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

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide 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. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

Download Full-text

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17249347 ◽

2020 ◽

Vol 17 (24) ◽

pp. 9347 ◽

Cited By ~ 1

Author(s):

Md Mamunur Rashid ◽

Joarder Kamruzzaman ◽

Mohammad Mehedi Hassan ◽

Tasadduq Imam ◽

Steven Gordon

Keyword(s):

Machine Learning ◽

Smart City ◽

Service Providers ◽

Detection System ◽

Smart Cities ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Increased Risk ◽

Iot Devices

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

Download Full-text

Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010013 ◽

2021 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Claudia Campolo ◽

Giacomo Genovese ◽

Antonio Iera ◽

Antonella Molinaro

Keyword(s):

Resource Constraints ◽

Smart Cities ◽

Traditional Approach ◽

Low Cost ◽

Iot Applications ◽

Software And Hardware ◽

Iot Devices ◽

Consumer Devices ◽

Hardware Platforms ◽

Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

Download Full-text

Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

Sustainability ◽

10.3390/su13094716 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4716

Author(s):

Moustafa M. Nasralla

Keyword(s):

Machine Learning ◽

Time Series ◽

Virtual Reality ◽

Time Series Analysis ◽

Smart City ◽

Smart Cities ◽

Rehabilitation Services ◽

Simulation Scenario ◽

Common Problems ◽

Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

Download Full-text

Machine learning techniques based on security management in smart cities using robots

Work ◽

10.3233/wor-203423 ◽

2021 ◽

pp. 1-12

Author(s):

Zhang Mengqi ◽

Wang Xi ◽

V.E. Sathishkumar ◽

V. Sivakumar

Keyword(s):

Machine Learning ◽

Information And Communication Technologies ◽

Smart City ◽

Smart Cities ◽

Security Management ◽

Machine Learning Techniques ◽

Quality Of Services ◽

Learning Techniques ◽

Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

Download Full-text

A Survey of Context-Aware Messaging-Addressing for Sustainable Internet of Things (IoT)

Sustainability ◽

10.3390/su12104105 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4105

Author(s):

Alaa Omran Almagrabi ◽

Yasser D. Al-Otaibi

Keyword(s):

Internet Of Things ◽

Smart Cities ◽

Contextual Information ◽

Spatial Models ◽

Context Aware ◽

Engineering Technology ◽

Smart Transportation ◽

Context Aware Computing ◽

Iot Applications ◽

Smart Healthcare

Nowadays, communication engineering technology is merging with the Internet of Things (IoT), which consists of numerous connected devices (referred to as things) around the world. Many researchers have shown significant growth of sensor deployments for multiple smart engineering technologies, such as smart-healthcare, smart-industries, smart-cities, and smart-transportation, etc. In such intelligent engineering technologies, sensors continuously generate a bunch of messages in the network. To enhance the value of the data in the messages, we must know the actuality of the data embedded inside the messages. For this purpose, the contextual information of the data creates a vital challenge. Recently, context-aware computing has emerged to be fruitful in dealing with sensor information. In the ubiquitous computing domain, location is commonly considered one of the most essential sources of context. However, whenever users or applications are concerned with objects, and their site or spatial relationships, location models or spatial models are necessary to form a model of the environment. This paper investigates the area of context-aware messaging and addressing services in diverse IoT applications. The paper examines the notion of context and the use of context within the data exchanged by the sensors in an IoT application for messaging and addressing purposes. Based on the importance and need for context of the information, we identify three critical categories of new IoT applications for context-aware messaging and addressing services: emergency applications, applications for guiding and reminding, and social networking applications. For this purpose, a representative range of systems is reviewed according to the application type, the technology being used, their architecture, the context information, and the services they provide. This survey assists the work of defining an approach for context-aware messaging services domain by discovering the area of context-aware messaging.

Download Full-text

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

Electronics ◽

10.3390/electronics7110309 ◽

2018 ◽

Vol 7 (11) ◽

pp. 309 ◽

Cited By ~ 19

Author(s):

Hind Bangui ◽

Said Rakrak ◽

Said Raghay ◽

Barbora Buhnova

Keyword(s):

Cloud Computing ◽

Leading Edge ◽

Cloud Services ◽

Future Research ◽

Research Directions ◽

Iot Applications ◽

Future Research Directions ◽

Iot Devices ◽

The Internet Of Things

Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood. This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers who might benefit from this transition.

Download Full-text

Automatic Failure Recovery for Container-Based IoT Edge Applications

Electronics ◽

10.3390/electronics10233047 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3047

Author(s):

Kolade Olorunnife ◽

Kevin Lee ◽

Jonathan Kua

Keyword(s):

Internet Of Things ◽

Smart Cities ◽

Smart Homes ◽

Edge Computing ◽

Failure Recovery ◽

Software Failures ◽

Iot Applications ◽

Remote Management ◽

Management Ability ◽

Iot Devices

Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so forth. Newer paradigms such as edge computing are developed to facilitate computation and data intelligence to be performed closer to IoT devices, hence reducing latency for time-sensitive tasks. However, IoT applications are increasingly being deployed in remote and difficult to reach areas for edge computing scenarios. These deployment locations make upgrading application and dealing with software failures difficult. IoT applications are also increasingly being deployed as containers which offer increased remote management ability but are more complex to configure. This paper proposes an approach for effectively managing, updating and re-configuring container-based IoT software as efficiently, scalably and reliably as possible with minimal downtime upon the detection of software failures. The approach is evaluated using docker container-based IoT application deployments in an edge computing scenario.

Download Full-text

Modeling of Trusted Public Emergency Services for Smart Cities Using Blockchain and IoT-based Cognitive Networks

10.21203/rs.3.rs-742472/v1 ◽

2021 ◽

Author(s):

Bhawana Bhawana ◽

Sushil Kumar

Keyword(s):

Smart City ◽

Queuing Theory ◽

Smart Home ◽

Emergency Service ◽

Smart Cities ◽

Distributed Storage ◽

Cognitive Networks ◽

Smart Healthcare ◽

Public Emergency ◽

Iot Devices

Abstract The Internet of Things (IoT) recently gained attention from the last few years due to various smart city applications deployment. The existing literature discusses different public emergency service (PES) aspects from smart-healthcare to smart-home automation. However, less work explores for the smart-fire-brigade system. The PESs require high computation, timely service fulfillment, service transparency, and trust, which are difficult to achieve through a centralized system. In recent years, blockchain technology has gained enormous popularity for immutable data management that ensures transparency, reliability, and data integrity using distributed storage. This paper presents a blockchain based model for secure and trusted public emergency service in IoT-enabled smart cities (BMSTP) to handle the PES requests in real-time fairly. An edge compute server (ECS) is introduced to enhance data processing speed and local data storage. Simultaneously, a queuing theory model is used to process PES requests quickly. The ECS manages an access control list (ACL) for smart-home IoT devices to protect against the illegal placement of any new IoT devices near smart-home to misguiding public emergency service departments (PESDs). Further, a reputation model is designed for PESDs to scale their service quality. We explored the BMSTP for smart-homes placed under different sub-areas of a smart-city. The experiment results show the proposed system model is efficient in scheduling the smart-home PES requests to an appropriate PESD and minimizing the delay to reaching the smart-home location.

Download Full-text

Ciudades inteligentes totalmente integrales, eficientes y sostenibles

10.35985/9789585522510 ◽

2019 ◽

Keyword(s):

Quality Of Life ◽

Energy Consumption ◽

Co2 Emissions ◽

Smart Cities ◽

Research Groups ◽

Innovation And Technology ◽

Strategic Issue ◽

Reduce Energy Consumption ◽

New Challenges

Smart Cities are the result of the increasingly urgent need to orient our lives towards sustainability. Therefore, these cities use infrastructure, innovation and technology to reduce energy consumption and CO2 emissions, in order to improve the quality of life of their citizens. Being a strategic issue that brings new challenges, the organizers request participation in the I IberoAmerican Congress of Smart Cities (ICSC-CITIES 2018), which will be a discussion forum that will create synergies among different research groups to favor the development of Smart Cities, and contribute to their knowledge and integration in different scenarios, their possible development and the strategies to address them.

Download Full-text

Edge Analytics With Machine Learning Technique for Medical IoT Applications

10.4018/978-1-7998-9132-1.ch008 ◽

2022 ◽

pp. 131-142

Author(s):

Jeya Mala D. ◽

Pradeep Reynold A.

Keyword(s):

Machine Learning ◽

Decision Making ◽

Data Analytics ◽

Machine Learning Technique ◽

Iot Applications ◽

Wide Range ◽

Learning Technique ◽

Internal Storage ◽

Iot Devices

Edge analytics are tools and algorithms that are deployed in the internal storage of IoT devices or IoT gateways that collect, process, and analyze the data locally rather than transmitting it to the cloud for analysis. Edge analytics is applied in a wide range of applications in which immediate decision making is required. In the case of general IoT data analytics on the cloud, the data need to be collected from the IoT devices and to be sent to the cloud for further processing and decision making. In life-critical applications such as healthcare, the time taken to send the data to the cloud and then getting back the processed data to take decisions will not be acceptable. Hence, in these kinds of MIoT applications, it is essential to have analytics to be done on the edge in order to avoid such delays. Hence, this chapter is providing an abstract view on the application of machine learning in MIoT so that the data analytics provides fruitful results to the stakeholders.

Download Full-text