Digital twin (DT) is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object (PO) into a software counterpart. The softwarized object, ...termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely augmented and virtual reality (e.g., avatars), multiagent systems, and virtualization. This survey thereby allows for the identification of an extensive set of DT features that point to the "softwarization" of POs. To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT. Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the DT concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT. This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL ...algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander's parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.
Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the ...best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.
Recent advancements in the Internet of Things (IoT) have enabled the development of smart parking systems that use services of third‐party parking recommender system to provide recommendations of ...personalized parking spot to users based on their past experience. However, the indiscriminate sharing of users' data with an untrusted (or semitrusted) parking recommender system may breach the privacy because users' behavior and mobility patterns could be inferred by analyzing their past history. Therefore, in this article, we present two solutions that preserve privacy of users in parking recommender systems while analyzing the past parking history using k‐anonymity (anonymization) and differential privacy (perturbation) techniques. Specifically, given an original parking database containing users' parking information, the k‐anonymity mechanism constructs an anonymized database, while differential privacy perturbs the query response using the Laplace mechanism, making the users indistinguishable in both approaches, hence preserving the privacy. Experimental results on a data set constructed from real parking measurements evaluate the trade‐off between privacy and utility, therefore enabling users to receive parking spots recommendations while preserving their privacy.
This paper preserves the privacy of users while analyzing parking database by parking recommender system using two well‐known privacy‐preserving techniques of k‐anonymity (anonymization) and differential privacy (perturbation). Both techniques make the users indistinguishable in both approaches and preserve the privacy. We performed experiments by constructing a data set from a real parking measurement values and evaluated the trade‐off between privacy and utility.
The Digital Twin (DT) is an emerging approach that promises to change the way products and systems are made and used. The DT is attracting increasing interest in the Internet of Things community for ...its potential applications. Despite the hype, this approach requires a more precise definition and characterization in terms of its properties in relationship to software architectures and their platform implementations, as well as a deeper analysis of its potential applications and actual feasibility in several industries. This paper investigates the basic properties that hold for a DT, sketches a software framework and presents two application scenarios. The paper also addresses the business impact of DT by discussing servitization capabilities.
Proliferation of data sources associated to Internet of Things (IoT) deployment as well as those bound to Open Data Portals (e.g. European Data Portal, Municipalities Open Data Portals, etc.) and ...Social Media platforms is creating an abundance of information that is called to bring benefits for both the private and public sectors, through the development of added-value services, increasing administrations' transparency and availability or fostering efficiency of public services. However, pieces of information without a context are significantly less valuable. Raw data lacks semantics and it is highly heterogeneous from one data-source to another. This poses a challenge to make it useful. To turn all this data into valuable information it is necessary to enable its combination so that meaningful context can be created. Moreover, it is fundamental to define the mechanisms enabling the adoption and orchestration of advanced (typically AI-enabled) data processing techniques to be applied over the harmonized datasets and data-streams. This paper presents the Data Enrichment Toolchain (DET) that provides the necessary harmonization and enrichment to datasets and data-streams coming from heterogeneous sources. The value of the enriched data lies on the one hand in the transfer of the data into a semantically grounded knowledge graph and, on the other hand, in the creation of new data through linking, aggregating and reasoning on the data. In both cases, the benefit of employing linked-data modelling and semantics comes from the extension of the metadata that is associated to every piece of information. Furthermore, the experimental evaluation of the DET implementation that we have carried out is also presented in the paper.
The revolution of Internet of Things (IoT) is pervading many facets of our everyday life. Among the multiple IoT application domains, well-being is becoming one of the popular scenarios in IoT which ...aims to offer new services including smart fitness. This paper focuses on smart fitness covering IoT-based solutions for this domain as well as the impacts of artificial intelligence and social-IoT. IoT-based smart fitness is divided into three categories: Fitness trackers (including wearable and non-wearable sensors), movement analysis and fitness applications. Data collected from IoT-based smart fitness and users could be used for enhancing training performance by Artificial Intelligence (AI)-based algorithms. Sensor to sensor relationship is another notable topic which can be implemented by social-IoT that can share data, information and experiences of users’ training from different places and times. In this his study a comprehensive review on different types of fitness trackers and fitness applications in provided and followed by a review of AI algorithms used in smart fitness scenarios. Lastly detail discussions on the benefits and the potential problems of smart fitness are presented and a shortlist of existing gaps and potential future work have been identified and proposed.
The digital twin is used in several problem domains. It is an enabler for Internet of Things applications as a framework for representing and simulating how physical objects interact in target ...environments. The concept of general-purpose sensing is aiming at determining basic sensing capabilities from which deriving, by means of artificial intelligence algorithms (termed synthetic sensing), relevant information for representing an environment. This article explores the possible relationships and the feasibility of an integration of synthetic sensing within a digital twin framework. This article presents relevant concepts and technologies, challenges and some enabled scenarios that this integration can bring. This approach is in its infancy and there is a need to validate and assess its viability, and benefits. This article identifies challenges and validation steps that can lead to consolidation and adoption of this approach. Finally, this article presents some future work aiming at demonstrating the approach.
Estimating the Quality of Transmission (QoT) of the optical signal from source to destination nodes is the cornerstone of design engineering and service provisioning in optical transport networks. ...Recent studies have turned to Machine Learning (ML) techniques to improve the accuracy of QoT estimation. In this paper, we survey the literature on this topic and classify the studies into categories based on their scope. Accordingly, we distinguish four categories of ML-based solutions: i) check lightpath feasibility, ii) estimate a lightpath's QoT, iii) enhance existing analytical models and iv) improve model generalization. We describe the proposed solutions in each category in terms of ML algorithms, inputs/outputs of the models, source of data and performance evaluation. Deploying a ML-based solution in the real field is not straightforward and presents several challenges. Therefore, we also discuss from an operator's perspective the potential of these solutions for real-field deployment.
PDF
