Machine Learning for Enabling ActiveMeasurements in IoT Environments

With the explosion of Internet of Things (IoT) technology, network operators tryto provide more and more new services related to IoT. For traditional services,operators are accustomed to using IP-layer active measurements for assessingend-to-end network performance to ensure the quality of service (QoS). Similarly,they also want to use the same methods to assess end-to-end networkperformance in IoT systems. However, due to the resource-constrained IoTenvironment, intrusive active measurements may induce energy and networkoverhead, which are sensitive topics for IoT applications.The thesis investigates a new approach where network performance metrics,such as packet loss and round-trip time, are predicted from network and environmentalfeatures, such as topology information, packet statistics, and environmentalsensing features. The overarching goal is to lower the impact ofactive measurements adjusting into resource-constrained IoT systems. The predictionfunctionality is based upon supervised machine learning algorithms. Inthis thesis, we discuss how this functionality can be implemented as part of theIoT network management system with an active measurement proxy.Evaluation of the predictive functionality is based on extensive experimentationon the EWSN'17 testbed. Probe packets are sent periodically to estimate networkperformance metrics. Simultaneously, device and infrastructure metricsare collected from each network node. After collecting the data, an ML (Machinelearning) pipeline is used to learn the relation between accessible featuresand target service metrics. To predict service-level metrics, the thesis specificallyevaluates two prediction models based on statistical learning methodsincluding linear and tree-based regression algorithms.The results for dierent scenarios and topologies show that the new approach canaccurately estimate the service-level network performance metrics for wirelesssensor networks with error rates lower than 10% for RTT and 16% for 20minaverage loss (NMAE).Med den snabba utvecklingen av Internet of Things (IoT) s tillhandahller ntoperatrernaallt er nya tjnster inom IoT. Fr traditionella ntverkstjnster r operatrervana vid att anvnda aktiva mtningar p IP-lagret fr att bedma ntverkets end-toendprestanda och p s stt skerstlla kvalitn p tjnsten. Med liknande metodik villde ocks mta och bedma ntverksprestanda i IoT-system. Aktiva mtningar kandock negativt pverka energi- och ntverksbelastningen, vilka r kritiska resurser frIoT-applikationer.Den hr uppsatsen undersker ett nytt tillvgagngsstt fr att mta ntverksprestandadr underliggande statistik, ssom topologiinformation, radio-, och paketinformation,anvnds fr att frutse olika mtt, ssom paketfrluster och frdrjningar. Prediktionsfunktionalitetenr baserad p Machine Learning. I denna uppsats diskuterasven hur denna funktionalitet kan implementeras som en del av IoT proxy frntverkshanteringssystem.Utvrdering av frutsgningsfunktionaliteten baseras p omfattande experiment iEWSN'17 testmiljn. Aktiva mtningar genomfrs genom att periodiskt skicka probepaket fr att uppskatta ntverksprestanda. Samtidigt samlas mtdata in frn varjentverksnod. Efter att ha samlat in data, s anvndes en ML-pipeline byggd fr attlra sig frhllandet mellan mtdata frn noderna och ntverksprestanda. Uppsatsenutvrderar specikt tv metoder baserade p Machine Learning, nmligen linjra ochtrdbaserade regressionsalgoritmer.Resultaten fr olika scenario och topologier visar att det nya tillvgagngssttet kanuppskatta 10% fr frdrjning och 16% fr genomsnittlig packetfrlust