Algorithms for Big Data Delivery over the Internet of Things 19th IEEE Conference on Business Informatics Doctoral Consortium Algorithms for Big Data Delivery over the Internet of Things Andreas P. Plageras Kostas E. Psannis

Purpose & Goals Improve healthcare more accessible & faster Sensors, actuators, cameras, etc. Monitor patient’s health Provide appropriate care Aim: Propose suitable algorithms for efficient data delivery Solve management issues Analyze the IoT-health big data Provide security solutions

Contents Introduction Challenges & Issues Methodology (IoT, BD, CC, WSNs) Challenges & Issues Methodology Proposed Hybrid Network Topology Layers & Protocols used Contiki OS & Cooja Emulator

Introduction Enormous amounts of data are growing because of the continuous increasing use of smart devices. Connect, collect, exchange, & transfer. With the convergence of new technologies many sectors are benefited. IoT , Cloud, WSNs, technologies related to Big Data. By 2030 one trillion devices will be connected. There is urgent need for adoption of big data (big data) & IoT applications. These technologies are interdependent and should be developed jointly. The new trend: is accompanied by countless development & improvement expectations in all areas. comes along with concerns about security & violation of privacy.

Challenges & Issues Different types of data bring enormous challenges for: the acquisition, larger storage spaces, such as cloud, mining big data, the management, the data analysis of large amounts of data (big data analytics), speed and quality of data, integration of different data types. Issues: fundamental problems of large data (definitions and models), the standardization of large amounts of data, the development of computational methods of large data, protection of the personal data, privacy, the user interaction by using feedback/guidance allowed during data mining.

Hadoop, Map Reduce, HDFS Hadoop is a free framework in a distributed computing environment supports the processing of large data, increases the data transfer rate, & is fault-tolerant, scalable and flexible. The Hadoop reduction map (Hadoop map reduce) is a frame used for processing and generating large data sets with a parallel and distributed algorithm in a cluster. The HDFS (Hadoop Distributed File System) is a file system used by all nodes in a Hadoop cluster for storing data.

Methodology Mastery of Big Data technologies, IoT, CC, WSNs, etc. Aim: The healthcare sector can be benefited by these technologies in combination with other security mechanisms, protocols, and algorithms. Aim: a convergence of technologies, protocols, mechanisms, & algorithms, to provide better solutions. Contiki OS & Cooja emulator: The Contiki is designed to support IP networking in low-power network connections. It is implemented in C. In the Cooja emulator we can simulate WSNs. It is a flexible java simulator that supports C for application software development. With such tools we can simulate in real time our sensor networks.

Proposed Hybrid Network Topology

Technologies & Protocols Used Internet of Things (IoT): Communication between physical & cyber objects. Big Data (BD): IoT & BD are interdependent technologies & should be developed jointly. Cloud Computing (CC): Accuracy, Scalability, Flexibility, Efficiency, Reliability, Low-cost, Storage spaces. Wireless Sensor Networks (WSNs): The purpose of these networks is the production of big data. IPv6 over Low-power Wireless Personal Area Network (6LoWPAN): Reduces header overheads, not using DHCP, Supports multi-casting. IEEE 802.15.4 Standard: Lower data rate, Lower power, Lower cost. Constrained Application Protocol (CoAP): It supports devices that operate primarily on battery. Compresses the HTTP data to send less data through the wireless link. Provides strong security

Layers & Protocols Used The layers and the protocols can be used in each layer. Layers Sub-layers Protocols Physical - IEEE 802.15.4 Data Link MAC   6LoWPAN Network IPv6/HTTP Transport CoAP/UDP/TCP Application CoAP

Contiki OS & Cooja Emulator

Transmission in our network

The packets received & lost (time)

Conclusions Wide range of research in the field of IoT, CC, & other technologies of effective healthcare systems. Challenges & issues of the IoT-Big data. Technologies, communication protocols, security mechanisms, & other techniques involved in our research. Limitations of the research: Limited Energy Privacy Future Work: Investigate new communication protocols, algorithmic solutions for delivery of large amounts of e-health data, security solutions, & efficient systems. Develop new innovative ideas & applications.

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