A survey on machine learning in Internet of Things: Algorithms, strategies, and applications

  title={A survey on machine learning in Internet of Things: Algorithms, strategies, and applications},
  author={Seifeddine Messaoud and Abbas Bradai and Syed Hashim Raza Bukhari and Pham Tran Anh Quang and Olfa Ben Ahmed and Mohamed Atri},
  journal={Internet Things},

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

This survey provides an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare, and introduces the advantages of using ML.

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

The possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains is discussed, in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machineLearning algorithms.

Secure and Energy Efficient Routing in Wireless Sensor Network using Machine Learning

This paper is mainly emphasizing an energy-efficient and secure routing mechanism that improves the performance of the network and an optimum cluster head that helps to save energy in multi-hop communication.

Detection and segmentation the affected brain using ThingSpeak platform based on IoT cloud analysis

This work proposes a health care system based on medical image analysis processes in the programmable ThingSpeak platform using MATLAB built into the platform within the cloud and MATLAB environment and achieves a match in the analysis processes within the local environment, and ThingSpe speak platform environment by 100%.

Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

A method that uses the CNN-Attention-LSTM model based on federated learning to forecast the multi-energy load of IEMs and shows that federated models can achieve an accuracy comparable to the central model while having a higher precision than individual models, and FedAdagrad has the best prediction performance.

Deep learning-based video quality enhancement for the new versatile video coding

The conventional in-loop filtering in VVC is replaced by the proposed WSE-DCNN model that eliminates the compression artifacts in order to improve visual quality and hence increase the end user QoE.

A Hybrid Multi-objective Algorithm for Imbalanced Controller Placement in Software-Defined Networks

A novel multi-objective version of the Marine Predator Algorithm was introduced and was applied to several real-world software-defined networks and was compared with some state-of-the-art algorithms regarding L C−S, L C −C, Imbalance, SP, and obtained Pareto members, demonstrating the superiority of the proposed controller placement algorithm.

COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis: Deep Learning Approach

This paper presents a deep learning technique-based COVID-19 cough and breath analysis that can recognize positive COvid-19 cases from both negative and healthy CO VID-19 coughing and breath recorded on smartphones or wearable sensors.

Deep reinforcement learning for dynamic scheduling of a flexible job shop

The ability to handle unpredictable dynamic events is becoming more important in pursuing agile and flexible production scheduling. At the same time, the cyber-physical convergence in production



Machine learning and data analytics for the IoT

This paper critically review how IoT-generated data are processed for machine learning analysis and highlights the current challenges in furthering intelligent solutions in the IoT environment and proposes a framework to enable IoT applications to adaptively learn from other IoT applications.

Edge Machine Learning: Enabling Smart Internet of Things Applications

A step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

A thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain and discusses why DL is a promising approach to achieve the desired analytics in these types of data and applications.

Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments).