The implementation of thermally sprayed components in steel manufacturing presents challenges for production and plant maintenance. While enhancing performance through specialized surface properties, these components may encounter difficulties in meeting modified requirements due to standardization in the refurbishment process. This article proposes updating the established coating process for thermally spray coated components for steel manufacturing (TCCSM) by integrating real-time data analytics and predictive quality management. Two essential components--the data aggregator and the quality predictor--are designed through continuous process monitoring and the application of data-driven methodologies to meet the dynamic demands of the evolving steel landscape. The quality predictor is powered by the simple and effective multiple kernel learning strategy with the goal of realizing predictive quality. The data aggregator, designed with sensors, flow meters, and intelligent data processing for the thermal spray coating process, is proposed to facilitate real-time analytics. The performance of this combination was verified using small-scale tests that enabled not only the accurate prediction of coating quality based on the collected data but also proactive notification to the operator as soon as significant deviations are identified.

The battery-less Internet of Things (IoT) devices are a key element in the sustainable green initiative for the next-generation wireless networks. These battery-free devices use the ambient energy, harvested from the environment. The energy harvesting environment is dynamic and causes intermittent task execution. The harvested energy is stored in small capacitors and it is challenging to assure the application task execution. The main goal is to provide a mechanism to aggregate the sensor data and provide a sustainable application support in the distributed battery-less IoT network. We model the distributed IoT network system consisting of many battery-free IoT sensor hardware modules and heterogeneous IoT applications that are being supported in the device-edge-cloud continuum. The applications require sensor data from a distributed set of battery-less hardware modules and there is provision of joint control over the module actuators. We propose an application-aware task and energy manager (ATEM) for the IoT devices and a vector-synchronization based data aggregator (VSDA). The ATEM is supported by device-level federated energy harvesting and system-level energy-aware heterogeneous application management. In our proposed framework the data aggregator forecasts the available power from the ambient energy harvester using long-short-term-memory (LSTM) model and sets the device profile as well as the application task rates accordingly. Our proposed scheme meets the heterogeneous application requirements with negligible overhead; reduces the data loss and packet delay; increases the hardware component availability; and makes the components available sooner as compared to the state-of-the-art.

Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.

In machine learning, classification models need to be trained in order to predict class labels. When the training data contains personal information about individuals, collecting training data becomes difficult due to privacy concerns. Local differential privacy is a definition to measure the individual privacy when there is no trusted data curator. Individuals interact with an untrusted data aggregator who obtains statistical information about the population without learning personal data. In order to train a Naive Bayes classifier in an untrusted setting, we propose to use methods satisfying local differential privacy. Individuals send their perturbed inputs that keep the relationship between the feature values and class labels. The data aggregator estimates all probabilities needed by the Naive Bayes classifier. Then, new instances can be classified based on the estimated probabilities. We propose solutions for both discrete and continuous data. In order to eliminate high amount of noise and decrease communication cost in multi-dimensional data, we propose utilizing dimensionality reduction techniques which can be applied by individuals before perturbing their inputs. Our experimental results show that the accuracy of the Naive Bayes classifier is maintained even when the individual privacy is guaranteed under local differential privacy, and that using dimensionality reduction enhances the accuracy.

In recent years, data has played an increasingly important role in the economy as a good in its own right. In many settings, data aggregators cannot directly verify the quality of the data they purchase, nor the effort exerted by data sources when creating the data. Recent work has explored mechanisms to ensure that the data sources share high quality data with a single data aggregator, addressing the issue of moral hazard. Oftentimes, there is a unique, socially efficient solution. In this paper, we consider data markets where there is more than one data aggregator. Since data can be cheaply reproduced and transmitted once created, data sources may share the same data with more than one aggregator, leading to free-riding between data aggregators. This coupling can lead to non-uniqueness of equilibria and social inefficiency. We examine a particular class of mechanisms that have received study recently in the literature, and we characterize all the generalized Nash equilibria of the resulting data market. We show that, in contrast to the single-aggregator case, there is either infinitely many generalized Nash equilibria or none. We also provide necessary and sufficient conditions for all equilibria to be socially inefficient. In our analysis, we identify the components of these mechanisms which give rise to these undesirable outcomes, showing the need for research into mechanisms for competitive settings with multiple data purchasers and sellers.

On Jan. 13, banks in the European Union will become the leaders of the so-called open banking movement, allowing access to customer account data for any third-party service provider their customers approve via a dedicated communication interface. The move is the fruit of the second Payment Services Directive (PSD2), which the European Commission says will "facilitate innovation, competition and efficiency," give consumers more and better choice in the EU retail payment market, and introduce higher security standards for online payments. In the U.S., however, open banking is largely ad hoc and more of a workaround. A few large banks, such as Wells Fargo and JPMorgan Chase, have made bilateral agreements with data aggregators and accounting software providers. The rest have mostly opted out.

I have just read a new report from McKinsey, The Age of Analytics: Competing in a Data-Driven World (Dec 2016). It weighs in at a mammoth 120 pages, but there are some very interesting nuggets in here. First, McKinsey went back to a previous study they did on data and analytics in 2011 and compared the economic value that they estimated could be realized by the use of data and analytics with the actual results. To get to the point, the results to date are not exactly awe-inspiring. Let's not forget that data and analytics have been a top priority for many organizations for 5 years now according to many industry analysts.

First, McKinsey went back to a previous study they did on data and analytics in 2011 and compared the economic value that they estimated could be realized by the use of data and analytics with the actual results. To get to the point, the results to date are not exactly awe-inspiring. Let's not forget that data and analytics have been a top priority for many organizations for 5 years now according to many industry analysts. So, why did organizations fail to realize the potential in these areas? Organizations will need to build a distinct competence in data management to solve the data challenges while at the same time solving the problem of rapid technology change in analytics and applications.