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

  • Ioannis Vakintis
  • Spyros PanagiotakisEmail author
  • George Mastorakis
  • Constandinos X. Mavromoustakis
Part of the Computer Communications and Networks book series (CCN)


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).


Sensor Data Proxy Server NoSQL Database Collect Sensor Data Client Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ioannis Vakintis
    • 1
  • Spyros Panagiotakis
    • 1
    Email author
  • George Mastorakis
    • 1
  • Constandinos X. Mavromoustakis
    • 2
  1. 1.Department of Informatics EngineeringTechnological Educational Institute of CreteHeraklionGreece
  2. 2.Department of Computer ScienceUniversity of NicosiaNicosiaCyprus

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