No abstract

Recently, IoT has greatly influenced our daily lives through various applications. One of the most promising application is smart city that leverages IoT devices to manage cities without any human intervention. The high possibility of sensing and publishing sensitive data in this smart environment leads to three significant issues: (1) privacy-preserving (2) heterogeneity, and (3) real-time services. We observe that current studies are in lack of addressing these challenges. In this paper, we propose a new privacy-preserving architecture for IoT devices in the smart city by leveraging ontology, a data model, at the edge of the network. At first, we propose an ontology that consists of privacy information of devices. Then, we mount a real-time privacy-preserving method on top of it that is achieved by providing a dynamic environment from the privacy-preserving point of view. Based on the simulation results using Protege and Visual Studio on a synthetic dataset, we find that our solution provides privacy at real-time while addressing heterogeneity issue so that many IoT devices can afford it. Thus, our proposed solution can be widely used for smart cities.

Blockchain technology (BC) offers an innovation platform for decentralized and transparent transactions in the maritime port industry. This technology allows guaranteeing trust, transparency and traceability of cargo and data to be tracked. Today, in the port systems of emerging market countries, BC technology is increasingly being incorporated into information and communication processes. In parallel, the social domain has begun to be explored due to the lack of link between the port and the city it occupies, and the need to incorporate public actors in decision-making at the governance level. In this sense, the present work aims to promote BC technology in order to transform data and information into useful knowledge for effective decision making, through the use of Crowdsourcing. A Crowdsourcing Blockchain (CrowdBC) conceptual framework and its architecture are generated for a port system in which the cybertechnological, social and cognitive domains (CSTC) of smart ports, the knowledge generation process and Crowdsourcing technology are interrelated. Finally, opportunities are discussed for ports that are in permanent development to reduce the gaps with the smart industry. As a discussion, two possible scenarios and recommendations for future implementations that consider the social and cognitive aspects of Industry 4.0 are presented.

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε, γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.