Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper.

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

Sentiment analysis, also known as opinion mining, plays a big role in both private and public sector Business Intelligence (BI); it attempts to improve public and customer experience. Nevertheless, de-identified sentiment scores from public social media posts can compromise individual privacy due to their vulnerability to record linkage attacks. Established privacy-preserving methods like k-anonymity, l-diversity and tcloseness are offline models exclusively designed for data at rest. Recently, a number of online anonymization algorithms (CASTLE, SKY, SWAF) have been proposed to complement the functional requirements of streaming applications, but without open-source implementation. In this paper, we present a reusable Apache NiFi dataflow that buffers tweets from multiple edge devices and performs anonymized sentiment analysis in real-time, using randomization. The solution can be easily adapted to suit different scenarios, enabling researchers to deploy custom anonymization algorithms.

Low grade sulfide ores are difficult to process due to their composite mineralogy and their fine grained dissemination with gangue minerals. Therefore, fine grinding of such ores becomes essential to liberate valuable minerals. In this research, selective flotation was carried out using two pitched blade turbine impellers with diameters of 6 cm and 7 cm to float copper and nickel. The main focus of this research was to generate optimum hydrodynamic conditions that can effectively separate nickel and copper from gangue minerals. In addition, we investigated the effects of superficial gas velocity, impeller speed, bubble size distribution, and bubble surface area flux on the flotation recovery and rate constant. The results demonstrated that a 7 cm impeller comparatively produced optimum hydrodynamic conditions that improved Cu-Ni recovery and the rate constant. The maximum copper and nickel recoveries in the 7 cm impeller tests were observed at 93.1% and 72.5%, respectively. However, a significant decrease in the flotation rate of nickel was observed, due to entrainment of nickel in copper concentrate and the slime coating of gangue minerals on the nickel particle surfaces.

Machine control systems are advancing side by side with the adoption of 5G and growing trends of the internet of things (IoT) has made autonomous excavators more ubiquitous. The autonomous excavators have gained significant interest in the earthworks area, due to their enhanced productivity for long hours, safety, space exploration, mining and construction work. However, A great amount of effort is required to address many existing challenges such as adaptive movement and control, task planning (digging, moving debris, etc.), collaborative work with other machines and humans. In this study, we review the state of the art and provide artificial intelligence (AI-) driven road map for implementing a complete autonomous framework for the earth-moving machine to our test platform 'Smart Excavator'. Furthermore, the challenges and required effort to implement the framework are also discussed in comparison with existing literature.