In a collaboration between Kolmården Zoo and Linköping University, supported by the Norrköping municipality’s fund for research and innovation, mobility measurements have been performed inside the zoo. These measurements have been done by six WiFi sniffers collecting anonymised MAC addresses from the visitors smartphones. The aim of this thesis is to analyse these data to understand visitor flows in the park and other statistics using a model based mobility analysis. The work implies that one can make a rather good prediction of the geographical visitor distribution using this equipment and statistical models. / I ett samarbete mellan Kolmården djurpark och Linköpings universitet, sponsrat av Norrköpingskommuns fond för forskning och utveckling, har rörelsemätningar gjorts inuti parken. Mätningarna har utgjorts av sex WiFi-sniffers som samlar in anonymiserade MAC-adresser från besökares smartphones. Målet med detta arbete är att analysera denna data för att förstå besökarflöden i parken och annan statistik genom att använda en modellbaserad rörelseanalys. Arbetet visar att man med denna utrsutning och statistiska metoder kan skapa en god prediktion av hur den geografiska besökardistributionen ser ut över tid.

In this thesis, we characterize the proprietary active scanning algorithm of several wireless network interface cards. Our experiments are the first of its kind to observe the complete scanning process as the wireless network interface cards probe all the channels in the 2.4GHz spectrum. We discuss the: 1) correlation of channel popularity during active scanning and access point channel deployment popularity; 2) number of probe request frames statistics on each channel; 3) channel probe order; and 4) dwell time. The knowledge gained from characterizing wireless network interface cards is important for the following reasons: 1) it helps one understand how active scanning is implemented in different hardware and software; 2) it can be useful in identifying a wireless rogue host; 3) it can help implement Active Scanning in network simulators; and 4) it can radically influence research in the familiar fields like link-layer handovers and effective deployment of access points.

The number of WiFi Devices is increasing every day, most people traveling hasa device with a WiFi network card enabled. This is something EffectSoft AB in Halmstad utilizes in their service Flow, to track and count devices. The accuracy of counted devices was however not accurate enough for a commercial use and this is where this candidate Thesis will continue research on how to improve the accuracy. It introduces the fundamental problem on how one cannot directly count transmitted MAC-Addresses to count present devices, since the manufacturers implement features against this such as MAC-Address randomization. It covers how manufacturers are not consistent in their implementation of the IEEE 802.11 standard, how this can be utilized to estimate how many devices are present in the networkwith three different approaches. It also concludes that Control Frame Attacks is not a viable approach any longer to count devices and the best method for counting devices are a combination of Passive Probe Request Analysis techniques. / Mängden enheter som kommunicerar över WiFi ökar dagligen och idag bär de flesta människor en enhet med ett aktiverat WiFi-nätverkskort. Detta använder EffectSoft AB, ett företag i Halmstad till sin teknik Flow för att räkna mobila enheter. Noggrannheten för beräkningen är dock inte tillräckligt bra för att produkten ska kunna vara applicerbar på marknaden och därav handlar denna kandidatuppsatsen om beräkning av mobila enheter. Denna rapport presenterar de problem som man stöter på vid beräkning av mobila enheter som till exempel randomisering av MAC-Adresser. Den täcker även hur tillverkare inte är konsekventa i sin implementation av IEEE 802.11 standarden och hur detta kan utnyttjas genom tre metoderför beräkning av antal mobila enheter. Det fastställs att Control Frame Attack inte längre är en möjlig metod för syftet samt att den bästa metoden för beräkning avantalet mobila enheter är en kombination av olika passiva Probe Request analyser.

The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build upon. Machine learning algorithms such as k-Nearest-Neighbors were also developed to estimate a mobile device's approximate location inside a building.