The Cognitive Internet of Things and Big Data

Recently, various systems exhibit similar properties but in different architectures. Therefore, we review the architecture that enhances the processing related to data collection from heterogeneous data sources such as IoT middleware for water management, Dynamic configuration of sensors using mobile sensor hub in internet of things paradigm, and Capturing sensor data from mobile phones using global sensor network middleware. Thus, the tool is capable to be a solution for the heterogeneity issue for data collection. Moreover, in Internet of food and farm 2020, even non-technical users are able to configure dynamic intelligent environment without technical assistance. In Dynamic configuration of sensors using mobile sensor hub in internet of things paradigm, the use of data center HDFS, many-sided frameworks and more flexible set of features facilitates to deal with larger volumes and different heterogeneous data sources. other architectures improve the processing related to ETL and business analysis. the choice of the data store is depending on the selected tool for data ingestion. Hence, moving the data ingestion tool in Big Data and the Internet of Things: Enterprise Information Architecture for a New helps for processing the complex data transformations. In addition, the high-speed links connect to enterprise data through a programmable interface to ease seamless resource management and control physical resources as in Four-Layer Architecture for Product Traceability in Logistic Applications. Furthermore, the architecture of Fog computing: A platform for internet of things and analytics is suitable for projects from small proof of concepts to large application. In Big Data and the Internet of Things: Enterprise Information Architecture for a New and Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater, the time of the data and the real-time consideration for the analysis process is slow because of increasing the amount of data and using complex algorithms. In addition we had an several investigations related to cognitive IoT published by the research community. While these papers define technologies, tools, and platform for different IoT applications and architectures. Most of them let the user have a limited review without analyzing solutions for technical constraints of each architecture. Knowing which techniques and tools can fit well in data flow process is important. Hence, In A survey on Internet of Things architectures, Internet of things: architectures, protocols, and applications, Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges, Security and privacy in the Internet of Things: Current status and open issues, the authors discuss IoT architectures and classify the architectures by their domains. However, the process of data flow, data control and technical constraints have not been discussed.

Fig 1. Comparison for IoT architectures classification.

Other papers such as A survey on internet of things architecture, protocols, possible applications, security, privacy, realworld implementation and future trends,  A survey of Internet-of-Things: Future vision, architecture, challenges and services, A survey on internet of things: Architecture, enabling technologies, security, A survey on trust management for Internet of Things,, Internet of things in industries: A survey, and Security and privacy challenges in industrial internet of things illustrate the services, layers and security requirements of cognitive IoT architectures without defining a deep comparison of other architectures in several domains that implement different frameworks and IoT projects. However, authors of On the security and privacy of Internet and Autonomic and cognitive architectures for the Internet of Things explain frameworks and the usage of them in different IoT solution. Also the, they present IoT projects for different architectures and presents a deep investigation on each security requirement as criteria for each project. Despite a lack of literature related to data flow architecture based cognitive IoT in these survey articles Internet of things: architectures, protocols, and applications, Autonomic and cognitive architectures for the Internet of Things, and A survey of technologies in internet of things, we can reasonably argue that investigation on the process of data flow is well suited for future developments to select the right tools, technologies, and methodologies against constraints. As shown in figure 1, the architectures in these survey articles were classified due to the domains, services, layers, IoT projects, wireless network, framework, and security. The identification of these domains will help in developing IoT solution. Moreover, we study and analyze the existing technologies, tools, and techniques from the related works and several survey papers in order to select the appropriate components for our architecture as can be seen in table 1.

Tablica 1. Mapping between data flow and existing technologies.