Publications

Neighbor management in vehicular networks comes with the risk of unnecessarily overloading the wireless channel, particularly when two-hop neighbor information is required. A possible solution to this challenge is the use of probabilistic data structures. In our previous work, we explored the benefits of using Bloom filters for maintaining this neighbor information showing promising results. In this paper, we now evaluate the usage of a additional probabilistic data structure, the Cuckoo Filter, which is advertised as a superior alternative to Bloom filter. We assess the performance of the Cuckoo approach in a vehicular networking scenario and find that it does not meet these expectations. In fact, it may lead to worse performance in specific configurations.

Renewable energy resources and energy efficiency measures are effective means of curtailing CO2 emissions. Solar and wind technologies have been mostly developed to meet the energy demand of off-grid remote areas or locations without grid connections. However, it is well-known that the power generation of these resources is affected by daily fluctuations and seasonal variability. One way to mitigate such an effect is to incorporate hydrokinetic resources into the energy system, which has not been well investigated yet. Therefore, this study examines the prospects of designing a hybrid system that integrates hydrokinetic energy to electrify an off-grid area. Hydrokinetic energy generation depends on water flow velocity (WFV). We estimate WFV by a model-based approach with geographical and weather data as inputs. Together with the models of the other components (wind turbine, PV panel, battery, and diesel generator) in the micro-grid, an optimization problem is formulated with the total net present cost and the cost of energy as performance criteria. A genetic algorithm (GA) is used to solve this problem for determining an optimal system configuration. Applying our approach to a small community in Nigeria, our findings show that the flow velocity of a nearby river ranges between 0.017 and 5.12 m/s, with a mean velocity of 0.71 m/s. The resulting optimal micro-grid consists of 320 kW of PV, 120 units of 6.91 kWh batteries, 2 (27 kW) hydrokinetic turbines, an 120 kW converter, zero wind turbines, and a 100 kW diesel generator. As a result, the total energy generated will be 471,743 kWh/year, of which 12% emanates from hydrokinetic energy. The total net present cost, the cost of energy, and the capital cost are USD 1,103,668, 0.2841 USD/kWh, and USD 573,320, respectively.

Practicing mindfulness is associated with stress reduction and with positive effects in the context of learning and teaching. Although effects on student populations have been studied extensively, there are few studies implementing mindfulness exercises in university courses directly. For this reason, we aimed to investigate whether the use of a brief mindfulness exercise in regular university courses, guided by the lecturers, is feasible and has immediate effects on the students’ mental states. We conducted a preregistered multicenter study with one observational arm, following an ABAB design. In total, N = 325 students from 19 different university courses were included at baseline and n = 101 students at post measurement. Students were recruited by N = 14 lecturers located in six different universities in Germany. Lecturers started their courses either by guiding a brief mindfulness exercise (intervention condition) or as they regularly would, with no such exercise (control condition). In both conditions, the mental states of students and lecturers were assessed. Over the semester, n = 1193 weekly observations from students and n = 160 observations from lecturers were collected. Intervention effects were analyzed with linear mixed-effects models. The brief mindfulness exercise, compared to no such exercise, was associated with lower stress composite scores, higher presence composite scores, higher motivation for the courses, as well as better mood in students. Effects persisted throughout a respective course session. Lecturers also reported positive effects of instructing mindfulness. Implementing a brief mindfulness exercise in regular university teaching sessions is feasible and has positive effects on both students and lecturers.

Coverage and Capacity Optimization (CCO) and Inter-Cell Interference Coordination (ICIC) are two tightly coupled and conflicting Self-Organizing Network (SON) functions that are responsible for ensuring optimal coverage and capacity in any cellular network. While executing currently, these functions may modify the same RF and antenna parameters, resulting in severe performance deteriorations. In this context, a centralized optimization and coordination approach may be impractical considering the large sizes of network clusters and the dynamics involved between the several other defined SON use cases. In this work, an implicitly coordinated and scalable self-organizing architecture is followed such that when a carefully defined multi-objective utility function for CCO-ICIC joint optimization is optimized locally by each RAN node, a desired balance between the two conflicting network targets of coverage and capacity is ensured globally. Pareto analysis of three variants of the proposed Local Multi-Objective KPI (LMO KPI) has been conducted to implicitly coordinate the two SON functions in a distributed self-organized manner. In order to recommend appropriate network configurations dynamically to quickly adapt to altering network environments, two collaborative filtering-based Recommender Systems (RecSys), one using a Deep Autoencoder and another based on Singular Value Decomposition, have been employed along with a neural network regressor to improve recommendations for cold-start scenarios. The two proposed hybrid-RecSys-based SON coordination solutions, while adopting an appropriate Local Multi-Objective KPI (LMO KPI), outperform previous work in coverage by 36% and in capacity by around 2% while reducing power consumption by more than 50%. The study demonstrates that the definition of the LMO KPI is crucial to the performance of this approach. Altogether, the work shows that the adopted self-organization and implicit SON-coordination approach is not only feasible and performant but also scales well if implemented meticulously.

Sensor data sharing has an immense potential to enhance the perception capabilities of vehicles and to provide better situational awareness. It is being standardized as collective perception by the European Telecommunication Standards Institute (ETSI) as part of Cooperative Intelligent Transport Systems (C-ITS). For the transmission of collective perception messages via sidelink in Cellular-V2X, sensing-based semi-persistent scheduling (SB-SPS) in the unmanaged mode of 5G-NR V2X provides low-latency communication among road traffic participants that are located outside the cellular network coverage. The unpredictability of the collective perception messages in periodicity and size poses certain challenges on the SB-SPS, thereby creating poor utilization of radio resources and high risk of resource collisions. Existing system level simulations study the performance of collective perception from the application perspective without addressing the radio resource allocation at the access layer. This work investigates the challenges of the sidelink resource allocation mechanisms in 5G-NR V2X and assesses the impact of the mechanism on the performance of collective perception by simulation in a realistic urban traffic environment. A practical approach is adopted to formulate mathematical models that can characterize the radio resource utilization and resource collisions arising in such environments and yield the appropriate 5G-NR V2X parameters.