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Evaluation of a Web Crowd-Sensing IoT Ecosystem Providing Big Data Analysis

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Resource Management for Big Data Platforms

Abstract

In this chapter, we design and develop an IoT ecosystem for pervasive and crowd-sensing computing, which is based on HTML5 APIs. Our platform is interfaced with the real world through the sensors of various IoT devices in order to group and graphically present the retrieved data following statistical processing. The platform consists of two application-specific components: the first, the client part, runs in the user devices in order to collect sensor data and transmit them to the server; the second, the server part, runs in a cloud computing environment and is responsible for processing, analyzing, and visualizing the Big Data collected from all end devices in a human friendly format, e.g., a map. The application is multisensor as it can collect data from almost all sensors of mobile devices and is totally based on HTML5 features. In the first part of the proposed chapter we will present a brief description of the above platform architecture. Then, we will present an evaluation of the platform’s performance under various wireless access networks (e.g., Wi-Fi, 2G, 3G) in terms of latency. Finally, we will use the platform as a tool for benchmarking various database ecosystems in order to find the pros and cons of adopting different database philosophies in such a web crowd-sensing environment. The tests contain basic commands such as insert data, read data, and search data and some more advanced such as data reading with sorting and data aggregation tests. The database implementations we are benchmarking include both NoSQL (Mongo DB, Redis DB) and SQL approaches (MySQL).

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Correspondence to Spyros Panagiotakis .

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Vakintis, I., Panagiotakis, S., Mastorakis, G., Mavromoustakis, C.X. (2016). Evaluation of a Web Crowd-Sensing IoT Ecosystem Providing Big Data Analysis. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-44881-7_22

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