data to the first gateway, wherein the first gateway provides the data to a second network.