A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems.

Sara Blanc,Néstor X Arreaga-Alvarado,Senén Palanca-Barrio,José-Luis Bayo-Montón

doi:10.3390/s21020672

Sara Blanc, Néstor X Arreaga-Alvarado + Show 2 more

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https://doi.org/10.3390/s21020672

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Abstract

This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request–response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption.

Highlights

The latest innovations in technology and communication allow for flexible adaptation of the Internet of Things (IoT) paradigm to many application areas
Many scenarios could benefit from the advantages of an IoT architecture which includes an edge or fog computing layer able to carry out data storage, control management, decision making, service integration, and intra- and interoperability
complex event processing systems (CEP) concept could be applied on choreography, assuming consumers at the edge level to generate automatic actions, as we propose in this paper

Summary

Introduction

The latest innovations in technology and communication allow for flexible adaptation of the Internet of Things (IoT) paradigm to many application areas. We can find several examples that propose interconnected layers from the sensors to the end user [1,2,3,4,5]. The complexity of these layers depends on the application objectives and magnitude of the observation, the ubiquity of sensors and actuators, and the available infrastructure. Many scenarios could benefit from the advantages of an IoT architecture which includes an edge or fog computing layer able to carry out data storage, control management, decision making, service integration, and intra- and interoperability. The computational capacity of machines is usually between the low capacity of the sensors and the high performance of cloud systems

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