Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Big Data Analytics for vehicle multisensory anomalies detection

Autonomous driving is assisted by different sensors, each providing information about certain parameters. What we are looking for is an integrated perspective of all these parameters to drive us into better decisions. To achieve this goal, a system that can handle these Big Data issues regarding volume, velocity and variety is needed. This paper aims to design and develop a real-time Big Data Warehouse repository, integrating the data generated by the multiple sensors developed in the context of IVS (In-Vehicle Sensing) systems; the data to be stored in this repository should be merged, which will imply its processing, consolidation and preparation for the analytical mechanisms that will be required. This multisensory fusion is important because it allows the integration of different perspectives in terms of sensor data, since they complement each other. Therefore, it can enrich the entire analysis process at the decision-making level, for instance, understanding what is going on inside the cockpit.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039334; Funding Reference: POCI-01-0247-FEDER-039334]

Processing Big Data Using Secure HDFS

The main objective of this project was to collect the data and provide a solution to the problems faced by a huge organization, which holds the data of many diverse fields. The challenge here was to understand Hadoop and its key features for successful implementation of a Hadoop platform. Users and clients evaluate or analyze the functioning and progress of it. By applying DAIMC methodology, which supports a rapid, iterative development style and better result driven. The team focused on the decision driven as well as data driven. The team also concentrated on the necessities of the decisions to be made, rather than enclosing all existing data. While following this, organization totally relied on agile development and business opportunity management for a successful implementation

Behavior life style analysis for mobile sensory data in cloud computing through MapReduce

Cloud computing has revolutionized healthcare in today's world as it can be seamlessly integrated into a mobile application and sensor devices. The sensory data is then transferred from these devices to the public and private clouds. In this paper, a hybrid and distributed environment is built which is capable of collecting data from the mobile phone application and store it in the cloud. We developed an activity recognition application and transfer the data to the cloud for further processing. Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of user's activities. These activities are visualized to find useful health analytics and trends. In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends