The presented findings were achieved in the scope of a thesis supervised in a joint effort involving the Technical University of Berlin and the Fraunhofer FOKUS Institute. This work was partially funded by the German Federal Ministry for Economic Affairs and Energy under the funding number 03SIN514.

Climate change has put significant pressure on energy markets. Political decisions such as the plan of the German government to shut down coal power plants by 2038 are shifting electricity production towards renewable and distributed energy resources. The share of these resources will continue to grow significantly in the coming years. This trend changes the ways how energy markets work which mandates fundamental changes in the underlying IT infrastructure. In this paper, we propose a blockchain-based solution which enables an economically viable and grid-serving integration of distributed energy resources into the existing energy system. Our blockchain-based approach targets intraday and day-ahead operating reserve markets, on which energy grid operators and operators of distributed energy resources can trade flexibilities within the schedulable energy production and consumption of their resources. By utilizing these flexibilities as an operating reserve, renewable and climate-friendly technologies can contribute to maintaining the grid stability and security of supply while simultaneously creating economically interesting business models for their operators. We propose to define blockchain-based short-term energy markets by utilizing the concept of general-purpose smart contracts and cryptocurrencies. This enables direct and decentralized trading of energy flexibilities without any intermediary or central instance. We demonstrate the feasibility of our approach through an implementation of a prototype of the proposed markets based on the Ethereum blockchain and provide a detailed evaluation of its efficiency and scalability.

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often generate or collect sensitive, real-time data used in critical application scenarios (e.g., health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victim of malicious actors. In this paper, we presented a brief overview of existing attacks and defense strategies and used this as motivation for proposing an integrated methodology for developing protection mechanisms for smart city IoT networks. The goal of this work was to lay out a theoretical plan and a corresponding pipeline of steps, i.e., a development and implementation process, for the design and application of cybersecurity solutions for urban IoT networks. The end goal of following the proposed process is the deployment and continuous improvement of appropriate IoT security measures in real-world urban IoT infrastructures. The application of the methodology was exemplified on an OMNET++-simulated scenario, which was developed in collaboration with industrial partners and a municipality.

VoIP-based emergency communication is a promising approach to improving the safety of citizens worldwide. The transition required in this scope includes substituting the legacy PSTN/SS7 based emergency call system by Next Generation IP based components for call establishment and control. Thereby, SIP is used as a session control protocol and RTP as the means to transfer emergency data between the caller and the corresponding Public Safety Access Point (PSAP). The emergency data is not only restricted to voice communication but can cover a rich variety of data, which can be acquired by different means (including the end-user devices) and transmitted over IP. This includes video, geopositioning data, voice, Real-Time Text, and sensor data in line with emerging IoT architectures and approaches. A vital aspect in this scope is given by the performance of the underlying network, including its capability to establish calls in emergencies and to transfer the data required for serving the situation. Therefore, in this paper, we evaluate the computational performance of the most recent VoIP emergency system implementation, which was developed by the H2020-EMYNOS project as a realisation of the EENA NG112 Long Term Definition (LTD) vision. We perform a series of trials and evaluate the performance of the EMYNOS system in a multi-party lab environment established during the project. We evaluate the time needed to perform basic emergency call operations over IP, whilst in parallel generating Internet type of background traffic. Correspondingly, we worked out a methodology and implemented it in our testbed, both of which are presented in the current paper. The obtained numerical results lead to the conclusion that SIP-based emergency services stand a good chance to replace legacy systems when it comes to their performance. Additionally, we also provide a perspective on how the blockchain technology could potentially be put to use to enhance the quality of the next-generation emergency services. We propose the utilisation of blockchain technology for tracking emergency calls and enabling efficient recognition of fraud calls, which is a critical aspect for PSAP providers concerning the potential denial of service attacks. In this context, we provide evaluations and numerical results based on a private Ethereum based blockchain playground running at the premises of Fraunhofer FOKUS.

<p>Autonomous drones are reaching a level of maturity when they can be deployed in cities to support tasks ranging from medicine or food delivery to environmental monitoring. These operations rely on powerful AI models integrated into the drones. Ensuring these models are robust is essential for operating in cities as any errors in the decisions of the autonomous drones can cause damage to the citizens or the urban infrastructure. We contribute a research vision for trustworthy city-scale deployments of autonomous drones. We highlight current key requirements and challenges that have to be fulfilled for achieving city-scale autonomous drone deployments. In addition, we also analyze the complexity of using XAI methods to monitor drone behavior. We demonstrate this by inducing changes in AI model behavior using data poisoning attacks. Our results demonstrate that XAI methods are sensitive enough to detect the possibility of a data attack, but a combination of multiple XAI methods is better to improve the robustness of the estimation. Our results also suggest that currently, the reaction time to counter an attack in city-scale deployment is large due to the complexity of the XAI analysis.</p>