The aim was to identify geographical areas of concern in the process of catchment delineation in urban areas in geographic information systems, GIS. The aim was also to develop a new method for the delineation in order to address identified problems. The calculations of the catchment areas are important for subsequent hydraulic modeling, which is performed in order to properly design storm water systems capable of handling the storm water. An object oriented method was developed that took into account the identified, problematic factors in the infrastructure and buildings of the urban area. An alternative algorithm for the preprocessing of the digital elevation model, DEM, was tested to see how the results would differ from a current method for catchment delineation with another algorithm for the preprocessing of the DEM. The two different algorithms for DEM preprocessing were used before the same procedure of catchment delineation was performed on the DEMs in ArcGIS. The delineated catchments from the two different preprocessing algorithms along with the object oriented method, were evaluated against reference catchment areas manually plotted by hydraulic modeling specialists. The object oriented method showed a higher correlation to the reference than the topographic methods for the most important target parameters when performing well. However the object oriented method has to improve in precision and efficiency if it is to be used for the calculations. The catchment delineation methods with different DEM preprocessing algorithms yields different results. For the DEM preprocessing, an algorithm from Whitebox GAT seemed to performed better in flat areas and in areas where the storm water lines have been lowered into the DEM, than the ArcGIS method did. However the combination of Whitebox GAT and ArcGIS resulted in some problems with the catchment delineation and hence the combined method has to be refined in order to be used.

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.