Blockchain technology is a booming new field in both computer science and economicsand other use cases than cryptocurrencies are on the rise. Permissioned blockchains are oneinstance of the blockchain technique. In a permissioned blockchain the nodes which validatesnew transactions are trusted. Permissioned blockchains and distributed databasesare essentially two different ways for storing data, but how do they compare in terms ofperformance? This thesis compares Hyperledger Fabric to Apache Cassandra in four experimentsto investigate their insert and read latency. The experiments are executed usingDocker on an Azure virtual machine and the studied systems consist of up to 20 logicalnodes. Latency measurements are performed using varying network size and load. Forsmall networks, the insert latency of Cassandra is twice as high as that of Fabric, whereasfor larger networks Fabric has almost twice as high insert latencies as Cassandra. Fabrichas around 40 ms latency for reading data and Cassandra between 150 ms to 250 ms, thusit scales better for reading. The insert latency of different workloads is heavily affected bythe configuration of Fabric and by the Docker overhead for Cassandra. The read latency isnot affected by different workloads for either system.

The adoption of Blockchain technology looks promising in many areas. However, it is still a relatively new area of research, and implementations are not entirely free of challenges. This study focuses on the potential benefits of blockchain adoption in the public sector, with the potential to enhance democratic processes. Blockchain technology is, by design, apt at managing several significant challenges regarding communication and security in digital networks. This includes maintaining data integrity, enhancing security and privacy, and to mitigate fraud and manipulation. Possible issues that might arise when applied to the public sector is regarding law and regulation compliance. To cover the current state of the field, an exploratory literature review has been conducted. Among the results are several interesting blockchain projects around the world with emphasis on civic dialogue. Studying possible network architectures it appears that Hyperledger Fabric is the most prominent when it comes to implementations in the public sector. Based on this study, and on input from experts within the field, a proposed model that could be implemented and tested on a smaller scale is presented. The purpose is to record and track expenditures on public sector projects and make them available to the public. / Antagandet av Blockchain-teknik ser lovande ut på många områden. Det är dock fortfarande ett relativt nytt forskningsområde, och implementeringar är inte helt fria från utmaningar. Denna studie fokuserar på de potentiella fördelarna med blockchain-tillämpningar i den offentliga sektorn, och dess potential att förbättra demokratiska processer. Blockchain-tekniken är designad för att hantera flera viktiga utmaningar när det gäller kommunikation och säkerhet i digitala nätverk. Detta inkluderar upprätthållande av dataintegritet, förbättrad säkerhet och integritet för att minska risken för bedrägerier och manipulation. Möjliga problem som kan uppstå när de tillämpas på den offentliga sektorn handlar om efterlevnad av lagar och förordningar. För att täcka fältets nuvarande tillstånd har en undersökande litteraturstudie genomförts. Bland resultaten finns flera intressanta blockchain-projekt runt om i världen med tonvikt på medborgardialog. När man studerar möjliga nätverksarkitekturer verkar det som att Hyperledger Fabric är den mest framträdande när det gäller implementeringar i den offentliga sektorn. Baserat på denna studie och på input från experter inom området presenteras en föreslagen modell som kan implementeras och testas i mindre skala. Syftet är att registrera och spåra utgifter för projekt inom den offentliga sektorn och göra dem tillgängliga för allmänheten.

When the Internet was created it did not include any protocol for identifying the person behind the computer. Instead, the act of identification has primarily been established by trusting a third party. But, the rise of Distributed Ledger Technology has made it possible to authenticate a digital identity and build trust without the need of a third party. The Swedish Maritime Administration are currently validating a new maritime digital infrastructure for the maritime transportation industry. The goal is to reduce the number of accidents, fuel consumption and voyage costs. Involved actors has their identity stored in a central registry that relies on the trust of a third party. This thesis investigates how a conversion from the centralized identity registry to a decentralized identity registry affects the usability and the risk for compromised data integrity. This is done by implementing a Proof of Concept of a decentralized identity registry that replaces the current centralized registry, and comparing them. The decentralized Proof of Concept’s risk for compromised data integrity is 95.1% less compared with the centralized registry, but this comes with a loss of 53% in efficiency.

With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies. / Doctor of Philosophy / The modern power grid is transforming from unidirectional to transactive power systems. Distributed peer-to-peer (P2P) energy trading is becoming more and more popular. Rooftop PV energy and negawatt-hours as two main sources of electricity assets are playing important roles in peer-to-peer energy trading. It enables the building owner to join the electricity market as both energy consumer and producer, also named prosumer. While P2P energy trading participants are usually un-informed and do not know how much energy they can generate during the next hour. Thus, a system is needed to guide the participant to submit a reasonable amount of PV energy or negawatt-hours to be supplied. This dissertation develops a machine learning-based anomaly detection model for an energy trading platform to detect the reasonable PV energy and negawatt-hours available for the next hour's electricity trading market. The anomaly detection performance of this framework is analyzed. The building load forecasting model used in negawatt-hour trading also considers the effect of building occupancy level and HVAC setpoint adjustment. Moreover, the implication of CO2 measurement devices to monitor building occupancy levels is demonstrated. Finally, a simple Hyperledger-based electricity trading platform that enables participants to sell photovoltaic solar energy/ negawatt-hours to other participants is simulated to demonstrate the potential benefits of blockchain.

A smart contract is a recently emerging technology which enables agreement to be automatable by computers and enforceable by legal enforcement or tamper-proof execution of code. A majority of smart contracts are run on the blockchain which enables smart contract transactions without a central authority. Smart contract implementation contains several challenges which makes implementation more difficult. The problem is that organisations struggle to implement smart contracts due to the absence of documentation, standardisation, and guidelines making it difficult to know how a smart contract should be implemented. Additionally, it is unclear what capabilities and tools are required for smart contract implementation. Therefore, it is challenging for organisations to assess their own competence of smart contract implementation. This thesis aims to develop a Smart Contract Maturity Model (SCMM). The purpose of the model is to clarify the functionalities and capabilities required to implement a smart contract while also offering organisations the ability to assess the smart contract implementation competency. This improves the adoption of smart contracts. Through the help of the design science framework, the SCMM emerged from the thesis. Applying design science included explicating the problem, defining requirements, designing and developing the artefact, demonstrating and evaluating the artefact. A literature survey was used to explicate the problem and to define requirements for the maturity model. Furthermore, a case study including interviews were used to refine the requirements and to demonstrate and evaluate the SCMM. The SCMM includes maturity levels, generic goals and practices, specific goals, key processing areas and practices, tools, glossaries and smart contract examples. Inspired by the Capability Maturity model Model Integration for Development (CMMI-DEV), the maturity levels of the SCMM consisted of initial, foundation, managed, defined, quantitatively managed and optimising. The identified key processing areas were stakeholder capabilities, resources and tools, platform, contract implementation, standards, laws and terminology and security. Although there were several limitations, the SCMM contributed to the field of smart contracts by closing the gap of previous research and improving the adoption of smart contracts.