Distributed Edge Analytics in Edge-Fog-Cloud Continuum

TL;DR

This paper explores distributed edge analytics across the edge-fog-cloud continuum using three frameworks, demonstrating feasibility through case studies in serverless data pipelines, distributed computing, and federated learning.

Contribution

It introduces three frameworks—MQTT-based SDP, CANTO, and FIDEL—for implementing distributed edge analytics across the continuum, showcasing their effectiveness.

Findings

Distributed edge analytics is feasible across the continuum.

The proposed frameworks enable effective data processing at the edge.

Case studies validate the approaches in real-world scenarios.

Abstract

To address the increased latency, network load and compromised privacy issues associated with the Cloud-centric IoT applications, fog computing has emerged. Fog computing utilizes the proximal computational and storage devices, for sensor data analytics. The edge-fog-cloud continuum thus provides significant edge analytics capabilities for realizing interesting IoT applications. While edge analytics tasks are usually performed on a single node, distributed edge analytics proposes utilizing multiple nodes from the continuum, concurrently. This paper discusses and demonstrates distributed edge analytics from three different perspectives; serverless data pipelines (SDP), distributed computing and edge analytics, and federated learning, with our frameworks, MQTT based SDP, CANTO and FIDEL, respectively. The results produced in the paper, through different case studies, show the feasibility of performing distributed edge analytics following the three approaches, across the continuum.