Advances and practice in Internet of Things

Internet of Things (IoT) services and technologies become more and more predominant in the current and future era of the Internet. Lot of devices and applications are developed and connected to the classical IP networks. As for any new network services testing and evaluation is made in the intranet environment in these days. Neither the efficiency measurement methods of the energy usage nor communication nor sustainability aspects of these services have been determined exactly. Well defined solutions based on statistical analysis are needed to gather practical knowledge about the sensor based network nodes. The errors of the collected data arise in different levels of the process. There are errors during the measurement of a given signal by the sensor, another type of errors is generated during the transmission of collected data through the sensor access network, finally the store process of the received data influenced by errors as well. Evaluation of the hardware and software resource usage by the services and applications are very dissimilar tasks from the classical wired or wireless data network situations in case of IoT. Because of the data streams coming from quite a number of sensors, the statistical analysis of several dozens or hundreds of time series become a complex processing problem. The time series affected by errors generate a pre-processing challenge, which depends on several simultaneous aspects like the measured signal, the applied IoT communication technology and the data storing mechanism. The statistical analysis methods of the time series originated from the preliminary processing differ from the classical data network performance evaluation methods since in the IoT streams are grouped in epoch time periods, for instance. Having huge number of several variables distributed in time and physical space which describes special aspects of the measured and/or controlled system implies complex event processing. This paper presents a case study for th- complex data set which has been captured from 18 TFLOPS capacity supercomputer system with 1.5 thousand CPU cores.