# Self-adaptive Architectures in IoT Systems: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2109.03312 · HTML: https://mlsanity.com/paper/2109.03312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [A User Study on Explainable Online Reinforcement Learning for Adaptive Systems](https://mlsanity.com/paper/2307.04098/) (2023)

## Abstract (arXiv)

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.