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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 42292

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Special Issue Editors

Prof. Dr. Marc Kurz

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Department of Mobility and Energy, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
Interests: mobile software systems; frameworks and architectures; activity and context recognition; Internet of Things; distributed and autonomic computing; adaptive and self-adaptive systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Erik Sonnleitner

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Guest Editor

Department of Mobility & Energy, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
Interests: system security; mobile device security; blockchains and distributed ledger technology; web security; authentication and authorization; information hiding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of mobile systems in recent decades, people are ever increasingly connected to smart devices. These devices aim to make our lives more comfortable and assist in different situations—the most prominent examples for such devices might be the mobile phone, or wearable and ubiquitous systems in general.

By applying approaches that can be classified within the topic “artificial intelligence”, these mobile systems strive to provide some kind of “intelligent behavior”, adapting to the current user’s contextual state. Additionally, security aspects concerning personal and sensitive data are becoming more and more relevant. These two important factors might be diametrically opposed, since “intelligence” usually needs a lot of data to sense the current context of users, but data might be sensitive in terms of privacy and security concerns. Nevertheless, security in mobile systems needs to be considered as a critical factor.

Therefore, the aim of this Special Issue on “Secure and Intelligent Mobile Systems” is to discuss the hybridity of intelligence and security with respect to the (self-)adaptation of mobile systems according to the actual contextual state realizing “intelligent behavior”.

Potential topics of interest for this Special Issue include (but are not limited) the following:

Artificial intelligence;
Ambient intelligence;
Ubiquitous computing;
Pervasive and embedded systems;
Security aspects for mobile systems;
Internet of things (IoT);
Intelligent behavior of mobile systems;
Adaptive and self-adaptive behavior;
Wearable and mobile systems;
Self-adaptation in mobile environments;
Context-awareness and context-aware behavior;
Context-aware intelligent systems;
Adaptive artificial intelligence;
Privacy and security in mobile systems;
Architectural support for security;
Cyber security;
Cryptography;
Frameworks and architectures for intelligent mobile systems;
Architectures for context-aware applications;
Smart sensing networks;
Wearable/body wireless networks;
Mobile big data;
Mobile software systems;
Context- and location-aware services;
Mobile cloud services;
Mobile social media;
Software architecture for mobile applications;
Wrist-worn sensors;
Integrated textile sensors;
Sensing with smartphones and wearables;
Mobile crowd sensing systems;
Wearable sensors, actuators, input/output devices;
Big data and mobile networks.

Prof. Dr. Marc Kurz
Prof. Dr. Erik Sonnleitner
Guest Editors

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Published Papers (10 papers)

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Research

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24 pages, 990 KiB

Open AccessArticle

Continuous Mobile User Authentication Using Combined Biometric Traits

by Dominik Reichinger, Erik Sonnleitner and Marc Kurz

Appl. Sci. 2021, 11(24), 11756; https://doi.org/10.3390/app112411756 - 10 Dec 2021

Cited by 5 | Viewed by 3826

Abstract

Current state of the art authentication systems for mobile devices primarily rely on single point of entry authentication which imposes several flaws. For example, an attacker obtaining an unlocked device can potentially use and exploit it until the screen gets locked again. With continuous mobile user authentication, a system is embedded into the mobile devices, which continuously monitors biometric features of the person using the device, to validate if those monitored inputs match and therefore were made by the previously authenticated user. We start by giving an introduction towards the state of the art of currently used authentication systems and address related problems. For our main contribution we then propose, implement and discuss a continuous user authentication system for the Android ecosystem, which continuously monitors and records touch, accelerometer and timestamp data, and run experiments to gather data from multiple subjects. After feature extraction and normalization, a Hidden Markov Model is employed using an unsupervised learning approach as classifier and integrated into the Android application for further system evaluation and experimentation. The final model achieves an Area Under Curve of up to 100% while maintaining an Equal Error Rate of 1.34%. This is done by combining position and accelerometer data using gestures with at least 50 data points and averaging the prediction result of 25 consecutive gestures. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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30 pages, 11769 KiB

Open AccessArticle

Comparing Human Activity Recognition Models Based on Complexity and Resource Usage

by Simon Angerbauer, Alexander Palmanshofer, Stephan Selinger and Marc Kurz

Appl. Sci. 2021, 11(18), 8473; https://doi.org/10.3390/app11188473 - 13 Sep 2021

Cited by 16 | Viewed by 2908

Abstract

Human Activity Recognition (HAR) is a field with many contrasting application domains, from medical applications to ambient assisted living and sports applications. With ever-changing use cases and devices also comes a need for newer and better HAR approaches. Machine learning has long been one of the predominant techniques to recognize activities from extracted features. With the advent of deep learning techniques that push state of the art results in many different domains like natural language processing or computer vision, researchers have also started to build deep neural nets for HAR. With this increase in complexity, there also comes a necessity to compare the newer approaches to the previous state of the art algorithms. Not everything that is new is also better. Therefore, this paper aims to compare typical machine learning models like a Random Forest (RF) or a Support Vector Machine (SVM) to two commonly used deep neural net architectures, Convolutional Neural Nets (CNNs) and Recurrent Neural Nets (RNNs). Not only in regards to performance but also in regards to the complexity of the models. We measure complexity as the memory consumption, the mean prediction time and the number of trainable parameters of the models. To achieve comparable results, the models are all tested on the same publicly available dataset, the UCI HAR Smartphone dataset. With this combination of prediction performance and model complexity, we look for the models achieving the best possible performance/complexity tradeoff and therefore being the most favourable to be used in an application. According to our findings, the best model for a strictly memory limited use case is the Random Forest with an F1-Score of 88.34%, memory consumption of only 0.1 MB and mean prediction time of 0.22 ms. The overall best model in terms of complexity and performance is the SVM with a linear kernel with an F1-Score of 95.62%, memory consumption of 2 MB and a mean prediction time of 0.47 ms. The two deep neural nets are on par in terms of performance, but their increased complexity makes them less favourable to be used. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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23 pages, 4752 KiB

Open AccessArticle

Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications

by Isidro Calvo, José Miguel Gil-García, Eneko Villar, Aitor Fernández, Javier Velasco, Oscar Barambones, Cristian Napole and Pablo Fernández-Bustamante

Appl. Sci. 2021, 11(17), 8174; https://doi.org/10.3390/app11178174 - 3 Sep 2021

Cited by 3 | Viewed by 2445

Abstract

Wireless technologies are being introduced in industrial applications since they provide certain benefits, such as the flexibility to modify the layout of the nodes, improving connectivity with monitoring and decision nodes, adapting to mobile devices and reducing or eliminating cabling. However, companies are still reluctant to use them in time-critical applications, and consequently, more research is needed in order to be massively deployed in industrial environments. This paper goes in this direction by presenting a novel wireless acquisition system aimed at industrial applications. This system embeds a low-cost technology, such as XBee, not frequently considered for deterministic applications, for deploying industrial applications that must fulfill certain QoS requirements. The use of XBee 900 MHz modules allows for the use of the 2.4 GHz band for other purposes, such as connecting to cloud services, without causing interferences with critical applications. The system implements a time-slotted media access (TDMA) approach with a timely transmission scheduling of the messages on top of the XBee 900 MHz technology. The paper discusses the details of the acquisition system, including the topology, the nodes involved, the so-called coordinator node and smart measuring nodes, and the design of the frames. Smart measuring nodes are implemented by an original PCB which were specifically designed and manufactured. This board eases the connection of the sensors to the acquisition system. Experimental tests were carried out to validate the presented wireless acquisition system. Its applicability is shown in an industrial scenario for monitoring the positioning of an aeronautical reconfigurable tooling prototype. Both wired and wireless technologies were used to compare the variables monitored. The results proved that the followed approach may be an alternative for monitoring big machinery in indoor industrial environments, becoming especially suitable for acquiring values from sensors located in mobile parts or difficult-to-reach places. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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27 pages, 5754 KiB

Open AccessArticle

Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels

by Yung-Yue Chen, Ming-Zhen Ellis-Tiew, Wei-Chun Chen and Chong-Ze Wang

Appl. Sci. 2021, 11(14), 6338; https://doi.org/10.3390/app11146338 - 8 Jul 2021

Cited by 11 | Viewed by 2547

Abstract

In this investigation, a smart collision avoidance control design, which integrates a collision avoidance navigation and a nonlinear optimal control method, is developed for unmanned surface vessels (USVs) under randomly incoming ships and fixed obstacle encounter situations. For achieving collision avoidance navigation, a fuzzy collision risk indicator and a fuzzy collision avoidance acting timing indicator are developed. These two risk indicators can offer effective pre-alarms for making the controlled USVs to perform dodge actions in time when obstacles appear. As to nonlinear optimal control law, it provides a precise trajectory tracking ability for the controlled USVs to follow a collision avoidance trajectory, which is generated via a smart collision avoidance trajectory generator. Finally, a power allocation method is used to transform the desired control law into available actuator outputs to guide the USVs to follow a desired collision avoidance trajectory. From simulation results, the proposed collision avoidance strategy reveals a promising collision avoidance performance and an accurate trajectory tracking ability with respect to fixed objects and randomly moving ships under the effect of environmental ocean disturbances. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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36 pages, 22845 KiB

Open AccessArticle

A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies

by Chin-Ling Chen, Zi-Yi Lim, Hsien-Chou Liao, Yong-Yuan Deng and Peizhi Chen

Appl. Sci. 2021, 11(11), 4939; https://doi.org/10.3390/app11114939 - 27 May 2021

Cited by 13 | Viewed by 4328

Abstract

Tobacco products are an addictive commodity. According to the World Health Organization’s (WHO) latest statistics data, tobacco kills more than eight million people each year. In 2003, the WHO proposed the Framework Convention on Tobacco Control (FCTC) to provide an effective framework for the control of tobacco products to governments around the world. In the field of tobacco products, the hardest problem is how to prevent counterfeit tobacco products and smuggling. To solve the problems, we proposed a blockchain-based traceable and verifiable logistics system for tobacco products with global positioning system (GPS) and radio-frequency identification (RFID) Technologies. In this research, we provide an overview of system architecture, and also define the protocol and the smart contract in every phase that stores data into the blockchain center. We realized a decentralized database and authentication system that uses blockchain and smart contract technology; every protocol in every phase was designed to achieve the integrity of data and non-repudiation of message. Every tobacco product’s shipping record will be completed by scanning the RFID tag and retrieving the GPS with a mobile reader, where the record will be updated and validated in the blockchain center. In the end, the security and costs of the system were analyzed, and a comparison was made with the EU’s (European Commission) method. Our system is more flexible for transportation, more secure in the communication protocol, and more difficult to tamper and forge data. In general, the proposed scheme solved the problem of tobacco products counterfeiting and tracking issues. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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20 pages, 26968 KiB

Open AccessArticle

Undersampled Differential Phase Shift On–Off Keying for Visible Light Vehicle-to-Vehicle Communication

by Michael Plattner and Gerald Ostermayer

Appl. Sci. 2021, 11(5), 2195; https://doi.org/10.3390/app11052195 - 3 Mar 2021

Cited by 7 | Viewed by 2304

Abstract

An important development direction for the future of the automotive industry is connected and cooperative vehicles. Some functionalities in traffic need the cars to communicate with each other. In platooning, multiple cars driving in succession reduce the distances between them to drive in the slipstream of each other to reduce drag, energy consumption, emissions, and the probability of traffic jams. The car in front controls the car behind remotely, so all cars in the platoon can accelerate and decelerate simultaneously. In this paper, a system for vehicle-to-vehicle communication is proposed using modulated taillights for transmission and an off-the-shelf camera with CMOS image sensor for reception. An Undersampled Differential Phase Shift On–Off Keying modulation method is used to transmit data. With a frame sampling rate of 30 FPS and two individually modulated taillights, a raw data transmission rate of up to 60 bits per second is possible. Of course, such a slow communication channel is not applicable for time-sensitive data transmission. However, the big benefit of this system is that the identity of the sender of the message can be verified, because it is visible in the captured camera image. Thus, this channel can be used to establish a secure and fast connection in another channel, e.g., via 5G or 802.11p, by sending a verification key or the fingerprint of a public key. The focus of this paper is to optimize the raw data transmission of the proposed system, to make it applicable in traffic and to reduce the bit error rate. An improved modulation mode with smoother phase shifts is used that can reduce the visible flickering when data is transmitted. By additionally adjusting the pulse width ratio of the modulation signal and by analyzing the impact of synchronization offsets between transmitter and receiver, major improvements of the bit error rate (BER) are possible. In previously published research, such a system without the mentioned adjustments was able to transmit data with a BER of 3.46%. Experiments showed that with those adjustments a BER of 0.48% can be achieved, which means 86% of the bit errors are prevented. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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26 pages, 12753 KiB

Open AccessArticle

Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition

by Marc Kurz, Robert Gstoettner and Erik Sonnleitner

Appl. Sci. 2021, 11(5), 2015; https://doi.org/10.3390/app11052015 - 25 Feb 2021

Cited by 8 | Viewed by 9402

Abstract

Since electronic components are constantly getting smaller and smaller, sensors and logic boards can be fitted into smaller enclosures. This miniaturization lead to the development of smart rings containing motion sensors. These sensors of smart rings can be used to recognize hand/finger gestures enabling natural interaction. Unlike vision-based systems, wearable systems do not require a special infrastructure to operate in. Smart rings are highly mobile and are able to communicate wirelessly with various devices. They could potentially be used as a touchless user interface for countless applications, possibly leading to new developments in many areas of computer science and human–computer interaction. Specifically, the accelerometer and gyroscope sensors of a custom-built smart ring and of a smartwatch are used to train multiple machine learning models. The accuracy of the models is compared to evaluate whether smart rings or smartwatches are better suited for gesture recognition tasks. All the real-time data processing to predict 12 different gesture classes is done on a smartphone, which communicates wirelessly with the smart ring and the smartwatch. The system achieves accuracy scores of up to 98.8%, utilizing different machine learning models. Each machine learning model is trained with multiple different feature vectors in order to find optimal features for the gesture recognition task. A minimum accuracy threshold of 92% was derived from related research, to prove that the proposed system is able to compete with state-of-the-art solutions. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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32 pages, 6280 KiB

Open AccessEditor’s ChoiceArticle

A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support

by Alexandru Predescu, Diana Arsene, Bogdan Pahonțu, Mariana Mocanu and Costin Chiru

Appl. Sci. 2021, 11(4), 1449; https://doi.org/10.3390/app11041449 - 5 Feb 2021

Cited by 25 | Viewed by 4271

Abstract

This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is designed to provide entertainment value by means of gamified interaction with the environment, while the crowdsensing component involves a set of roles for finding, solving and validating water-related issues. The mathematical model of distance-constrained multi-depot vehicle routing problem with heterogeneous fleet capacity is evaluated in the context of the proposed scenario, with random initial conditions given by the location of players, while the Vickrey–Clarke–Groves auction model provides an alternative to the centralized task allocation strategy, subject to the same evaluation method. A blockchain component based on the Hyperledger Fabric architecture provides the level of trust required for achieving overall platform utility for different stakeholders in mobile crowdsensing. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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25 pages, 17726 KiB

Open AccessArticle

Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

by Erik Sonnleitner, Oliver Barth, Alexander Palmanshofer and Marc Kurz

Appl. Sci. 2020, 10(18), 6270; https://doi.org/10.3390/app10186270 - 10 Sep 2020

Cited by 15 | Viewed by 3971

Abstract

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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Review

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39 pages, 646 KiB

Open AccessFeature PaperReview

Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey

by Edward Staddon, Valeria Loscri and Nathalie Mitton

Appl. Sci. 2021, 11(16), 7228; https://doi.org/10.3390/app11167228 - 5 Aug 2021

Cited by 15 | Viewed by 4693

Abstract

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system. Full article

(This article belongs to the Special Issue Secure and Intelligent Mobile Systems)

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