Leader Node Selection in Fog Networks for IoT Prediction

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Solution Overview

Problem

Current communication systems in IoT environments face challenges in efficiently handling predictions due to network latency, data processing bottlenecks, and biased learning models, particularly in decentralized and hierarchical fog networks where leader node selection is crucial for coordinated learning and reduced communication overhead.

Innovation Solution

A method for dynamically selecting leader nodes in a communications system by identifying clusters of nodes based on service request patterns, data variance, similarity, and entropy, and then electing nodes with optimal energy resources, uptime, computing power, and communication constraints to manage machine-learning model updates and predictions across layers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If decentralized learning is implemented in fog networks, then system autonomy and scalability are improved, but network latency and communication overhead increase

Engineering Contradiction:

Improvesystem autonomyVSAvoidnetwork latency

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system segments the fog network into multiple clusters, each with its own leader node that performs local model aggregation. This segmentation allows autonomous decentralized learning at the cluster level while reducing the need for frequent global communications, thereby lowering network latency while maintaining system autonomy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Leader nodes act as intermediaries between member nodes and the central cloud. They perform local federated averaging and coordinate inter-layer communications, which reduces the communication overhead on the global network while maintaining autonomous learning capabilities at the edge.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If leader nodes are selected based on multiple criteria (energy, computing power, uptime), then system reliability and efficiency are improved, but the complexity of node selection increases

Engineering Contradiction:

Improvesystem reliabilityVSAvoidnode selection complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system evaluates potential leader nodes based on multiple dynamic parameters including energy resources, computing power, and uptime. By continuously monitoring and re-evaluating these parameters, the system adapts to changing network conditions and selects the most suitable leader nodes, improving reliability without requiring complex manual configuration.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Nodes autonomously evaluate their own capabilities and participate in the leader selection process based on predefined criteria. This self-service mechanism reduces the need for centralized control and complex selection algorithms, as nodes independently determine their suitability for leadership roles based on their current resource states.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If clusters are formed based on service request patterns and data similarity, then learning accuracy and context-awareness are improved, but the computational overhead for cluster identification increases

Engineering Contradiction:

Improvelearning accuracyVSAvoidcomputational overhead

Core Design Contradiction:

Measurement precisionVSPower

Solution Approach 1:

The system pre-identifies clusters based on service request patterns and data similarity metrics before initiating the federated learning process. By performing this clustering action in advance, the system establishes context-aware groups that improve learning accuracy while avoiding the need for continuous computational analysis during the learning process itself.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20230169356A1Communications System, First Node, Second Node, Third Node and Methods Performed Thereby for Handling a Prediction of an Event

Publication Date: 2023.06.01 TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

AI summary

A method, performed by communications system (100), for a prediction of an event. The first node (111) determines (603) a first set of nodes (121) and sends (604), a first indication of it. The second node (112) determines (606), a vote for one of the nodes in the first set of nodes (121) to be leader, and sends (607) a second indication indicating the determined vote. The second node (112) exchanges (610) a third indication indicating that the third node (113) is the leader. The third node (113) receives (612) from the other nodes in the first set of nodes (121), a respective fourth indication of a respective prediction on the event. The third node (113) updates (613) a machine-learning model of the event based on the received fourth indications and an own prediction, and sends (614), an indication of the updated machine-learning model to another node (114).