In the "Internet of Things" (IoT), the things will be able to sense, communicate, and interact. They will also exchange data, information and knowledge, and locate themselves and other things that surround them. In order to be able to interact, the things need to recognize that they are in proximity of other things. It is anticipated that the most widespread components of the IoT will be passive radio frequency identification (RFID) tags because they are inexpensive and provide automatic identification. However, passive RFID tags are not capable of performing complex operations, such as proximity detection and localization, which will be required in future networks. In this thesis, we describe existing problems with current RFID systems and survey potential solutions for localization and proximity detection. Then we present a new RFID device called "Sense-a-Tag" (ST) that can passively detect and decode backscattered signals from tags in its proximity. There have already been an attempt to use this device for tracking. However, detailed analysis of the performance of the ST especially for proximity detection has not been performed yet. We show that when STs are added to a standard RFID system, the problems of proximity detection and localization with RFID tags can readily be solved. Then we applied ST-based system for identifying people and object interactions. The potential uses of ST as an augmented device for IoT applications are discussed in this thesis. Advantages and limitations of an ST based RFID system have been investigated in details for each application. Results obtained from real experiments illustrate that an ST-based RFID system is feasible for proximity detection applications. In addition, a special software is developed in C\# to process the data and run a localization algorithm based on proximity detection information. The same software has been used for tracking people's activity. Different scenarios have been considered in the experiments. We tried to consider majority of factors that might affect the accuracy in the experiments including: angle and distance between the reader/ST and tags, timing in sending queries, presence of human body, etc. The simulations based on real experiments and results illustrates that an ST-based RFID system can be a realistic solution for proximity detection and localization for Location Positioning systems (LPS) and activity monitoring in future IoT.

In the "Internet of Things" (IoT), the things will be able to sense, communicate, and interact. They will also exchange data, information and knowledge, and locate themselves and other things that surround them. In order to be able to interact, the things need to recognize that they are in proximity of other things. It is anticipated that the most widespread components of the IoT will be passive radio frequency identification (RFID) tags because they are inexpensive and provide automatic identification. However, passive RFID tags are not capable of performing complex operations, such as proximity detection and localization, which will be required in future networks. In this thesis, we describe existing problems with current RFID systems and survey potential solutions for localization and proximity detection. Then we present a new RFID device called "Sense-a-Tag" (ST) that can passively detect and decode backscattered signals from tags in its proximity. There have already been an attempt to use this device for tracking. However, detailed analysis of the performance of the ST especially for proximity detection has not been performed yet. We show that when STs are added to a standard RFID system, the problems of proximity detection and localization with RFID tags can readily be solved. Then we applied ST-based system for identifying people and object interactions. The potential uses of ST as an augmented device for IoT applications are discussed in this thesis. Advantages and limitations of an ST based RFID system have been investigated in details for each application. Results obtained from real experiments illustrate that an ST-based RFID system is feasible for proximity detection applications. In addition, a special software is developed in C\# to process the data and run a localization algorithm based on proximity detection information. The same software has been used for tracking people's activity. Different scenarios have been considered in the experiments. We tried to consider majority of factors that might affect the accuracy in the experiments including: angle and distance between the reader/ST and tags, timing in sending queries, presence of human body, etc. The simulations based on real experiments and results illustrates that an ST-based RFID system can be a realistic solution for proximity detection and localization for Location Positioning systems (LPS) and activity monitoring in future IoT.

Automatic detection of space objects in optical images is important to close proximity operations, relative navigation, and situational awareness. To better protect space assets, it is very important not only to know where a space object is, but also what the object is. In this dissertation, a method for detecting multiple 1U, 2U, 3U, and 6U CubeSats based on the faster region-based convolutional neural network (Faster R-CNN) is described. CubeSats detection models are developed using Web-searched and computer-aided design images. In addition, a two-step method is presented for detecting a rotating CubeSat in close proximity from a sequence of images without the use of intrinsic or external camera parameters. First, a Faster R-CNN trained on synthetic images of 1U, 2U, 3U, and 6U CubeSats locates the CubeSat in each image and assigns a weight to each CubeSat class. Then, these classification results are combined using Dempster's rule. The method is tested on simulated scenarios where the rotating 3U and 6U CubeSats are in unfavorable views or in dark environments. Faster R-CNN detection results contain useful information for tracking, navigation, pose estimation, and simultaneous localization and mapping. A coarse single-point attitude estimation method is proposed utilizing the centroids of the bounding boxes surrounding the CubeSats in the image. The centroids define the line-of-sight (LOS) vectors to the detected CubeSats in the camera frame, and the LOS vectors in the reference frame are assumed to be obtained from global positioning system (GPS). The three-axis attitude is determined from the vector observations by solving Wahba's problem. The attitude estimation concept is tested on simulated scenarios using Autodesk Maya.

Denna rapport presenterar ett proof-of-concept avseende inomhuspositionering, baserat på Bluetooth-fyrar och mobiltelefoner, kombinerat med ett system för besökshantering. Detta mot bakgrunden av ett önskemål om att underlätta interaktionen mellan lärare och studenter på Malmö högskola.Till följd av att viss verksamhet på Malmö högskola flyttat till nya lokaler har lärarnas arbetsplatser förändrats, vilket lett fram till att studenterna har svårt att dels lokalisera tillgängliga lärare och dels initiera besök utan att störa andra lärare. Genom att upprätta en prioriterad kravspecifikation utifrån MoSCoW-metoden, baserat på intervju- och enkätsvar från lärare, identifieras kraven till ett system för positionering av lärare samt tillhörande system för besökshantering.Utifrån Pahl’s utvecklingsprocess för framtagning av en teknisk artefakt analyseras och utvärderas olika lösningsalternativ för inomhuspositionering, vilket leder fram till ett förslag avseende en prototyp. Prototypen avser symbolisk positionering i ett system för närhetsdetektering utifrån fördefinierade zoner, baserat på aktiva mobiltelefoner och passiva Bluetooth-fyrar. Slutligen implementeras framtagen prototyp för inomhuspositionering, i kombination med ett system för besökshantering, för att visa på dess genomförbarhet. Detta i form av en summerad systemvy som även inkluderar en molnbaserad databas, ett webbgränssnitt och två Android-applikationer. / This report presents a proof-of-concept regarding indoor positioning based on Bluetooth beacons and mobile phones, combined with a system for visitor management. This against the background of a desire to facilitate the interaction between teachers and students at Malmö University.As a result of that Malmö University moved to new facilities, the teachers' workplaces changed, which led to students having difficulty locating available teachers and initiate visits without disturbing other teachers. By establishing a prioritized requirement specification based on MoSCoW method, which is a result of interviews and survey responses from teachers, the requirements are identified for a system for the positioning of teachers and related systems for visitor management.Based on Pahl's development process for the preparation of a technical artifact, some different solutions for indoor positioning is analyzed and evaluated, leading to a proposal regarding a prototype. The prototype relates to symbolic positioning in a system for proximity detection by predefined zones, based on active mobile phones and passive Bluetooth beacons. Finally, the prototyp for indoor positioning is then implemented in combination with a system for visitor management to demonstrate its feasibility. This in the form of a summed system view that includes a cloud-based database, a web interface and two Android applications.