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17 pages, 19512 KiB

Open AccessArticle

Semantic Reference Model for Individualization of Information Processes in IoT Heterogeneous Environment

by Dmitry Mouromtsev

Electronics 2021, 10(20), 2523; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10202523 - 16 Oct 2021

Cited by 4 | Viewed by 1855

The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and [...] Read more.

The individualization of information processes based on artificial intelligence (AI), especially in the context of industrial tasks, requires new, hybrid approaches to process modeling that take into account the novel methods and technologies both in the field of semantic representation of knowledge and machine learning. The combination of both AI techniques imposes several requirements and restrictions on the types of data and object properties and the structure of ontologies for data and knowledge representation about processes. The conceptual reference model for effective individualization of information processes (IIP CRM) proposed in this work considers these requirements and restrictions. This model is based on such well-known standard upper ontologies as BFO, GFO and MASON. Evaluation of the proposed model is done on a practical use case in the field of precise agriculture where IoT-enabled processes are widely used. It is shown that IIP CRM allows the construction of a knowledge graph about processes that are surrounded by unstructured data in soft and heterogeneous domains. CRM also provides the ability to answer specific questions in the domain using queries written with the CRM vocabulary, which makes it easier to develop applications based on knowledge graphs. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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12 pages, 1857 KiB

Open AccessArticle

Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network

by Sergei Mikhailov and Alexey Kashevnik

Electronics 2021, 10(12), 1390; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121390 - 09 Jun 2021

Cited by 13 | Viewed by 2280

Abstract

COVID-19 has greatly affected the tourist industry and ways of travel. According to the UNTWO predictions, the number of international tourist arrivals will be slowly growing by the end of 2021. One of the ways to keep tourists safe during travel is to [...] Read more.

COVID-19 has greatly affected the tourist industry and ways of travel. According to the UNTWO predictions, the number of international tourist arrivals will be slowly growing by the end of 2021. One of the ways to keep tourists safe during travel is to use a personal car or car-sharing service. The sensor-based information collected from the tourist’s smartphone during the trip allows his/her behaviour analysis. For this purpose, we propose to use the Internet of Things with ambient intelligence technologies, which allows information processing using the surrounding devices. The paper describes a solution to the car tourist trajectory prediction, which has been the demanding subject of different research studies in recent years. We present an approach based on the usage of the bidirectional LSTM neural network model. We show the reference model of the tourist support system for car-based attraction-visiting trips. The sensor data acquisition process and the bidirectional LSTM model construction, training and evaluation are demonstrated. We propose a system architecture that uses the tourist’s smartphone for data acquisition as well as more powerful surrounding devices for information processing. The obtained results can be used for tourist trip behaviour analysis. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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19 pages, 420 KiB

Open AccessArticle

Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories

by Savanna Denega Machado, João Elison da Rosa Tavares, Márcio Garcia Martins, Jorge Luis Victória Barbosa, Gabriel Villarrubia González and Valderi Reis Quietinho Leithardt

Electronics 2021, 10(11), 1260; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111260 - 25 May 2021

Cited by 29 | Viewed by 3847

Abstract

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients [...] Read more.

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s disease (AD). This paper presents an ontology-based computational model that receives physiological data from external IoT applications, allowing identification of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of context histories and context prediction, which, considering the state of the art, is the only one that uses analysis of context histories to perform predictions. In this research, we also propose a simulator to generate activities of the daily life of patients, allowing the creation of data sets. These data sets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1026 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical, and methodological aspects that are recorded in this article. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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26 pages, 3694 KiB

Open AccessArticle

Data Collection Technology for Ambient Intelligence Systems in Internet of Things

by Alexander Vodyaho, Vasiliy Osipov, Nataly Zhukova and Vladimir Chernokulsky

Electronics 2020, 9(11), 1846; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111846 - 04 Nov 2020

Cited by 11 | Viewed by 2529

Abstract

Ambient Intelligence System (AmIS) can be constructed using data collected from Internet of Things (IoT). In this paper, the IoT data collection problem is studied for AmIS with dynamic structure and dynamic behavior of participants (devices), where constraints on resources consumption and performance [...] Read more.

Ambient Intelligence System (AmIS) can be constructed using data collected from Internet of Things (IoT). In this paper, the IoT data collection problem is studied for AmIS with dynamic structure and dynamic behavior of participants (devices), where constraints on resources consumption and performance are essential. A novel technology is proposed, which includes the following steps: (1) definition of the data collection (DC) problem (considering the model of the observed system, DC conditions, etc.); (2) DC policy assignment; (3) construction of DC models; (4) evaluation and presentation of the data processing results. The proposed DC technology supports the development of data collecting subsystems in AmIS. Such subsystems provide data that reflect the changes in structure, state, situation, and behavior of participants in their IoT environment in time. Therefore, we show how this “cognitive” function of the DC process increases the intelligence level of IoT environment. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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18 pages, 598 KiB

Open AccessArticle

Predictors of Ambient Intelligence: An Empirical Study in Enterprises in Slovakia

by Vladimír Bolek and Anita Romanová

Electronics 2020, 9(10), 1655; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9101655 - 11 Oct 2020

Cited by 4 | Viewed by 2209

Abstract

Integration of innovative pervasive technologies into the surrounding environment creates ambient intelligence which has become the subject of several scientific and academic considerations. In particular, the pervasive technology is based on Internet of Things (IoT) and Industrial Internet of Things (IIoT). Ambient intelligence [...] Read more.

Integration of innovative pervasive technologies into the surrounding environment creates ambient intelligence which has become the subject of several scientific and academic considerations. In particular, the pervasive technology is based on Internet of Things (IoT) and Industrial Internet of Things (IIoT). Ambient intelligence may be viewed from several perspectives: a technological, multi-aspect and visionary point of view. Several predictors influence the level of ambient intelligence. The aim is to identify significant predictors influencing ambient intelligence and simultaneously determine their impact on the achieved level of ambient intelligence of the company. In the research methodology, we acknowledge the research presumptions, the research question and research hypotheses, the measurement instrument, obtained data and methods used in accordance with the research model. The scientific article presents the results of an empirical study conducted on 206 enterprises, focusing on the examining of the influence of selected predictors on the level of ambient intelligence in enterprises in the Slovak Republic. It examines not only the influence of selected predictors on the level of ambient intelligence, but also their mutual interaction. We found that these activities even build ambient intelligence on their own, although in interaction with each other, their effect on the level of ambient intelligence begins to fade. Therefore, it is necessary to opt for their combination appropriately. Finally, we present the limits of the research and suggestions on further directions of research in this field. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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19 pages, 3928 KiB

Open AccessArticle

IoT-Oriented Design of an Associative Memory Based on Impulsive Hopfield Neural Network with Rate Coding of LIF Oscillators

by Petr Boriskov

Electronics 2020, 9(9), 1468; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091468 - 08 Sep 2020

Cited by 9 | Viewed by 2684

Abstract

The smart devices in Internet of Things (IoT) need more effective data storage opportunities, as well as support for Artificial Intelligence (AI) methods such as neural networks (NNs). This study presents a design of new associative memory in the form of impulsive Hopfield [...] Read more.

The smart devices in Internet of Things (IoT) need more effective data storage opportunities, as well as support for Artificial Intelligence (AI) methods such as neural networks (NNs). This study presents a design of new associative memory in the form of impulsive Hopfield network based on leaky integrated-and-fire (LIF) RC oscillators with frequency control and hybrid analog–digital coding. Two variants of the network schemes have been developed, where spiking frequencies of oscillators are controlled either by supply currents or by variable resistances. The principle of operation of impulsive networks based on these schemes is presented and the recognition dynamics using simple two-dimensional images in gray gradation as an example is analyzed. A fast digital recognition method is proposed that uses the thresholds of zero crossing of output voltages of neurons. The time scale of this method is compared with the execution time of some network algorithms on IoT devices for moderate data amounts. The proposed Hopfield algorithm uses rate coding to expand the capabilities of neuromorphic engineering, including the design of new hardware circuits of IoT. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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16 pages, 3089 KiB

Open AccessArticle

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

by Andrei Velichko

Electronics 2020, 9(9), 1432; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091432 - 02 Sep 2020

Cited by 33 | Viewed by 6302

Abstract

This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform [...] Read more.

This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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Review

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23 pages, 4662 KiB

Open AccessReview

A Survey on Layer-Wise Security Attacks in IoT: Attacks, Countermeasures, and Open-Issues

by Gaurav Sharma, Stilianos Vidalis, Niharika Anand, Catherine Menon and Somesh Kumar

Electronics 2021, 10(19), 2365; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10192365 - 28 Sep 2021

Cited by 10 | Viewed by 3743

Abstract

Security is a mandatory issue in any network, where sensitive data are transferred safely in the required direction. Wireless sensor networks (WSNs) are the networks formed in hostile areas for different applications. Whatever the application, the WSNs must gather a large amount of [...] Read more.

Security is a mandatory issue in any network, where sensitive data are transferred safely in the required direction. Wireless sensor networks (WSNs) are the networks formed in hostile areas for different applications. Whatever the application, the WSNs must gather a large amount of sensitive data and send them to an authorized body, generally a sink. WSN has integrated with Internet-of-Things (IoT) via internet access in sensor nodes along with internet-connected devices. The data gathered with IoT are enormous, which are eventually collected by WSN over the Internet. Due to several resource constraints, it is challenging to design a secure sensor network, and for a secure IoT it is essential to have a secure WSN. Most of the traditional security techniques do not work well for WSN. The merger of IoT and WSN has opened new challenges in designing a secure network. In this paper, we have discussed the challenges of creating a secure WSN. This research reviews the layer-wise security protocols for WSN and IoT in the literature. There are several issues and challenges for a secure WSN and IoT, which we have addressed in this research. This research pinpoints the new research opportunities in the security issues of both WSN and IoT. This survey climaxes in abstruse psychoanalysis of the network layer attacks. Finally, various attacks on the network using Cooja, a simulator of ContikiOS, are simulated. Full article

(This article belongs to the Special Issue Ambient Intelligence in IoT Environments)

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