scholarly journals Malware threat analysis techniques and approaches for IoT applications: a review

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Chimeleze Collins Uchenna ◽  
Norziana Jamil ◽  
Roslan Ismail ◽  
Lam Kwok Yan ◽  
Mohamad Afendee Mohamed
Keyword(s):  
Smart Devices ◽  
Analysis Techniques ◽  
Threat Analysis ◽  
Iot Applications ◽  
Holistic Approaches ◽  
Business Efficiency ◽  
Application Protocols ◽  
Smart Factories ◽  
Share Information ◽  
Constrained Devices

Internet of things (IoT) is a concept that has been widely used to improve business efficiency and customer’s experience. It involves resource constrained devices connecting to each other with a capability of sending data, and some with receiving data at the same time. The IoT environment enhances user experience by giving room to a large number of smart devices to connect and share information. However, with the sophistication of technology has resulted in IoT applications facing with malware threat. Therefore, it becomes highly imperative to give an understanding of existing state-of-the-art techniques developed to address malware threat in IoT applications. In this paper, we studied extensively the adoption of static, dynamic and hybrid malware analyses in proffering solution to the security problems plaguing different IoT applications. The success of the reviewed analysis techniques were observed through case studies from smart homes, smart factories, smart gadgets and IoT application protocols. This study gives a better understanding of the holistic approaches to malware threats in IoT applications and the way forward for strengthening the protection defense in IoT applications.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

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.

Streamlining Service Platform for Integrating IoT Services

2018 ◽  
pp. 106-138
Keyword(s):  
Web Services ◽  
Impedance Matching ◽  
Data Access ◽  
Service Systems ◽  
Smart Devices ◽  
Service Access ◽  
Service Platform ◽  
Iot Applications ◽  
Distributed Execution ◽  
Event Based

IoT scenarios involve both smart devices hosting web services and very simple devices with external web services. Without unified access to these types of devices, the construction of IoT service systems would be cumbersome. The basic principle of this chapter is the integration of distributed events into SOA. The data access capability of physical entities is first separated from their actuation capability, which acts as a foundation for ultra-scale and elastic IoT applications. Then, a distributed event-based IoT service platform is established to support the creation of IoT services and allow the hiding of service access complexity, where the IoT services are event-driven; the design goals are impedance matching between service computation and event communication. The coordination logic of an IoT service system is extracted as an event composition that supports the distributed execution of the system and offers scalability. Finally, an application is implemented on the platform to demonstrate its effectiveness and applicability.

Bluetooth Low-Energy-Based Applications

2018 ◽  
pp. 95-112
Author(s):  
Smita Sanjay Ambarkar ◽  
Rakhi Dattatraya Akhare
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Smart Devices ◽  
The Internet ◽  
Bluetooth Low Energy ◽  
Iot Applications ◽  
Proximity Detection ◽  
Battery Power ◽  
Commercial Market ◽  
The Internet Of Things

This chapter focuses on the comprehensive contents of various applications and principles related to Bluetooth low energy (BLE). The internet of things (IoT) applications like indoor localization, proximity detection problem by using Bluetooth low energy, and enhancing the sales in the commercial market by using BLE have the same database requirement and common implementation idea. The real-world applications are complex and require intensive computation. These computations should take less time, cost, and battery power. The chapter mainly focuses on the usage of BLE beacons for indoor localization. The motive behind the study of BLE devices is that it is supported by mobile smart devices that augment its application exponentially.

Machine Learning for Industrial IoT Systems

2021 ◽  
pp. 336-358
Author(s):  
Mona Bakri Hassan ◽  
Elmustafa Sayed Ali Ahmed ◽  
Rashid A. Saeed
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Operations Management ◽  
Industrial Applications ◽  
Smart Devices ◽  
Iot Applications ◽  
Industrial Iot ◽  
Effective Decision ◽  
Effective Decision Making

The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.

A Scalable Big Stream Cloud Architecture for the Internet of Things

2015 ◽  
Vol 5 (4) ◽  
pp. 26-53 ◽  
Author(s):  
Laura Belli ◽  
Simone Cirani ◽  
Luca Davoli ◽  
Gianluigi Ferrari ◽  
Lorenzo Melegari ◽  
...  
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Low Latency ◽  
Smart Objects ◽  
Heterogeneous Devices ◽  
Cloud Architecture ◽  
Iot Applications ◽  
Processing Platform ◽  
The Internet Of Things ◽  
Constrained Devices

The Internet of Things (IoT) will consist of billions (50 billions by 2020) of interconnected heterogeneous devices denoted as “Smart Objects:” tiny, constrained devices which are going to be pervasively deployed in several contexts. To meet low-latency requirements, IoT applications must rely on specific architectures designed to handle the gigantic stream of data coming from Smart Objects. This paper propose a novel Cloud architecture for Big Stream applications that can efficiently handle data coming from Smart Objects through a Graph-based processing platform and deliver processed data to consumer applications with low latency. The authors reverse the traditional “Big Data” paradigm, where real-time constraints are not considered, and introduce the new “Big Stream” paradigm, which better fits IoT scenarios. The paper provides a performance evaluation of a practical open-source implementation of the proposed architecture. Other practical aspects, such as security considerations, and possible business oriented exploitation plans are presented.

scholarly journals Cognitive solution for IoT communication technologies – emphasis on 5G

2020 ◽  
Vol 71 (2) ◽  
pp. 131-137
Author(s):  
Anisur Rahman Asif ◽  
Fatema Zahra ◽  
Mohammad Abdul Matin
Keyword(s):  
Internet Of Things ◽  
Traffic Congestion ◽  
Communication Technologies ◽  
Vital Role ◽  
Smart Devices ◽  
Network Technology ◽  
Intelligent Network ◽  
5G Networks ◽  
Cognitive Feature ◽  
Share Information

AbstractThe 5G cellular network technology, in collaboration with Internet of Things (IoT), is envisaged to connect the world together through a flawless Internet connection between devices and sensors. The things in IoT term can be of any smart devices or Internet-enabled sensors that can share information in order to perform a task collectively or individually. On the other hand, Cognitive Internet of Things plays a vital role in utilizing available spectrum for 5G networks. Addition of cognitive feature to the things can allow decision making capability, thereby reducing the overall traffic congestion and improving the efficiency of the whole IoT system. Therefore, it is of great concern to develop an intelligent network to address a few challenges such as heterogeneity and volume of devices in 5G. The aim of this article is to provide a critical review of existing IoT communication Technologies and highlight several challenges and their potential solutions associated with Cognitive IoT based 5G networks.

scholarly journals Information sharing and privacy as a socio-technical phenomenon

2019 ◽  
Vol 28 (3) ◽  
pp. 68-71
Author(s):  
Bernadette Kamleitner ◽  
Mahshid Sotoudeh
Keyword(s):  
Information Sharing ◽  
Data Protection ◽  
Personal Data ◽  
Smart Devices ◽  
Personal Data Protection ◽  
Social Challenge ◽  
The Social ◽  
Wide Range ◽  
Assistant Systems ◽  
Share Information

The present proliferation of portable smart devices and stationary home assistant systems changes the ways in which people share information with each other. Such devices regularly have permission to switch on at any time and can collect a wide range of data in their environment. In consequence, the social challenge of personal data protection is growing and necessitates a better understanding of privacy as an interdependent phenomenon. Interview by Mahshid Sotoudeh (ITA-ÖAW).

scholarly journals Power Consumption prediction of IoT application Protocols Based on Linear Regression

2021 ◽  
Vol 11 (2) ◽  
pp. 0-0
Keyword(s):  
Linear Regression ◽  
Power Consumption ◽  
Smart Grids ◽  
Smart Cities ◽  
Data Communications ◽  
Iot Applications ◽  
Access To Data ◽  
Application Protocols ◽  
Power Consumption Prediction ◽  
Consumption Prediction

The advent of the Internet of Things (IoT) augurs new cutting-edge applications in modern life such as smart cities and smart grids. These applications require protocols more efficient for ensuring the reliability of data communications in the IoT networks. Many works state that IoT cannot meet their demands without application protocols improvement with Artificial Intelligence (AI) as IoT are expected to generate unprecedented traffic giving IoT researchers access to data that can help in studying and analyzing the demands and develop application protocols conceptions to meet the requirement of IoT applications. In literature, several works introduced AI in some layers of the TCP/IP model including wireless communication and routing. In this article, an evaluation of application protocols HTTP, MQTT, DDS, XMPP, AMQP, and CoAP has been presented; and subsequently, the power consumption prediction of MQTT and COAP based on the linear regression model is analyzed, in order to enhance data communications in IoT applications.

Sign in / Sign up

Export Citation Format

Share Document