Prediction of Bus Waiting Time in Smart Cities Using Machine Learning Techniques and the Internet of Things

2020 ◽  
pp. 352-362
Author(s):  
El Arbi Abdellaoui Alaoui ◽  
Stéphane Cédric Koumetio Tekouabou ◽  
Walid Cherif ◽  
Hassan Silkan
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Waiting Time ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Learning Techniques ◽  
The Internet Of Things

scholarly journals Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
Author(s):  
Giuliano Vitali ◽  
Matteo Francia ◽  
Matteo Golfarelli ◽  
Maurizio Canavari
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Management ◽  
Cropping System ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Crop Fields ◽  
Learning Techniques ◽  
The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

scholarly journals Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

2021 ◽  
Vol 11 (6) ◽  
pp. 5066
Author(s):  
Vusi Sithole ◽  
Linda Marshall
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
The Internet ◽  
Use Case ◽  
Object Oriented Design ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
The Internet Of Things

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

scholarly journals Mitigating Malicious Insider Attacks in the Internet of Things using Supervised Machine Learning Techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 13-28
Author(s):  
Mir Shahnawaz Ahmad ◽  
Shahid Mehraj Shah
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Supervised Machine Learning ◽  
Security Requirements ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Prime Field ◽  
Learning Techniques ◽  
The Internet Of Things

The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network.

scholarly journals A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 880
Author(s):  
Imran ◽  
Zeba Ghaffar ◽  
Abdullah Alshahrani ◽  
Muhammad Fayaz ◽  
Ahmed Mohammed Alghamdi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Of Service ◽  
Internet Of Things ◽  
Software Defined Networking ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Resources Management ◽  
Learning Techniques ◽  
The Internet Of Things

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

scholarly journals Social Media Analytics of the Internet of Things

2021 ◽  
Author(s):  
Jim Scheibmeir ◽  
Yashwant K. Malaiya
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Internet Of Things ◽  
Smart Cities ◽  
The Internet ◽  
Social Media Analytics ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Abstract The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This article illuminates the public view of the Internet of Things through a content-based analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a two-week period. Using supervised and unsupervised machine learning methods, we have identified interconnecting relationships between trending themes and the most mentioned industries. We have identified characteristics of language sentiment which can help to predict popularity within the realm of IoT conversation. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current Covid-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms.

scholarly journals Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

2021 ◽  
pp. 30-46
Author(s):  
Saad Hikmat Haji ◽  
Siddeeq Y. Ameen
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
The Internet ◽  
Business Services ◽  
Industrial Services ◽  
The Internet Of Things

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

scholarly journals A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications

2021 ◽  
Vol 3 (3) ◽  
pp. 128-145
Author(s):  
R. Valanarasu
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Smart Transportation ◽  
Iot Applications ◽  
Enhance Efficiency ◽  
The Internet Of Things

Recently, IoT is referred as a descriptive term for the idea that everything in the world should be connected to the internet. Healthcare and social goods, industrial automation, and energy are just a few of the areas where the Internet of Things applications are widely used. Applications are becoming smarter and linked devices are enabling their exploitation in every element of the Internet of Things [IoT]. Machine Learning (ML) methods are used to improve an application's intelligence and capabilities by analysing the large amounts of data. ML and IoT have been used for smart transportation, which has gained the increasing research interest. This research covers a range of Internet of Things (IoT) applications that use suitable machine learning techniques to enhance efficiency and reliability in the intelligent automation sector. Furthermore, this research article examines and identifies various applications such as energy, high-quality sensors associated, and G-map associated appropriate applications for IoT. In addition to that, the proposed research work includes comparisons and tabulations of several different machine learning algorithms for IoT applications.

scholarly journals Machine Learning Applications: The Past and Current Research Trend in Diverse Industries

Inventions ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 8 ◽  
Author(s):  
Omid Ameri Sianaki ◽  
Ashkan Yousefi ◽  
Azadeh Tabesh ◽  
Mehregan Mahdavi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Smart Cities ◽  
Research Trend ◽  
The Internet ◽  
Process Data ◽  
Learning Techniques ◽  
Machine Learning Applications

Dramatic changes in the way we collect and process data has facilitated the emergence of a new era by providing customised services and products precisely based on the needs of clients according to processed big data. It is estimated that the number of connected devices to the internet will pass 35 billion by 2020. Further, there has also been a massive escalation in the amount of data collection tools as Internet of Things devices generate data which has big data characteristics known as five V (volume, velocity, variety, variability and value). This article reviews challenges, opportunities and research trends to address the issues related to the data era in three industries including smart cities, healthcare and transportation. All three of these industries could greatly benefit from machine learning and deep learning techniques on big data collected by the Internet of Things, which is named as the internet of everything to emphasise the role of connected devices for data collection. In the smart grid portion of this paper, the recently developed deep reinforcement learning techniques and their applications in Smart Cities are also presented and reviewed.

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