24th Conference of Open Innovations Association FRUCT, FRUCT 2019
This proceeding includes the papers of the following topics:
Bioinformatics, e-Health and Wellbeing Internet of Things and enabling technologies Smart Spaces, Linked Data and Semantic Web Big Data and Data Mining, Data Storage and Management Knowledge and Data Managements Systems Location Based Services: Navigation, Logistics, e-Tourism Context Awareness and Proactive Services Sensor Design, Ad-hoc and Sensor Networking Natural Language Processing, Speech Technologies Artificial Intelligence, Robotics and Automation Systems Open Source Mobile OS: Architectures and Applications Software Design, Innovative Applications Smart Systems and Embedded Networks Security and Privacy: Applications and Coding Theory Next Generation Networks, Emerging Wireless Technologies, 5G Computer Vision, Image and Video Processing Crowdsourcing and Collective Intelligence IoT based methods for Smart Water Distribution and Management in Agriculture Innovative Drone Enhanced Applications Semantic Audio and the Internet of Things Intelligence, Social Media and Web
The reports were present at the 24th Conference of Open Innovations Association FRUCT held on April 8-12, 2019 in Moscow, Russia.
The smart monitoring system (SMS) vision relies on the use of ICT to efficiently manage and maximize the utility of network infrastructures and services in order to improve the quality of service and network performance. Many aspects of SMS projects are dynamic data driven application system where data from sensors monitoring the system state are used to drive computations that in turn can dynamically adapt and improve the monitoring process as the complex system evolves. In this context, a research and development of new paradigm of Distributed Big Data Driven Framework (DBDF) for monitoring data in mobile network infrastructures entails the ability to dynamically incorporate more accurate information for network monitoring and controlling purposes through obtaining real-time measurements from the base stations, user demands and claims, and other sensors (for weather conditions, etc.). The proposed framework consists of network probes, data parsing application, Message-Oriented Middleware, real-time and offline data models, Big Data storage and Decision layers., and Other data sources. Each Big Data layer might be implemented using comparative analysis of the most effective Big Data solutions. In addition, as a proof of concept, the roaming users detection model was created based on Apache Spark application. The model filters streaming protocols data, deserializes it into Json format and finally sends it to Kafka application. The experiments with the model demonstrated and acknowledged the capacities of the Apache Spark in building foundation for Big Data hub as a basic application for online mobile network data processing.
The paper presents the results of the structural design of the Internet of Things system, allowing to distinguish subsystems for cloud and fog computing to calculate parameters of information flows between them, and to support the structure optimization tasks.