Developing an edge computing platform for real-time descriptive analytics

TL;DR

This paper presents an edge computing platform deployed on transit buses that performs real-time descriptive analytics on transit data streams, aiming to improve transportation management through decentralized processing.

Contribution

The paper introduces a novel mobile edge computing platform for real-time descriptive analytics on transit data streams, demonstrating its practical deployment and evaluation.

Findings

Supports real-time pattern detection from transit data

Enhances decision-making for transit management

Identifies limitations of mobile edge analytics

Abstract

The Internet of Mobile Things encompasses stream data being generated by sensors, network communications that pull and push these data streams, as well as running processing and analytics that can effectively leverage actionable information for transportation planning, management, and business advantage. Edge computing emerges as a new paradigm that decentralizes the communication, computation, control and storage resources from the cloud to the edge of the network. This paper proposes an edge computing platform where mobile edge nodes are physical devices deployed on a transit bus where descriptive analytics is used to uncover meaningful patterns from real-time transit data streams. An application experiment is used to evaluate the advantages and disadvantages of our proposed platform to support descriptive analytics at a mobile edge node and generate actionable information to transit managers.