• Prof. Matthias Mnich, TU Hamburg, Institute for Algorithms and Complexity
• Prof. Petra Berenbrink, Universität Hamburg, FB Informatik

The transition of the European Union towards a carbon-neutral modern economy is one of the major challenges of this decade. As one key means, a decentralized generation and trading approach of elec-trical energy by so-called prosumers from renewable sources is promoted. This way, households gen-erate parts of their consumed electrical energy from solar panels, and trade remaining energy and excess energy within their community and the large supplier. In this project we address the challenging algorithmic questions arising in such “smart grid” settings: How much self-generated energy to con-sume and how much to sell? How to implement trade through smart contracts? At which prices and at which times to sell, and buy, energy from other prosumers and the supplier? What is the best strat-egy to decide how much energy to generate, and at which at which cost? How to use blockchains to verify the integrity of trades? How to ensure 99.99% availability of power? How to forecast energy consumption with uncertain demands and uncertain solar power? The goal in this project is to devel-oped advanced algorithmic methods to answer these questions, to implement these methods and make them work in real-life, to make a strong impact on our future society.

• Prof. Dr. Thomas C. Schmidt, HAW-Hamburg, Dept. Informatik
• Prof. Dr. Andreas Timm-Giel, TU Hamburg, Electrical Engineering, Computer
  Science and Mathematics

The objective of SUSTAIN is to establish a distributed urban sensing system that can
autonomously perform its measurement tasks in a sustainable, resilient way for an unlimited
time. The SUSTAIN network opens a maintenance-free option of easy user participation in urban
data acquisition and sharing, laying open grounds for environmental and socio-technical studies
as well as digital twins in the Hamburg metropolitan area.
The network consists of energy-neutral, next generation ECO-Boxes, a (successor of the)
hardware-software co-design for harvesting, measuring, and managing nodal energy with the
RIOT IoT operating system. Within this network, nodes autonomously coordinate their available
resources and jointly optimize operations to sustainably and reliably perform their sensing
duties with the best possible coverage.

This project tackles key topics of the sharing.city.college in the areas „Smart, Sharing
Infrastructures“ (energy, scalability, distribution, hw/sw co-design) and „Engineering
sharing.city (empirical tools, architectures, crowd-sensing). The major research challenges of
SUSTAIN are in ultra-lean distributed resource coordination, adaptive system
reconfiguration via dynamic voltage and frequency scaling, robust and resilient data
replication and aggregation based on low-power mesh networks, generic management and
integration into the multi-purpose RIOT operating system for low-end devices.

• Prof. Dr. Jochen Schiewe, HafenCity Universität Hamburg
  Lab for Geoinformatics and Geovisualization (g2lab)
• Prof. Dr. H. Siegfried Stiehl, Universität Hamburg
  Department of Informatics | Image Processing Research Group

SmartCities produce and require more and more heterogeneous data sets. In addition to the various digital databases and maps, crowdsourcing and citizen science approaches and projects make more and more user-generated data available (e.g. OpenStreetMap). Furthermore, data from various sen-sors contribute to the huge data availability. Successfully linking such huge and heterogeneous data sets is necessary to exploit the semantic information within and enable innovative applications. To establish this link, spatial relationships can be the key.

In this project, the georeferencing of historical and modern maps is taken up as an example for the problem area. The specific task is to automatically recognize persisting geographic features via OCR and keypoint detection and to match them between documents. There are manifold applications in this context: In particular, the solution to the georeferencing problem allows long-term monitoring of settlement and landscape developments and disposal of unexploded ordnance. The results will also have an impact on other tasks, such as the filling of incomplete modern maps from older stocks (data fusion), the semantic search in maps (e.g. by place name or spatial feature) as well as the „time and place independent provision of scientific sources“ as a new task of archives.

• Prof. Dr. Eva Bittner, Universität Hamburg, Department of Informatics
• Prof. Dr.-Ing. Jörg Noennig, HafenCity Universität Hamburg HCU, City Science Lab

Making diverse data accessible and understandable for citizens as well as planners and authorities throughout multifaceted urban planning processes is a key challenge toward participatory planning for smart cities. Citizens as experts of their own environment can be important partners in urban development, if they receive contextualized assistance in understanding planning alternatives and in interpreting and building on the vast amount of data available at different stages of the participation process. This project aims at enabling citizens to contribute in a more focused way while actively working with participation tools, e.g. for co-designing urban living solutions together with intelligent assistance systems. In order to achieve this research objective, new interaction models, methods, and prototypical systems in the field of machine learning and data science need to be developed and evaluated in the application domain of participatory urban planning and design. The project contributes technical (complex data integration processes and machine learning workflows for diverse data types, specific data infrastructure in urban planning, heterogeneous users) as well as socio-technical design knowledge (working practices and interaction models) for bridging existing gaps between citizens and professionals in participatory urban planning and design.

• Prof. Dr. Stefan Voß, Universität Hamburg, Institute of Information Systems, Fachbereich Informatik
• Prof. Dr. Volker Skwarek, Hochschule für Angewandte Wissenschaften Hamburg, Faculty of Life Science

This project aims to provide, after thorough investigation, integrated innovative solutions for urban public transportation. The focused urban public transportation includes the land transport and the city-connected waterway transport, both of which play vital roles in the contribution to the concept of smart city mobility and logistics. The growth in passenger flow, increasing energy consumption, and more stringent environmental requirements lead to sub-optimal use of urban public transportation capacity in terms of modal units, buses, subways, trains, vessels (ferries) and associated infrastructures. By using multi-agent based microscopic simulation, artificial intelligent optimization, satellite remote sensing, and data mining, four major achievements are expected by this proposal, which are a decision support system to provide an estimation of traffic delays, a mobile app to monitor real-time energy consumption, a microscopic traffic operation behavior analysis system to guide smart driving and sailing of vehicles and vessels, and an impact analysis-towards a framework for Pandemic. Based on that, a smart city logistics system involving the station/terminal, buses, subways, trains, vessels, and associated infrastructures is conducted to facilitate the smooth urban public transportation and increase the transport capacity. The results will support implementing generally recognized qualities for environmentally friendly, cost efficient, and effective urban public transportation.

• Prof. Dr. Mathias Fischer, Universität Hamburg, Fachbereich Informatik
• Prof. Dr. Thomas Schmidt, Hochschule für Angewandte Wissenschaften
  (HAW), Department Informatik

The Internet of Things (IoT) is the main driver for the digitalization of smart cities. For the
interconnection of IoT devices and provisioning of smart city services, a multitude of platforms
or middlewares have emerged. They allow to obtain and process data from connected sensors
in real-time, which resembles the concept of complex event processing (CEP). In CEP low-level
events are combined to complex events at higher abstraction levels. To prevent manipulations by
malicious users and attackers, the data integrity of complex events needs to be protected. This
requires to protect their complete composition starting from low-level events, like sensor values,
several levels of dependent complex events and intermediate results, all of them potentially
originating from different devices. This project will develop novel approaches for protecting
the data integrity and trustworthiness of data in CEP settings. For that, we will look into
cryptographic approaches, but also into the application of trust management. Moreover, we
will investigate the combination of integrity protection and trust management to combine the
advantages of both and enable scalable protection of smart urban data against manipulations
and attackers.