Road traffic increases constantly and the negative consequences in the form of traffic jams can be realized especially in urban areas. In order to provide real time traffic information to road users and traffic managers, accurate computer models gain relevance. A software called Mobile Millennium Stockholm (MMS) was developed to estimate and predict travel times and has been implemented on a 7km test stretch in the north of Stockholm. The core of the software is the cell transmission model (CTM) which is a macroscopic traffic flow model based on aggregated speed observations. This thesis focuses on different calibration techniques of the so called fundamental diagram as an important input factor to the CTM. The diagrams illustrate the mathematical function which defines the relation between traffic flow, density and speed. The calibration is performed in different scenarios based on the least square (LS) and total least square (TLS) error minimization. Furthermore, sources, representing the traffic demand, and sinks, representing the surrounding of the modeled network, are implemented as dynamic parameters to model the change in traffic behavior throughout the day. Split ratios, as a representation of the drivers‘ route choice in the CTM are estimated and implemented as well. For the framework of this work, the MMS software is run in a pure prediction mode. The CTM is based on the source, sink, split and fundamental diagram parameters only and run forward in time. For each fundamental diagram calibration scenario an independent model run is performed. The evaluation of the scenarios is based on the output of the model. The results are compared to existing Bluetooth travel time measurements for the test stretch, which are used as ground truth observations, and a mean average percentage error (MAPE) is calculated. This leads to a most reasonable technique for the fundamental diagram calibration – the total least square error minimization.

The IoT sensing system plays an important role in the field of the smart city. IoT devices are generally constrained nodes due to their limited power and memory. How to save energy has been a challenge for the scalability of sensing networks. Previous studies introduce the dynamic sink and three dynamic sink deployment strategies. It has been proved by simulation experiments that the sensing network with dynamic sinks can reduce energy consumption. Further investigations on new dynamic sink deployment strategies are needed to explore the full potential of dynamic sinks. This work investigates three new deployment strategies, namely Determinisitic Strategy, Prediction Strategy, and Improved Prediction Strategy. We design experiments with different scenarios and evaluate the packet delivery ratio (PDR) and power consumption performances using emulated IoT devices on the Cooja simulator. The results show that the setups with these three new deployment strategies have good performance in terms of PDR and power consumption. Furthermore, we compare the performance difference between these three new strategies. The Improved Prediction Strategy has advantages over the other two strategies and has application prospects in reality. / IoT-baserade sensorsystem spelar en viktig roll för smarta städer. IoT-enheter är i allmänhet begränsade noder vad gäller till exempel kraftförsörjning och minnesutrymme. Hur man kan spara energi har varit en utmaning för skalbarheten hos sensornätverk. I tidigare studier introduceras dynamiska sänknoder och tre strategier för utplacering av sådana sänknoder. Det har visat sig genom simuleringsexperiment att ett nätverk med dynamiska sänknoder kan minska energiförbrukningen. Ytterligare undersökningar av nya strategier för utplacering av sänknoder behövs för att utforska den fulla potentialen hos dynamiska sänknoder. I det här arbetet undersöks tre nya strategier, nämligen Determinisitic Strategy, Prediction Strategy och Improved Prediction Strategy. Vi utformar experiment med olika scenarier och utvärderar andelen levererade paket (Packet Delivery Ration", PDR) och energiförbrukningen med hjälp av emulerade IoT-enheter i Cooja-simulatorn. Resultaten visar att uppställningarna med dessa tre nya strategier har bra prestanda när det gäller PDR och energiförbrukning. Dessutom jämför vi prestandaskillnaden mellan dessa tre nya strategier. Improved Prediction Strategy har fördelar jämfört med de andra två strategierna och bedöms ha goda tillämpningsmöjligheter i verkliga miljöer.