Self-adaptive architectures in IoT systems: a systematic literature review

Jordi Cabot,Iván Alfonso,Kelly Garcés,Harold Castro

doi:10.1186/s13174-021-00145-8

Abstract

Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in bandwidth consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with simultaneous events of different types.In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.

Highlights

The Internet of Things (IoT) represents a global environment that interconnects the internet with a large number ofphysical objects such as sensors, vehicles, cell phones, household appliances, cameras, and machines [1]
Singh and Baranwal [3] classify these into three groups: (1) Quality of Service (QoS) of communication to measure the quality of network services with metrics such as jitter, bandwidth, performance and efficiency, and network connection time; (2) QoS of things with metrics such as availability, reliability, response time, and security; and (3) QoS of computation to measure computational performance with metrics such as scalability, dynamic availability, and response time
3.1.6 Cyber-attacks in IoT applications the security topic was not intentionally addressed in this study, we found the work of Prabavathy et al (S12), which proposes a strategy based on the use of fog computing to detect cyber-attacks

Summary

Introduction

The Internet of Things (IoT) represents a global environment that interconnects the internet with a large number of (cyber-)physical objects such as sensors, vehicles, cell phones, household appliances, cameras, and machines [1]. IoT aims to facilitate communication and exchange of information to enable new forms of interaction between things and people [2]. In most IoT systems, it is critical to guarantee the quality of service (QoS) to the users, according to the requirements of the application domain. Several metrics to measure the quality of service of IoT systems have been proposed. Singh and Baranwal [3] classify these into three groups: (1) QoS of communication to measure the quality of network services with metrics such as jitter, bandwidth, performance and efficiency, and network connection time; (2) QoS of things with metrics such as availability, reliability, response time, and security; and (3) QoS of computation to measure computational performance with metrics such as scalability, dynamic availability, and response time

Objectives

Methods

Findings

Conclusion