Abstract

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.

Highlights

  • Mobile service computing (MSC) is a new paradigm that merges the service computing and mobile computing paradigms

  • After an extensive literature review, we have found no communication model able to support real-time interactions among components of an Internet of Things (IoT) scenario that considers unstable communication links

  • The model presented in this paper is oriented to handling soft real-time quality of service based on MQTT TCP/IP communications in the open Internet

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Summary

Introduction

Mobile service computing (MSC) is a new paradigm that merges the service computing and mobile computing paradigms. Deng et al [1] identify three macro-scenarios (or deployment patterns) to provide and consume software services: cloud-to-mobile (C2M), mobile-to-mobile (M2M), and a combination of the previous ones (hybrid). The services are deployed in the cloud or in servers and consumed by mobile users. The cloud acts as an intermediary that stores and processes data and provides services that allow mobile users both to update and consume such data. Examples of applications that use this deployment pattern are Waze and Foursquare. This pattern is widely used today since it has several benefits, e.g., its simplicity to structure applications, but it requires a stable communication link between the cloud and the mobile units

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