Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks

One of the central problems in massive Internet of Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this work we present a traffic model for IoT devices running quasi-periodic applications and we present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series

Smart grid communication network management with variable communication requirements

Pametna elektroenergetska mreža predstavlja mrežu nove generacije koja treba da bude efikasna, proširiva, pouzdana i jednostavna za upravljanje. Pametnu mrežu karakteriše veliki broj uređaja i dvosmerna komunikacija sa njima. Ovi uređaji će generisati ogromne količine podataka koje je potrebno pročitati i transportovati do kontrolnog centra, za šta je neophodna odgovarajuća komunikaciona infrastruktura koja obezbeđuje adekvatan kvalitet usluge. U ovoj disertaciji je prikazano rešenje za obezbeđivanje kvaliteta usluge sabraćaja sa dinamičkim promenama prioriteta i propusnog opsega bazirano na programabilnim računarskim mrežama. Takođe je razvijena platforma za evaluaciju komunikacione infrastrukture pametnih mreža kako bi se omogućilo jednostavnije emuliranje različitih mrežnih topologija za potrebe razvoja novih algoritama upravljanja. Performanse rešenja su potvrđene putem šest testnih scenarija i pokazano je da predstavljeno rešenje daje bolje rezultate za sve scenarije sa aspekta obezbeđivanja propusnog opsega i mrežnog kašnjenja.Smart grid represents the next generation power network which should be efficient, extensible, reliable and easy to manage. The smart grid will have a great number of devices with two-way communication. These devices will generate large amount of data that needs to be read and transported to utility control center, which further requires adequate communication infrastructure with appropriate quality of service. This dissertation presents a solution for providing quality of service for traffic with dynamic priority and bandwidth requirements, based on software defined networks. The platform for smart grid communication infrastructure evaluation is developed to enable easy emulation of different network topologies for the purpose of developing new control algorithms. Solution performance is verified using six test scenarios and it is shown that the proposed solution gives better results for all scenarios from the aspect of bandwidth provision and network latency