An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

TL;DR

This paper introduces an edge-based framework for context-aware ambience intelligence tasks in IoP environments, emphasizing energy efficiency, dynamic sensor integration, and high performance for vision tasks.

Contribution

It proposes a novel architecture with automated task planning, sensor discovery, and dynamic environment support, validated by a prototype and experimental evaluation.

Findings

Efficient sensor and service discovery at the edge

Successful execution of vision tasks with good performance

Framework supports dynamic addition of sensors and services

Abstract

In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energy-efficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment.