The Internet-of-Things (IoT) is a network of interconnected devices with sensing, monitoring and processing functionalities that work in a cooperative way to offer services. Smart buildings, self-driving cars, house monitoring and management, city electricity and pollution monitoring are some examples where IoT systems have been already deployed. Amongst different kinds of devices in IoT, cameras play a vital role, since they can capture rich and resourceful content. However, since multiple IoT devices share the same gateway, the data that is produced from high definition cameras congest the network and deplete the available computational resources resulting in Quality-of-Service degradation corresponding to the visual content. In this thesis, we present an edge-based resource management framework for serving video processing applications in an Internet-of-Things (IoT) environment. In order to support the computational demands of latency-sensitive video applications and utilize effectively the available network resources, we employ edge-based resource management policy. We evaluate our proposed framework with a face recognition use case.

The Internet of Things (IoT) technology is undergoing expansion into different aspects of our life, changing the way businesses operate and bringing in efficiency and reliability of digital controls on various levels. Processing large amount of data from connected sensor networks becomes a challenging task. Specific part of it related to fleet management requires processing of the data on boards of vehicles equipped with multiple electronic devices and sensors for maintenance and operation of such vehicles. Herewith the efficiency of various configurations of employing Kalman filter algorithm for on-the-fly pre-processing of the sensory network originated data streams in IoT systems is investigated. Contextual grouping of the data streams for pre-processing by specialized Kalman filter units is found to be able to satisfy the logistics of IoT system operations. It is demonstrated that interconnection of the elementary Kalman filters into an organized network, the compositional Kalman filter, allows to take advantage of the redundancy of data streams to accomplish IoT pre-processing of the raw data. This includes intermittent data imputation, missing data replacement, lost data recovery, as well as error events detection and correction. Architectures are proposed and tested for the interaction of elementary Kalman filters in detection of GPS outage events and their compensation via data replacement procedure, as well as GPS offset occurrence detection and its compensation via data correction routine. Demonstrated is the efficiency of the suggested compositional designs of elementary Kalman filter networks for the purpose of data pre-processing in IoT systems.

One application of the Internet of Things that is currently developed is the collection of data from district heating systems. This development leads to the problem of how the collected data should be used to operate the systems in a manner that reduces the dependency on fossil fuels. This thesis proposes a characterization of this problem into visualization tasks and requirements, as well as a visualization application, based on the tool Power BI, for gaining insight into how to operate a district heating system. The tasks and requirements were elicited during collaborative meetings and prototyping together with district heating experts. The perceived effectiveness of the visualization application was validated with a usability evaluation, including the usability metric for user experience questionnaire, with four district heating experts. The perceived efficiency was, however, found to be lacking compared to the other usability aspects. A redesign of one of the Power BI components, which according to the evaluation should improve the application, is also suggested.

AF’s office in Karlstad currently offer consultation regarding Internet of Things but are looking to expand into development of services. They have requested that an end-to-end solution is developed in order to demonstrate the technology to potential clients. This thesis describes the work performed to realize an end-to-end solution for connecting sensors and actuators to the cloud. It also describes the problems experienced while working with Microsofts platform for cloud services, Azure. / Företaget ÅFs Karlstadkontor bistår i dagsläget med konsultering gällande Internet of Things men vill även börja utveckla och sälja tjänster. För att kunna demonstrera teknologin för potentiella kunder har de begärt att en end-to-end-lösning utvecklas. Den här uppsatsen beskriver det arbete som har utförts för att realisera en end-to-end-lösning för att koppla sensorer och ställdon till molnet. Den redogör även för de svårigheter som upplevdes gällande Microsofts plattform för molntjänster, Azure.

The Internet of Things (IoT) provide the possibility to build dynamic industrial systems and applications to improve the quality of production in industrial areas. There are many Industrial IoT Gateways (IoTGWs) available on the market, all of which have different functionalities and properties. Here, the different properties of the IIoTGWs were explored, with the researcher’s as well as vendor’s perspective in mind. The most important properties were identified using research papers and technical data sheets, based on this, a model was created. The model showed the best two gateways available in the database. The results show that methods used can be applied in future research.

With the Internet of Things becoming more and more popular, and a prediction that there will be more than 50 million devices connected to the Internet in 2020, the quantity of IoT platforms on the market is rapidly growing. Facing so many platforms to choose, the object of this thesis is to give some suggestions for reference by performing a quantitative comparison between two platforms: SensibleThings and Kaa. These two platforms have difference architectures so may suitable in different scenes. The comparison includes some measurement and evaluation under two designed scenarios and a general contrast in theory. Two scenarios cover cases of message delivery between two endpoints at different rates and multiple endpoints pushing log data continually. The result of measurement together with the theoretical analysis draw out the following conclusion. SensibleThings platform is more suitable for simple and small-scale message delivery between endpoints, like home environment with few devices. And Kaa platform is more suitable for large-scale and complicated application for data collection and processing, like meteorology field with huge amount of sensors and data.

Internet of things is a technology innovation which connects thousands of mil- lions of sensors and actuators. Platform is which provides both hardware and software service. There are so many platforms existed in the market. The objective of this study has been to answer the question which IoT platform fits bests for different scenarios by performing a quantitative comparison between SensibleThings and ThingWorx. The study has been conducted with the aid of my su- pervisor Stefan Forsström and professor TingTing Zhang. The study has shown that SensibleThings and ThingWorx both has its innovation features thus it fits to the different cases according to cases’ need. If the sensor need to update data quicker than 25 times/sec, it is better to choose ThingWorx. If the sensor doesn’t have good hardware it is better to use SensibleThings cause it is light- weight enough. For the case that more than one sensors’ data rush into one point simultaneously, ThingWorx has better performance. In this paper I use a health care system as an example. I think ThingWorx is more suitable for this case regard of its mature service on security and graphical visualization. Finally I presented my future work for platform evaluation.

With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

Context: Internet of things (IoT) technology is rapidly increasing and changes the business environment for a software organization. There is a need to understand what are important factors of business model should a software company focus on obtaining benefits from the potential that IoT offers. This thesis also focuses on finding the impact of IoT on software business model and software industry especially on software development. Objectives: In this thesis, we do research on IoT software business model and also software industry. The objectives of this research are included as follows: 1. Summarize the current business models for IoT and to identify the important factor for IoT business models. 2. Analyze the impact of IoT on software business models. 3. Analyze the impact of IoT on Software development especially on requirement engineering. 4. Provide recommendations how requirements engineering are connected to provide better support business modeling for IoT. Methods: We conducted a systematic literature review based on the guidelines suggested by Wohlin, to find the current business model for the IoT. Next, we designed and executed an industrial survey to explore the impact of IoT on a software business model and software development.The results of survey were statistically analyzed using descriptive statistics, chi-square test of significance and Friedman test. Results: 21 peer reviewed papers were identified which were analyzed in relation to their rigor and relevance. From the literature reviews, results indicate 9 business model elements are being focused on the IoT business models. In addition to this 4 most important business model factors were identified. On the other hand, the industrial survey resulted from 56 survey responses, identified that value proposition is the most important element for the IoT business model. It was also observed that even the impact is high for the value proposition. Regarding the software development, customer demands is highly impactful and moreover, the results suggest that requirement management is highly impactful. Conclusions: The current software business models were found for the IoT industries. In additional the software business model elements which were majorly focused in the IoT industries were also identified and the most important factor which brings the value for IoT business models were also discussed. Furthermore the impact of IoT on software business model element and software development, especially on the requirement phase, was analyzed and discussed. This helps the practitioners to understand the impact of IoT on software business models and software industry and helps the organization to improve IoT business to its full potential.

TCP/IP protocol gradually exposes many shortcomings such as poor scalability and mobility. Content-Centric Networking is a new architecture which cares about the content itself rather than its source. Therefore, this paper proposes a novel IoV architecture which based on Content-Centric Networking and tests its transmission interference time, transmission delay, and throughout in network layer. The experimental results show that the novel architecture is superior to the current IoV in the communication performance.