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Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses
(2021)
A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.
Dynamic point cloud compression based on projections, surface reconstruction and video compression
(2021)
In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.
Time-to-Live (TTL) caches decouple the occupancy of objects in cache through object-specific validity timers. Stateof- the art techniques provide exact methods for the calculation of object-specific hit probabilities given entire cache hierarchies with random inter-cache network delays. The system hit probability is a provider-centric metric as it relates to the origin offload, i.e., the decrease in the number of requests that are served by the content origin server. In this paper we consider a user-centric metric, i.e., the response time, which is shown to be structurally different from the system hit probability. Equipped with the state-of-theart exact modeling technique using Markov-arrival processes we derive expressions for the expected object response time and pave a way for its optimization under network delays.
In scientific research, the independent reproduction of experiments is the source of trust. Detailed documentation is required to enable experiment reproduction. Reproducibility awards were created to honor the increased documentation effort. In this work, we propose a novel approach toward reproducible research—a structured experimental workflow that allows the creation of reproducible experiments without requiring additional efforts of the researcher. Moreover, we present our own testbed and toolchain, namely, plain orchestrating service (pos), which enables the creation of such experimental workflows. The experiment is documented by our proposed, fully scripted experiment structure. In addition, pos provides scripts enabling the automation of the bundling and release of all experimental artifacts. We provide an interactive environment where pos experiments can be executed and reproduced, available at https://gallenmu.github.io/single-server-experiment.
LoRaWAN Network Planning in Smart Environments: Towards Reliability, Scalability, and Cost Reduction
(2022)
The goal in this work is to present a guidance for LoRaWAN planning to improve overall reliability for message transmissions and scalability. At the end, the cost component is discussed. Therefore, a five step approach is presented that helps to plan a LoRaWAN deployment step by step: Based on the device locations, an initial gateway placement is suggested followed by in-depth frequency and channel access planning. After an initial planning phase, updates for channel access and the initial gateway planning is suggested that should also be done periodically during network operation. Since current gateway placement approaches are only studied with random channel access, there is a lot of potential in the cell planning phase. Furthermore, the performance of different channel access approaches is highly related on network load, and thus cell size and sensor density. Last, the influence of different cell planning ideas on expected costs are discussed.
Lightning has fascinated humanity since the beginning of our existence. Different types of lightning like sprites and blue jets were discovered, and many more are theorized. However, it is very likely that these phenomena are not exclusive to our home planet. Venus’s dense and active atmosphere is a place where lightning is to be expected. Missions like Venera, Pioneer, and Galileo have carried instruments to measure electromagnetic activity. These measurements have indeed delivered results. However, these results are not clear. They could be explained by other effects like cosmic rays, plasma noise, or spacecraft noise. Furthermore, these lightning seem different from those we know from our home planet. In order to tackle these issues, a different approach to measurement is proposed. When multiple devices in different spacecraft or locations can measure the same atmospheric discharge, most other explanations become increasingly less likely. Thus, the suggested instrument and method of VELEX incorporates multiple spacecraft. With this approach, the question about the existence of lightning on Venus could be settled.
The first step towards aerial planetary exploration has been made. Ingenuity shows extremely promising results, and new missions are already underway. Rotorcraft are capable of flight. This capability could be utilized to support the last stages of Entry, Descent, and Landing. Thus, mass and complexity could be scaled down.
Autorotation is one method of descent. It describes unpowered descent and landing, typically performed by helicopters in case of an engine failure. MAPLE is suggested to test these procedures and understand autorotation on other planets. In this series of experiments, the Ingenuity helicopter is utilized. Ingenuity would autorotate a ”mid-air-landing” before continuing with normal flight. Ultimately, the collected data shall help to understand autorotation on Mars and its utilization for interplanetary exploration.
Shannon channel capacity estimation, based on large packet length is used in traditional Radio Resource Management (RRM) optimization. This is good for the normal transmission of data in a wired or wireless system. For industrial automation and control, rather short packages are used due to the short-latency requirements. Using Shannon’s formula leads in this case to inaccurate RRM solutions, thus another formula should be used to optimize radio resources in short block-length packet transmission, which is the basic of Ultra-Reliable Low-Latency Communications (URLLCs). The stringent requirement of delay Quality of Service (QoS) for URLLCs requires a link-level channel model rather than a physical level channel model. After finding the basic and accurate formula of the achievable rate of short block-length packet transmission, the RRM optimization problem can be accurately formulated and solved under the new constraints of URLLCs. In this short paper, the current mathematical models, which are used in formulating the effective transmission rate of URLLCs, will be briefly explained. Then, using this rate in RRM for URLLC will be discussed.
We attempt to identify sequences of signaling dialogs, to strengthen our understanding of the signaling behavior of IoT devices by examining a dataset containing over 270.000 distinct IoT devices whose signaling traffic has been observed over a 31-day period in a 2G network [4]. We propose a set of rules that allows the assembly of signaling dialogs into so-called sessions in order to identify common patterns and lay the foundation for future research in the areas of traffic modeling and anomaly detection.
This work proposes a novel approach to disperse dense transmission intervals and reduce bursty traffic patterns without the need for centralized control. Furthermore, by keeping the mechanism as close to the Long Range Wide Area Network (LoRaWAN) standard as possible the suggested mechanism can be deployed within existing networks and can even be co-deployed with other devices.
Utilizing multiple access technologies such as 5G, 4G, and Wi-Fi within a coherent framework is currently standardized by 3GPP within 5G ATSSS. Indeed, distributing packets over multiple networks can lead to increased robustness, resiliency and capacity. A key part of such a framework is the multi-access proxy, which transparently distributes packets over multiple paths. As the proxy needs to serve thousands of customers, scalability and performance are crucial for operator deployments. In this paper, we leverage recent advancements in data plane programming, implement a multi-access proxy based on the MP-DCCP tunneling approach in P4 and hardware accelerate it by deploying the pipeline on a smartNIC. This is challenging due to the complex scheduling and congestion control operations involved. We present our pipeline and data structures design for congestion control and packet scheduling state management. Initial measurements in our testbed show that packet latency is in the range of 25 μs demonstrating the feasibility of our approach.
Future mobile communication networks, such as 5G and beyond, can benefit from Virtualized Network Functions (VNFs) when deployed on cloud infrastructures to achieve elasticity and scalability. However, new challenges arise as to managing states of Network Functions (NFs). Especially control plane VNFs, which are mainly found in cellular core networks like the 5G Core (5GC), received little attention since the shift towards virtualizing NFs. Most existing solutions for these core networks are often complex, intrusive, and are seldom compliant with the standard. With the emergence of 5G campus networks, UEs will be mainly machine-type devices. These devices communicate more deterministically, bringing new opportunities for elaborated state management. This work presents an emulation environment to perform rigorous measurements on state access patterns. The emulation comes with a fully parameterized Markov model for the UE to examine a wide variety of different devices. These measurements can then be used as a solid base for designing an efficient, simple, and standard conform state management solution that brings us further towards stateless core networks.
Tactile Internet aims at allowing perceived real-time interactions between humans and machines. This requires satisfying a stringent latency requirement of haptic data streams whose data rates vary drastically as the results of perceptual codecs. This introduces a complex problem for the underlying network infrastructure to fulfill the pre-defined level of Quality of Service (QoS). However, novel networking hardware with data plane programming capability allows processing packets differently and opens up a new opportunity. For example, a dynamic and network-aware resource management strategy can help satisfy the QoS requirements of different priority flows without wasting precious bandwidth. This paper introduces virtual queues for service differentiation between different types of traffic streams, leveraging protocol independent switch architecture (PISA). We propose coordinating the management of all the queues and dynamically adapting their sizes to minimize packet loss and delay due to network congestion and ensure QoS compliance.
This document presents a networking latency measurement setup that focuses on affordability and universal applicability, and can provide sub-microsecond accuracy. It explains the prerequisites, hardware choices, and considerations to respect during measurement. In addition, it discusses the necessity for exhaustive latency measurements when dealing with high availability and low latency requirements. Preliminary results show that the accuracy is within ±0.02 μs when used with the Intel I350-T2 network adapter.
In time-sensitive networks (TSN) based on 802.1Qbv, i.e., the time-aware Shaper (TAS) protocol, precise transmission schedules and, paths are used to ensure end-to-end deterministic communication. Such resource reservations for data flows are usually established at the startup time of an application and remain untouched until the flow ends. There is no way to migrate existing flows easily to alternative paths without inducing additional delay or wasting resources. Therefore, some of the new flows cannot be embedded due to capacity limitations on certain links which leads to sub-optimal flow assignment. As future networks will need to support a large number of lowlatency flows, accommodating new flows at runtime and adapting existing flows accordingly becomes a challenging problem. In this extended abstract we summarize a previously published paper of us [1]. We combine software-defined networking (SDN), which provides better control of network flows, with TSN to be able to seamlessly migrate time-sensitive flows. For that, we formulate an optimization problem and propose different dynamic path configuration strategies under deterministic communication requirements. Our simulation results indicate that regularly reconfiguring the flow assignments can improve the latency of time-sensitive flows and can increase the number of flows embedded in the network around 4% in worst-case scenarios while still satisfying individual flow deadlines.
This paper gives an overview of our recent activities in the field of satellite communication networks, including an introduction to geostationary satellite systems and Low Earth Orbit megaconstellations. To mitigate the high latencies of geostationary satellite networks, TCP-splitting Performance Enhancing Proxies are deployed. However, these cannot be applied in the case of encrypted transport headers as it is the case for VPNs or QUIC. We summarize performance evaluation results from multiple measurement campaigns. In a recently concluded project, multipath communication was used to combine the advantages of very heterogeneous communication paths: low data rate, low latency (e.g., DSL light) and high data rate, high latency (e.g., geostationary satellite).
Today’s advanced Internet-of-Things applications raise technical challenges on cloud, edge, and fog computing. The design of an efficient, virtualized, context-aware, self-configuring orchestration system of a fog computing system constitutes a major development effort within this very innovative area of research. In this paper we describe the architecture and relevant implementation aspects of a cloudless resource monitoring system interworking with an SDN/NFV infrastructure. It realizes the basic monitoring component of the fundamental MAPE-K principles employed in autonomic computing. Here we present the hierarchical layering and functionality within the underlying fog nodes to generate a working prototype of an intelligent, self-managed orchestrator for advanced IoT applications and services. The latter system has the capability to monitor automatically various performance aspects of the resource allocation among multiple hosts of a fog computing system interconnected by SDN.
The increased occurrence of Software-Defined-Networking (SDN) not only improves the dynamics and maintenance of network architectures, but also opens up new use cases and application possibilities. Based on these observations, we propose a new network topology consisting of a star and a ring topology. This hybrid topology will be called wheel topology in this paper. We have considered the static characteristics of the wheel topology and compare them with known other topologies.