A Framework for Hybrid Collective Inference in Distributed Sensor Networks

2017-03-01 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

This research introduces a novel hybrid framework for collective inference in distributed sensor networks, integrating peer-to-peer data exchange with centralized cloud aggregation to optimize communication costs while maintaining high classification accuracy. Addressing challenges in IoT, vehicular networking, and UAV swarm coordination, the framework enables dynamic runtime communication strategy decisions, a capability not previously explored in combined cloud/distributed approaches. The authors derive optimal policies for agents implementing this hybrid method and evaluate its performance across various data distribution scenarios. The analysis demonstrates that this approach achieves classification accuracy comparable to centralized joint inference, but with significantly reduced theoretical communication costs, offering potential for efficient real-world applications, including those with complex underlying data distributions.

Key takeaway

For research scientists developing distributed sensor network applications with tight communication and computation constraints, this hybrid inference framework offers a pathway to achieve high classification accuracy with significantly lower communication overhead. You should consider implementing dynamic communication strategies that integrate both peer-to-peer and centralized cloud/edge processing to optimize resource utilization, especially in scenarios like UAV swarms or IoT platforms where battery life and bandwidth are critical.

Key insights

A hybrid cloud and distributed approach dynamically optimizes communication for collective inference in sensor networks.

Principles

• Combine peer exchange with centralized aggregation.
• Dynamic communication strategies reduce cost.
• Maintain high accuracy with reduced communication.

Method

The framework integrates distributed peer-to-peer data sharing with hierarchical cloud/edge computing. Agents make dynamic communication decisions based on cost optimization, allowing for flexible data transfer and classification across constrained devices and centralized services.

In practice

• Apply to UAV swarms for efficient coordination.
• Use in IoT platforms for global classification.
• Implement in Intelligent Transportation systems.

Topics

• Hybrid Collective Inference
• Distributed Sensor Networks
• IoT Applications
• Cloud-Edge Computing
• Dynamic Communication Strategy

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.