Industrial machine vision is rapidly adopting cost-effective 25-100 GbE network interfaces, which enable multi-megapixel cameras to generate frames of tens of megabytes at high frame rates with no tolerance for loss. However, the de facto GigE Vision (GEV) standard was developed in the 1 GbE era based on UDP sockets, and its capacity to scale has been the subject of ongoing debate. Although RDMA techniques like RoCEv2 and TCP as an intermediate solution have been suggested by vendors and standardization bodies for long-term adoption, a methodical, application-centric evaluation has been lacking.

Low Earth Orbit (LEO) satellite links are pivotal for ubiquitous vehicle connectivity, yet their performance is impacted by a complex interplay of weather, constellation dynamics, and physical obstructions. While individual models for these impairments exist, protocol and application designers lack a unified tool to evaluate behavior under realistic, composed LEO conditions. We present COSME, a route-aware, real-time mobility emulator that integrates multiple impairment models - including obstruction-based loss, constellation-induced jitter, precipitation-driven bandwidth reduction, and packet loss at handovers - into a single framework. By orchestrating Linux network namespaces via tc and netem, COSME enables the high-fidelity playback of merged impairment traces. We demonstrate COSME through five diverse application showcases, highlighting the impact of different congestion control algorithms and transport protocols on LEO connectivity.

Lately, growing effort has been invested to analyze the Starlink performance under vehicular user terminal mobility. As such measurement campaigns are labor- and time-consuming, existing studies mostly focus on single test drives. In this paper, we demonstrate that repeatability and replicability of mobile Starlink measurements represent serious challenges, mainly due to numerous factors that can be hardly controlled. First, we conduct an extensive measurement campaign of Starlink's latency and packet loss under vehicular mobility. The campaign focuses on repeatability and consists of 35 single test drives of the identical highway route over the span of four months. Then, we demonstrate that replicability remains challenging. For this, we replicate existing findings and find new, seemingly statistically significant correlations by purposefully cherry-picking single test drives. By considering our complete dataset, we show that in reality, these findings are likely statistical variations caused by uncontrollable factors. Based on this, we discuss the challenges and provide guidelines for meaningful future Starlink measurements under vehicular mobility.

Amidst global warming and climate change, reducing the energy consumption of our telecommunication infrastructure becomes increasingly important. A popular approach to do so is switching off underutilized hardware components during low-load phases, supplemented by Traffic Engineering (TE) to free them from the remaining traffic. While there already is a body of work examining the potential of this Green TE concept, most of these approaches focus solely on maximizing the switched-off hardware while not overutilizing the network, thereby not considering other crucially important operational constraints and requirements (e.g., regarding latency). This renders the actual practical usability of these approaches questionable at best. To address this issue, this paper proposes two Segment Routing-based Green TE algorithms for maximizing energy savings while also adhering to a broad set of operational constraints. In extensive evaluations on a variety of networks, including recent data from a Tier-1 Internet Service Provider, we show that our approaches are able to maintain near-optimal power saving levels, while substantially reducing configuration effort, satisfying latency bounds, and adhering to other important constraints as well.

Using Radio Frequency (RF) signals as a way of sensing has seen great popularity and promising results in existing research. However, the technology is limited by the fact that an object has to be located between a transmitter and a receiver in order to be observable by such a system. While many publications have addressed this limitation by leveraging multipath propagation effects, these are often only found in closed indoor environments. In this paper, we introduce a novel method to utilize RF sensing from a distance that does not rely on multipath effects, allowing it to be used in scenarios where it would have previously been considered impractical such as outdoor applications. By receiving the ground reflection of a signal, we measure objects within its path as the signal propagates through them. This allows combining both antennas into a single compact unit that can be pointed at an object from a distance. We perform a series of experimental evaluations to gather preliminary data on the feasibility and performance of this approach. Using our setup, we are able to count water bottles positioned underneath the sensor with high accuracy based solely on the received signal strength of the reflected component.

As the number of cyber-attacks is rising, Network Intrusion Detection Systems (NIDS) are becoming increasingly important for the detection and identification of malicious traffic, and Machine Learning techniques are gaining traction for the classification of network traffic. However, especially Deep Learning models are vulnerable to adversarial examples— subtle input perturbations that cause misclassification. While adversarial examples for images must be imperceptible, network data perturbations face complex, domain-specific constraints.We examine the vulnerability of NIDS to adversarial attacks, and compare six machine learning architectures and three black-box attack methods on four Network Intrusion Detection (NID) datasets. We define a threat model that provides minimal knowledge and limited access to an adversary, and use a highly restricted feature subset to apply perturbations to. Our results show attack success rates of 0.1-36.9%, with large, dataset-specific differences between architectures. In a second step, we apply three methods to minimize the size of the perturbations and find that the Pointwise attack is especially well suited for NID data. Finally, we analyze perturbed features and find single features greatly impact classification.

Collaboration among drone swarms in various applications often requires dynamic data exchange to optimize their missions. When employing device-to-device (D2D) communications, drones must form connected topologies enabling multi-hop communication. Maintaining a connected topology during their movement to new locations is a major challenge that has not been tackled in the literature. In this paper, we first formalize this problem, proving the decision version is NP-Hard, and break it into sub-problems, detailing their complexity under certain constraints. We then propose polynomial time solutions to these subproblems. Our proposed approach first makes a location assignment for each drone to determine where to move as their next locations. Next, our approach relocates each drone to their designated target location without losing in-network connectivity of the drone topology. We achieve this by leveraging a two-step approach: contraction and re-expansion. In the contraction phase, drones converge towards a specific location, reducing network diameter and increasing connectivity. In the expansion phase, drones disperse to their new target positions. Mathematical proofs and extensive simulations validate our method’s effectiveness in maintaining connectivity during drone relocations.

The prevalence of bird species is especially valuable as indicator for biodiversity, environmental health, and ecosystem changes. The development of reliable AI-based species classifiers and low-cost Autonomous Recording Units (ARUs) has significantly improved the capabilities of automation in species monitoring and localization. One ongoing challenge in the automated evaluation process is the issue of clock drift among the distributed ARUs, which can affect the accuracy and synchronization of data collected across multiple devices. Specifically, the clock drift of AudioMoths ARUs is of interest as they are commonly used in this context. In this paper, we characterize the clock drift in AudioMoth ARUs and explore different clock synchronization strategies. Additionally, we investigate the impact of the clock drift and the time synchronization strategies on bird call localization as an example in a simple TDoA localization simulation.

Low Earth Orbit (LEO) satellite networks such as Starlink allow global and affordable internet access. This not only connects remote residents, but also enables new potentials such as connecting non-stationary agricultural systems in rural areas for high-precision spot farming. One challenge is that tractors and farming robots need to transmit large amounts of data from cameras and laser scanners in real-time, which easily overwhelms the uplink capabilities of a single LEO connection. In this paper, we examine the Starlink performance of multiple terminals within a single service cell to lay ground for multipath link bundling as a possible approach to increase link capabilities. For this purpose, we present two measurement setups consisting of four and seven Starlink dishes placed in one service cell. We conduct multiple measurements and evaluate UDP and TCP throughput, latency, and packet loss. We find that UDP throughput scales almost linearly with the number of dishes, indicating significant bundling potential up to an expected limit. In contrast, TCP BBRv1’s bundling potential is limited, due to inefficiencies in utilizing the links. Moreover, we find that some packet loss events are synchronized within a service cell. For future and independent research, we make our dataset publicly available.

Many state-of-the-art Segment Routing (SR) Traffic Engineering (TE) algorithms rely on Linear Program (LP)-based optimization. However, the poor scalability of the latter and the resulting high computation times impose severe restrictions on the practical usability of such approaches for many use cases. A promising way to address these issues is to preemptively limit the number of SR paths considered during optimization by employing certain preprocessing strategies. In the first part of this paper, we conduct an extensive literature review of such preprocessing approaches together with a large-scale comparative performance study on a plethora of real-world topologies, including recent data from a Tier-1 Internet Service Provider (ISP). In the second part, we then use the insights gained from the former study to develop a novel combined preprocessing approach which also guarantees to not interfere with the satisfiability of practically important latency bound constraints. Our approach is able to reduce the number of SR paths to consider during optimization by as much as 97%–99%, while still allowing to achieve close to optimal solutions. This facilitates an around 10 × speedup for different LP-based SR TE algorithms, which is more than twice as good as what is achievable with any of the previously existing methods. Finally, we also study the applicability of the path preprocessing paradigm to the use case of tactical TE, showing that it facilitates an around 37% speedup in this context as well. All in all, this constitutes a major improvement over the current state-of-the-art and further facilitates the reliable use of LP-based TE optimization for large segment-routed networks.

Recent advancements in the context of Segment Routing (SR) have shown that the Midpoint Optimization (MO) concept enables a substantial reduction in the number of SR policies required to implement Traffic Engineering (TE) configurations. In this paper, we demonstrate that this concept can also be applied to the use case of tactical TE to considerably reduce the number of network changes required to react to critical events, thereby facilitating lower provisioning times and a generally improved time-to-repair. For this, we develop MOLS, a Local Search-based optimization routine that is able to provide close to optimal solutions within just a couple of seconds. The latter is shown based on extensive evaluations featuring various real-world topologies, including data from the backbone of a Tier-1 Internet Service Provider. Compared to state-of-the-art approaches relying on conventional SR, MOLS achieves similar or better solution quality while requiring substantially fewer configuration changes to implement the respective solutions. Furthermore, MOLS is able to resolve over 99% of overutilization scenarios resulting from different failure types, mostly within sub-second fashion and with an exceptionally small number of changes. Lastly, we also extend MOLS to adhere to specified latency bounds while even fixing initially violated ones.

Decentrally administered networks consist of a plethora of hosts providing various services to the Internet and several administrators are responsible for mitigating software vulnerabilities. Therefore, these networks potentially expose a large attack surface depending on the capabilities and workload of administrators. In this paper, we present the automateD nETwork pERimeter thREat pRevention System (DETERRERS). It automatically scans hosts at the network perimeter, assesses the risk of exposed vulnerabilities, and performs actions based on the assessed risk. This supports administrators in their work and enables faster reaction to software vulnerabilities. Additionally, host-based security policies can be configured in a modular user interface by system administrators and firewall configurations can be generated automatically. We deploy our system in a university network with decentralized administration and evaluate the risk assessment process, the influence on the attack surface of the network, and the time-to-remediate from vulnerabilities.

Network access control is generally seen as the first line of defense in network security. Being capable of automatically detecting and actively preventing unauthorized intrusion attempts is therefore a desirable property of modern networks, especially in large-scale industrial scenarios. Using MAC address filters, a first security measure on OSI Layer 2 can be established. Though, by using MAC address spoofing, this protection can be circumvented trivially, whereas securing against this attack is a notable challenge. Therefore, this paper introduces a new Layer 2 MAC address spoofing detection and prevention system called ‘PUF-Access-Control-System (PUF-ACS)’, that utilizes Physically Unclonable Functions (PUFs) to derive and bind unique MAC addresses to each device. By using PUFs, nanoscale imperfections during chip manufacturing are exploited to derive a device-unique secret that can only be generated by the device possessing the PUF. To quantify the latency impact and the security implications of the proposed solution, this paper also provides a detailed evaluation of the concept in terms of additional latency, and scalability considerations.

Increasing the robustness of networks against failures or traffic changes is an important objective for many operators. Instead of only adapting network configurations to the new conditions in a reactive fashion after a critical event has already occurred, proactively hedging networks against those events has gained increasing popularity over the recent years. A variety of approaches have been proposed that implement this concept of proactive robustness using Segment Routing (SR). However, all of those focus solely on the use of conventional end-to-end SR. In this paper, we show that this can be a limiting factor regarding the achievable level of robustness and that utilizing the Midpoint Optimization concept for SR, instead, facilitates (sometimes considerably) more robust durable Traffic Engineering configurations. Furthermore, it also allows to reduce the number of SR policies required to implement the respective configurations. This is not only discussed from a theoretical perspective but also confirmed by evaluations on real-world data from the backbone network of a Tier-1 Internet Service Provider.

Low Earth Orbit (LEO) satellite networks like Starlink are susceptible to both external factors (e.g., weather and obstruction) and internal factors such as frequency and resource allocation changes. This causes performance instabilities by design. In this demo, we show that such instabilities can have a detrimental impact on the performance of TCP and consequently on large parts of today’s internet, as most traffic uses TCP as transport protocol. Consequently, LEO-specific adaptations to either TCP and/or higher-level applications are necessary.

Indoor localization based on wireless communication networks has attracted a lot of attention in research. It can deliver better accuracy than a traditional Global Navigation Satellite System (GNSS) and operate in environments where satellite signals can not be received. Although more accurate methods have been published, fingerprinting based on Received Signal Strength (RSS) remains interesting. Such a system can be deployed on top of most existing networks without imposing additional requirements or restrictions to the communication. However, existing solutions usually require a larger number of base stations with known locations (anchors) within a mobile node’s reception range. This is required to prevent ambiguous fingerprints and deliver accurate results. However, it also increases the network’s energy consumption and operating cost. In this paper, we present a new approach that uses multi-frequency fingerprints to eliminate this requirement and operates with only a single anchor node while achieving accuracy that is comparable to existing solutions. In order to efficiently collect RSSI fingerprints across multiple frequencies, we introduce a measurement methodology using Software Defined Radio (SDR).

Traffic engineering is essential for optimizing computer network performance and ensuring efficient data transfer in backbone networks. Green traffic engineering, in particular, aims to reduce energy consumption and environmental impact while maintaining the efficacy of network operations. This paper presents an innovative algorithm that utilizes a 2-Segment Routing approach to reduce the number of active linecards within a backbone network, focusing on improving energy efficiency. Notably, the main contribution of this research lies in its incorporation of network failures into the optimization process. Unlike previous approaches, our algorithm ensures a balance between minimizing energy consumption and maintaining network functionality in the face of failures. Through a meticulous evaluation using real-world data obtained from a Tier-1 Internet Service Provider (ISP), we demonstrate the algorithm’s effectiveness in achieving energy savings while preserving network robustness during adverse scenarios. This dual emphasis on energy efficiency and resilience represents a notable advancement in the field of network optimization.

While several sophisticated scheduling strategies have been proposed to enhance performance of the Multipath Transport Control Protocol (MPTCP), most schedulers were implemented and evaluated before butter bloat optimizations have been introduced to the underlying Transport Control Protocol (TCP). Thus, the research question arises how these optimizations impact MPTCP performance across different scheduling strategies, and if a kernel-upgrade yields better results than changing the scheduler. To answer this question, we have upgraded several popular schedulers to the newer Linux kernel version 4, and conducted testbed measurements for different path rates and delays. Our data verifies that MPTCP indeed strongly benefits from the inherent TCP optimizations, so that the kernel upgrade often has a larger impact than a scheduler change. Thus, our findings indicate that the field of MPTCP scheduling must be revisited in the age of butter bloat optimizations.

A recent innovation in the context of Segment Routing (SR) Traffic Engineering (TE) is the use of the Midpoint Optimization (MO) concept to substantially reduce the number of SR policies required to implement TE solutions. However, these benefits come at the price of a potential deterioration of traffic steering capabilities as the individual, per-demand traffic control of end-to-end (E2E) SR is lost. In this paper, we show that a hybrid SR approach utilizing both MO as well as E2E SR allows to combine the individual benefits of both approaches. It results in improved TE capabilities and, thus, enables better TE solutions. Besides discussing this with theoretical examples, we also confirm our findings in an extensive evaluation on real-world network topologies. For this, we propose a TE algorithm based on the hybrid SR approach and show that it outperforms state-of-the-art algorithms that rely solely on MO or E2E SR. Furthermore, we demonstrate that the hybrid SR approach is also suitable for the use-case of time-constrained, tactical TE.

Nowadays, anomaly-based network intrusion detection system (NIDS) still have limited real-world applications; this is particularly due to false alarms, a lack of datasets, and a lack of confidence. In this paper, we propose to use explainable artificial intelligence (XAI) methods for tackling these issues. In our experimentation, we train a random forest (RF) model on the NSL-KDD dataset, and use SHAP to generate global explanations. We find that these explanations deviate substantially from domain expertise. To shed light on the potential causes, we analyze the structural composition of the attack classes. There, we observe severe imbalances in the number of records per attack type subsumed in the attack classes of the NSL-KDD dataset, which could lead to generalization and overfitting regarding classification. Hence, we train a new RF classifier and SHAP explainer directly on the attack types. Classification performance is considerably improved, and the new explanations are matching the expectations based on domain knowledge better. Thus, we conclude that the imbalances in the dataset bias classification and consequently also the results of XAI methods like SHAP. However, the XAI methods can also be employed to find and debug issues and biases in the data and the applied model. Furthermore, the debugging results in higher trustworthiness of anomaly-based NIDS.

Improving the energy efficiency of Internet Service Provider (ISP) backbone networks is an important objective for ISP operators. In these networks, the overall traffic load throughout the day can vary drastically, resulting in many backbone networks being highly overprovisioned during periods of lower traffic volume. This paper proposes a new Segment Routing (SR)-based traffic engineering algorithm to reduce power consumption of a backbone network in times of low utilization. The traffic steering capabilities of Segment Routing are utilized to remove traffic from as many links as possible. This is used to turn off the corresponding hardware components. Furthermore, it simultaneously ensures that solutions comply to additional operator requirements regarding the overall Maximum Link Utilization in the network. Based on data from a Tier-l ISP and a public available dataset, we show that our approach allows for up to 70 % of the overall linecards to be switched off, corresponding to an around 56 % reduction of the overall energy consumption of the network in times of low traffic demands.

Green Traffic Engineering encompasses network design and traffic routing strategies that aim at reducing the power consumption of a backbone network. We argue that turning off linecards is the most effective practically feasible approach to reach this goal. Thus, we investigate the problem of minimizing the number of active line cards in a network while simultaneously allowing a multi-commodity flow being routed and keeping the maximum link utilization below a certain threshold. In addition to proving this problem to be NP-hard, we present an optimal ILP-based algorithm as well as a heuristic based on 2-Segment Routing. Lastly, we evaluate both approaches on real-world networks obtained from the Repetita Framework and a globally operating Internet Service Provider. The results of this evaluation indicate that our heuristic is not only close to optimal but significantly faster than the optimal algorithm, making it viable in practice.

This paper proposes an optimal deployment strategy for Unmanned Aerial Vehicles (UAVs) to create a wireless mesh network above ground users in an area without internet connectivity. The deployment strategy is aimed at maximizing user coverage and providing in-network connectivity, even with the added challenge of ground user mobility. The area is modeled as a grid, and ground user mobility is simulated using BonnMotion and represented as a list of temporal heatmaps. The problem is formulated as a mixed integer linear program with a network connectivity constraint based on network flows. The proposed approach’s performance is evaluated through simulation, and the results validate its effectiveness in terms of coverage. Overall, this study demonstrates the potential of UAVs to serve as a flying network backbone, providing temporary connectivity to users.

This demo presents link ’em version 2, an open source modification of the Linux Network Emulator (netem), to enable trace-based loss and delay emulation of pre-recorded real-world network conditions. It is used in an experimental setup to demonstrate and evaluate the impact of Network Coding-based Forward Erasure Correction for TCP (TCPyNC) on the performance of Dynamic Adaptive Streaming over HTTP (DASH). Thus, the proposed demonstration showcases the novel features of link ’em v2 and highlights the capability of network coding-based FEC to improve the overall video quality and reduce stalling of DASH video streams.

Wireless transmission of images in real time over large distances is a challenge for applications such as agricultural UAV-based remote sensing. The Wifibroadcast project uses standard WiFi hardware to provide a purely unidirectional transmission link for such situations. This paper examines the trade-off between image compression and FEC for image transmission via Wifibroadcast. The goal is to achieve highest robustness against transmission errors with regards to visual quality. For this, we conducted two experiments: First, we implemented a complete simulation of the transmission process to assess different degrees of packet and byte corruption. Afterward, we incorporated real network traces into the simulation in order to model realistic corruption patterns. The results show that for uniformly distributed byte corruption patterns, compression in combination with redundancy does not yield benefits to visual quality. However, if packet loss is the dominating corruption source, FEC is able to make image transmission more robust.

Some core advances in the early research on wireless networks have been made with simple analytical models for collisions. While complex simulations are nowadays often used to evaluate such networks, the more simple analytical models are still helpful for gaining fundamental understanding of effects. One challenge in using such models is finding appropriate parameters for them. To facilitate quick plausibility checks on these models, ready-to-use parameter sets for realistic example networks are desirable. In this paper, we provide such parameter sets for two networks, one in an office building and one in an industrial facility.

Using existing general purpose wireless technologies, it is difficult to transmit large amounts of data from Unmanned Aerial Vehicles (UAVs) to a ground station during flight. Thus, there is a demand for specialized solutions. Wifibroadcast proposes a solution for this use case, utilizing a unidirectional and connectionless design to transmit data using unmodified low-cost WiFi hardware. In this paper, we measure and evaluate the performance of Wifibroadcast for UAV to ground transmissions over large distances and provide a direct comparison against standard IEEE 802.11. We demonstrate that Wifibroadcast is able to outperform WiFi in all tested scenarios while identifying specific characteristics, limitations, and requirements of a long-distance transmission on both the 5 GHz and 2.4 GHz frequency bands.

When given a cryptographic public verification key, a receiver needs to be sure about the authenticity of the sender. Whenever an adversary is able to replace a well-intended public verification key of one of two participants within a communication relationship, she is able to send maliciously signed messages, using her own secret key, thereby compromising the whole authorization system. To overcome these issues for small to medium-sized distributed sensor networks, we previously proposed the NFC-Key Exchange protocol. Using the NFC-KE challenge-response authorization scheme, an attacker will not be able to intercept any communication unless she is authorized by a centrally trusted authority. Though, the NFC-KE protocol considers only local file storages for the storing operations of the authorized public-keys. This approach is inherently vulnerable to any zero-day exploits that enable an attacker to infiltrate the filesystem. Additionally, once the central authority component is restarted or becomes corrupted, every previously exchanged key needs to be reauthenticated. By using the Distributed Ledger Technology (DLT) framework Hyperledger Fabric, this paper proposes a new extension concept for the NFC-KE protocol in order to enhance the security of the public-key store. By using this extension, the NFC-KE protocol gains increased tamper-resistance and outage resilience, due to the decentralized nature of the DLT.

In this paper, we discuss the concept of Midpoint Optimization (MO) for Segment Routing (SR). It is based on the idea of integrating SR policies into the Interior Gateway Protocol (IGP) to allow various demands to be steered into them. We discuss the benefits of this approach when compared to end-to-end SR and potential challenges that might arise in deployment. We further develop an LP-based optimization algorithm to assess the Traffic Engineering capabilities of MO for SR. Based on traffic and topology data from a Tier-1 Internet Service Provider as well as other, publicly available data, we show that this algorithm is able to achieve virtually optimal results with regards to the maximum link utilization, that are on par with state-of-the-art end-to-end SR approaches. However, our MO approach requires substantially less policies to do so. For some instances the achieved reduction ranges up to more than 99%.

The secure Internet of Things (loT) increasingly relies on digital cryptographic signatures which require a private signature and public verification key. By their intrinsic nature, public keys are meant to be accessible to any interested party willing to verify a given signature. Thus, the storing of such keys is of great concern, since an adversary shall not be able to tamper with the public keys, e.g., on a local filesystem. Commonly used public-key infrastructures (PKIs), which handle the key distribution and storage, are not feasible in most use-cases, due to their resource intensity and high complexity. Thus, the general storing of the public verification keys is of notable interest for low-resource loT networks. By using the Distributed Ledger Technology (DLT), this paper proposes a decentralized concept for storing public signature verification keys in a tamper-resistant, secure, and resilient manner. By combining lightweight public-key exchange protocols with the proposed approach, the storing of verification keys becomes scalable and especially suitable for low-resource loT devices. This paper provides a Proof-of-Concept implementation of the DLT public-key store by extending our previously proposed NFC-Key Exchange (NFC-KE) protocol with a decentralized Hyperledger Fabric public-key store. The provided performance analysis shows that by using the decentralized keystore, the NFC- KE protocol gains an increased tamper resistance and overall system resilience while also showing expected performance degradations with a low real-world impact.

Due to the COVID-19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use Bluetooth Low Energy (BLE) signal strength data to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does hardly deliver accurate results. We present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 & 5 GHz) and BLE signal strength data, measured in four different environments. We utilize these data to train machine learning models. The evaluation showed significant improvements in the distance classification and consequently also the contact tracing accuracy. However, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.

In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.

This work in progress paper presents an automated approach for network coverage prediction in real-world environments by combining mobile mapping, 3D mesh generation, and a ray launching based network simulator. We identify the challenges and demonstrate the functionality of such a pipeline. We preview an empirical evaluation in a realistic real-world environment.

Web traffic censorship limits the free access to information, making it a global human rights issue. The introduction of HTTP/3 (HTTP over QUIC) yields promising expectations to counteract such interference, due to its novelty, build-in encryption, and faster connection establishment. To evaluate this hypothesis and analyze the current state of HTTP/3 blocking, we extended the open-source censorship measurement-tool OONI with an HTTP/3 module. Using an input list of possibly-blocked websites, real-world measurements with HTTPS and HTTP/3 were conducted in selected Autonomous Systems in China, Iran, India, and Kazakhstan. The presented evaluation assesses the different blocking methodologies employed for TCP/TLS versus the ones employed for QUIC. The results reveal dedicated UDP blocking in Iran and major IP blocklisting affecting QUIC in China and India.

Over the recent years, Segment Routing (SR)-based Traffic Engineering (TE) received more and more attention in the research community. However, what has been mostly neglected so far is its capability to configure looping forwarding paths that visit nodes or even edges multiple times. In this paper, we show that, against intuition, the configuration of such loops can inherit (in some occasions significant) benefits with regards to common TE objectives if Equal Cost Multipath (ECMP) is used. This is not only illustrated on small theoretical examples but also confirmed for 2SR with real-world data from the backbone network of a Tier-1 Internet Service Provider, as well as other publicly available topologies.

In many robotic applications as well as in the area of immersive multimedia, large point clouds need to be transmitted over a network preferably in real-time. Typically, the point cloud is first compressed to a significantly smaller size, before it is transmitted as a simple file transfer. A drawback of this method is that it requires a point cloud to be fully available for the compression process to begin, inducing additional delay. In this paper, we explore the potential of streaming incoming points directly from an ongoing laser scan. Therefore, we propose a compression algorithm that operates on an ongoing point stream. Furthermore, we propose and compare different rate adaptation methods that dynamically adapt the compression rate to the currently available network data rate. Our evaluations show promising results in terms of rate fluctuations around the available network data rate and the achieved quality of our solution.

The quality of Bluetooth Low Energy (BLE) connections can be improved by deactivating wireless channels when external interference is detected. The BLE standard contains basic requirements to implement this behavior. However, it does not specify how channels should be classified and disabled, leaving the details up to the implementations. In this paper, an implementation is presented and evaluated, based partially on existing work. A new algorithm for re-activating channels that were previously disabled in order to allow for operation in dynamic environments with changing background interference is proposed and demonstrated to deliver improvements over existing work, resulting in better connection stability. Finally, an evaluation under real-world conditions confirms the improvements to BLE connections that were previously only shown in controlled testing environments.

TCP still is the prevailing transport protocol on the Internet. It does, however, suffer from severe performance issues on links with non-congestion losses. Instead of relying on the RTT-sensitive ARQ mechanism of TCP, network coding (NC) uses linear combinations of packets to correct forward erasures. Several approaches were developed to incorporate NC into TCP. While they are able to improve the offered service, their lack of deployability and fairness in modern environments is not desirable. To mitigate this, we propose TCPyNC, a Python-based intra-session NC user space shim layer for TCP. Not only are its gains equally high, but it is also more flexible regarding the use of TCP options and parameters. We use a numerical analysis and a testbed evaluation to determine parameters that enable a fair use of TCP in conjunction with Forward Erasure Correction (FEC). In order to reach this goal, we found the maxim less is more to be a sensible orientation. For scenarios with 10% random loss, instead of choosing a high redundancy rate and a large generation size in order to gain the best loss reduction, choosing redundancy rates that leave between 1% and 6% loss for TCP’s ARQ to correct and generation sizes below 32 when using default TCP parameters, yields fair results.

Wireless networks are indispensable for current and future elements of industry 4.0 and the digital transformation of companies. Industrial environments with arrays of machines mostly made of metal differ significantly from home and office environments, with consequential effects on wireless communication. We deployed wireless sensor networks built with commercial-off-the-shelf hardware in industrial and office scenarios and evaluated various aspects of wireless communications with different technologies and parameters. In this paper, we present our measurement architecture and the core findings. The results show highly spatial- and time-dependent differences between the two scenarios and also provide a comparative evaluation of the applied technologies. In addition, we also share parameters for established simulation models making future simulative evaluations particularly easy.

Traffic engineering is an important concept that allows Internet Service Providers (ISPs) to utilize their existing routing hardware more efficiently. One technology that can be used is Segment Routing (SR). In this paper, we address the use of SR to increase the resilience against failure scenarios. In addition, we develop solutions that are manageable and, thus, deployable in a tier 1 ISP network. We propose a post-convergence aware SR based optimization model. With it, we can proactively find a single SR configuration that is beneficial in all predefined failure scenarios, including single link failures, shared risk link group failures, and node failures. In addition to this use-case, we also extend the optimization model to include other important practical requirements such as keeping the number of SR tunnels to a minimum, avoiding arbitrary traffic splitting, or meeting latency bounds. We evaluate our approaches with recently measured data from a tier 1 ISP and show that we can improve over state of the art routing approaches.

Human mobility plays an important role in multiple research areas, ranging from virus spread research to network performance analysis. As real-world campaigns tend to be rather time consuming and expensive, they are often used to extract statistical features characterizing human mobility, which are then used as basis to create mobility models. For the implementation of such models and the subsequent simulative generation of movement traces, various tools exist. One such tool, focusing on the mobility modeling aspect of network performance analysis, is BonnMotion (BM).In this paper, we introduce new features to BM which increase the overall usability of the framework, simplify the implementation of new models, and improve the trace generation performance by introducing parallel processing capabilities. In addition, the introduced features are used to implement two additional movement models: The Sedentary Random Waypoint model and the Working Day Model. Furthermore, we exemplarily evaluate both, the impact of our enhancements, and the newly added models.

The continuous monitoring of crop growth is crucial for site-specific and sustainable farm management in the context of precision agriculture. With the help of precise in situ information, agricultural practices, such as irrigation, fertilization, and plant protection, can be dynamically adapted to the changing needs of individual sites, thereby supporting yield increases and resource optimization. Nowadays, IoT technology with networked sensors deployed in greenhouses and farmlands already contributes to in situ information. In addition to existing soil sensors for moisture or nutrient monitoring, there are also (mainly optical) sensors to assess growth developments and vital conditions of crops. This article presents a novel and complementary approach for a low-cost crop sensing that is based on temporal variations of the signal strength of low-power IoT radio communication. To this end, the relationship between crop growth, represented by the leaf area index (LAI), and the attenuation of signal propagation of low-cost radio transceivers is investigated. Real-world experiments in wheat fields show a significant correlation between LAI and received signal strength indicator (RSSI) time series. Moreover, influencing meteorological factors are identified and their effects are analyzed. Including these factors, a multiple linear model is finally developed that enables an RSSI-based LAI estimation with great potential.

Smart Farming is driven by the emergence of precise positioning systems and Internet of Things technologies which have already enabled site-specific applications, a sustainable resource management, and interconnected machinery. Nowadays, agricultural machines and implements are equipped with multiple embedded sensors and actors continuously producing extensive data streams. For data communication on such machinery, ISOBUS, an internal vehicle bus, is used. ISOBUS is based on the machine's Controller Area Network(CAN). However, neither CAN nor ISOBUS communication takes privacy or data sovereignty issues into account. With increasing interconnectivity of agricultural machines and their integration into farm management systems, those issues become more and more serious. In this paper, we briefly present the architecture of our modular privacy framework CAN't. Using off-the-shelf hardware, a special proxy is prototypically implemented that allows to purposefully filter and manipulate CAN data streams for the sake of privacy. The feasibility and possibilities of our approach are described in this paper. By means of a customized video game, a live demonstration will additionally show the effect of the proposed privacy filters.

Traffic engineering is an important concept that allows Internet Service Providers (ISPs) to utilize their existing routing hardware more efficiently. One technology that can be used in this field is Segment Routing (SR). While transferring SR approaches from theory towards an actual deployment, it quickly becomes apparent that there are many requirements and constraints that have to be satisfied. One of the most important requirement for a traffic engineering approach is the resilience against failure scenarios. In this paper, we propose a post-convergence aware 2-SR based optimization model. With it, we are able to proactively find a single SR configuration that is beneficial in all predefined failure scenarios, including single link failures, shared risk link group failures, and node failures. We evaluate our model with recently measured data from a tier 1 ISP and show that we can improve over state of the art routing approaches.

Excessive padding overhead induced by packets of heterogeneous lengths is a critical problem when using network coding for real-world data in the wild. Current network coding schemes either use excessive zero-padding or communication-intensive packet reconstruction strategies, i.e. bundling or chaining. This paper presents novel encoding strategies to reduce the padding-induced overhead while preserving the original packet structures. The conducted evaluation is based on traces of datagram-based real-world applications, namely browsing via QUIC, Skype VoIP, FFmpeg video streaming, and UFTP file transfer. The results verify that the packet-preserving coding schemes can significantly reduce the overhead of transmitted bytes for different generation sizes and packet loss rates without the need for additional communication about packet reconstruction.

Human mobility has been shown to have significant impact on the performance of Opportunistic Networks. For reliable performance evaluation, it is important to make sure the models used are in steady state. The mobility model Small World In Motion (SWIM) incorporates statistical properties of human movements in its results and is, at the same time, easy to configure. However, SWIM is not in steady state. In this paper, we show how a steady state for SWIM can be achieved.

Segment Routing (SR) based traffic engineering can be used to avoid network congestion effectively. Most research about SR considers the routing metric as a given constant. The metric defines shortest paths. As shortest paths are essential building blocks of SR paths, the metrics heavily influence SR. In this paper, we evaluate this influence thoroughly using data from a tier one Internet Service Provider (ISP) backbone, as well as other, publicly available topologies. We conclude that metrics do in fact play a role in SR optimization. Simple metrics, however, already push SR close to the optimum with respect to the most common traffic engineering objective of minimizing maximum utilization.

In distributed Smart Farming applications, a reliable communication is often crucial, in particular during collaborating operations. In rural areas, the network connectivity via Public Land Mobile Networks is, however, not always sufficient. Network disruptions may occur hindering reliable transmissions. For the delivery of periodic sensor data streams of agricultural machines, several modern communication frameworks adopt the Message Queue Telemetry Transport (MQTT). Although MQTT is built on top of TCP, a reliable delivery, even of important data, cannot generally be guaranteed in rural environments. Due to handovers and spatial dead zones, this is in particular the case when clients are mobile. This paper presents a case-study-based performance evaluation of MQTT regarding a reliable data transmission in real-world scenarios with mobile clients. Therefore, we investigate both links, publisher to broker and broker to subscriber, separately. By doing so, we show that both links significantly benefit from a suitable parameterization of MQTT. Moreover, for many data streams in the considered scenario, the default FIFO queuing strategy of MQTT is not always the most suitable approach to cope with network disruptions. Hence, we implemented LIFO queuing as well as a novel hybrid approach in MQTT as a proof-of-concept and evaluated its impact.

Increasing the efficiency of farming has been one of, if not the, most important factor in human development since the neolithic age. It laid the path for mankind's accomplishments in the present age. Even today, new improvements are required. One of the most promising improvements is precision farming which demands a spatial distribution of crop parameters in order to cultivate plants in every location in precisely the correct way, e.g. by supplying the perfect amount of fertilizer. One convenient way to gain such a distribution is measuring data on a combine harvester while driving across the field. When data is simply collected on board and transferred and evaluated later, huge amounts of data may be collected. However, being able to estimate the distribution while being on the field has some promising advantages ranging from getting improved status information or satisfying the curiosity of farmers to being able to predict when the harvester needs to empty its tank. The major challenge in transmitting the data is having only access to low bandwidth links due to underdeveloped public land mobile network infrastructures in rural areas. We propose Compressive Field Estimate (CFE), an approach for sending compressed data on the go in order to gain an early estimate of the whole spatial scalar field of plant and ground parameters. Lastly, we demonstrate CFE's performance in a simulative evaluation based on a trace recorded by a harvester on a spacious field.

Segment Routing (SR) can be used as a traffic engineering strategy to counteract increasing loads on networks like Internet Service Provider (ISP) backbones. Many SR approaches, however, optimize traffic flows that were measured in the past. This paper introduces a new tunnel training architecture. It aims to show that the results of these strategies can still be beneficial for routing new traffic flows using the tunnel training architecture. We evaluate the selection of different matrices for training, as well as the choice of different tunnel selection heuristics. The results show that optimization results tend to be beneficial even for traffic matrices that were not known during the optimization. Classifying the quality of SR tunnels, though, proves to be difficult and trace dependent.

This paper presents an energy-efficient solution to overcome packet loss in Wireless Sensor Networks (WSNs) by adding seed-based Random Linear Network Coding to MQTT for Sensor Networks (MQTT-SN). Whereas most sensors integrated in common WSN devices consume little energy, using the radio is costly. To increase battery lifetime, devices try to minimize their radio uptime, while still satisfy timeliness and reliability of delivered data. The proposed approach uses an optimized seed-based intrasession Network Coding scheme for Forward Error Correction to shorten the sensor node's radio uptime, reducing its power consumption. The presented approach is conform to the MQTT-SN specification and, thus, interoperable with existing systems. Since the implementation is based on the application layer, it is seamlessly deployable via Over-The-Air-Programming. The presented evaluation is based on collected traces from a real-world WSN deployment in the context of Precision Agriculture. Radio uptime and power consumption measurements in an experimental testbed confirm the achieved benefits.

On trains, predetermined maintenance of wheels requires significant amounts of resources. Therefore, it is desirable to replace the scheduled maintenance by a condition-based maintenance strategy, where manual maintenance only takes place when it is actually necessary. For a minimally invasive retrofitting of wagons, a system consisting of a Wireless Sensor Network (WSN) powered by energy-harvesting offers the most convenient and least costly solution. However, tight time synchronization and high energy efficiency is essential for a successful realization. Established time synchronization techniques for WSNs do not meet the special requirements for this scenario. Looking for an alternative, we noticed that one of the main components of WSNs, the sensor, has been barely exploited for this problem. We will demonstrate the feasibility of using sensor-data-based time synchronization instead of classical message-based synchronization in wireless networks for the example scenario of on-board wheel diameter measurements on trains. In contrast to popular current time synchronization protocols for WSNs, our approach is compatible with every wireless hardware. We perform a simulative evaluation of our approach on various wireless protocols based on message delay distributions measured on Raspberry Pi.

Human mobility analysis and modeling is a very interdisciplinary field of research. Mobility models play an important role particularly in assessing the simulative performance of mobile and opportunistic networks. The mobility traces, which most of these models are based on, however, mostly suffer from several shortcomings. In this paper, we use the extensive Lausanne Data Collection Campaign (LDCC) mobility trace as basis for further map-oriented processing. Map-matching and sensible addition of optimal routes between points mitigate problems like GPS spatial noise, anonymization, and data gaps. Moreover, stay point extraction is performed as a preparation for the analysis of elemental statistical mobility characteristics. An exemplary impact evaluation of contact statistics shows that the resulting map-oriented trace basis is indeed suited for large-scale mobility analysis and simulation.

This paper presents a smooth way to include Network Coding in the Constrained Application Protocol (CoAP) for large resource transmissions. Devices in the Internet of Things usually communicate using short messages with little data. In some cases, for example, requesting firmware updates, bigger resources need to be transferred. CoAP's recently finalized blockwise transfer scheme can handle large resources, but is not efficient in lossy environments. Network Coding has proven to be more error resistant. This paper demonstrates the limitations of CoAP's existing blockwise transfer scheme and presents a new approach, based on Network Coding. The evaluation compares CoAP's regular blockwise transfer to the new Network Coding extension. Measurements on an implemented client-server application with simulated losses and delay, confirm the benefits of the extension, resulting in reduced transfer durations.

Remote sensing data acquired from satellites are a vital information source for precision agriculture to assess current crop conditions. Field measurements of plant parameters, like the leaf area index (LAI), serve as a crucial basis to validate parameter maps derived from satellite images. Traditionally, in-situ LAI measurements are collected manually. Therefore, the assessment is cost-intensive and the temporal availability of measurements is limited. Measurements provided by small sensor devices organized in a wireless sensor network (WSN) are a low-cost alternative to manual field measurements. They allow a precise LAI determination with high temporal resolution at many different locations in a field or even an entire region. These information are highly demanded for the validation of spatial information on crop conditions derived from image data acquired by modern satellites like Sentinel-2.

Human mobility has been shown to be of significant impact on the performance of Opportunistic Networks (OppNets). As shown in earlier work, the integration of geographic restrictions is not only a further step to more credibility of OppNet simulative performance evaluation, but also influences relevant metrics, such as contact duration and number of contacts. In this paper, we discuss and propose solutions to modeling issues related to geographic restrictions and investigate the impact of the road network underlying the scenario's map extract. Inter-Contact Times (ICTs) are not significantly impacted by the diversity in road network structure but considerably shorter and less contacts are reported for Tokyo's dense road network, making it an especially challenging urban scenario for forwarding algorithm performance evaluation.

Although a realistic simulation of wireless networks mainly depends on a proper model of radio wave propagation, it is common to simply assume a fixed, circular range of communication. It has been shown that mobility models affect the results of the performance evaluation of wireless networks, e.g., contact and inter-contact times for opportunistic networks. However, even when using more realistic mobility models that consider street maps, the calculation of contact metrics widely relies on simplified propagation models. More realistic models such as ray launchers require 3-dimensional maps of the environment. We assume that the low availability of such maps is a major reason for falling back to more simplified models. We show that the level of detail in widely available map data from the OpenStreetMap-project is sufficient in certain scenarios such as the calculation of contact metrics. Our results open up a path to more realistic simulative performance evaluations using arbitrary cities.

One of the primary application scenarios for mobile wireless multi-hop networks are disaster areas. However, these pose specific challenges for routing, such as mobility and highly unpredictable links. The main applications for disaster area networks - group-based voice communication and group-oriented, map-based tracking - can be realized using multicast groups. Thus, we decided to implement ODMRP for disaster area deployments. In several disaster area maneuver on-site deployments, we identified the need for extensions, making the protocol more suitable for real-world deployments. In this paper, we propose three extensions to ODMRP: (1) link quality based routing, (2) prioritization of control messages, and (3) overhead reduction mechanisms. In simulations as well as in real-world measurements, we show the benefit of the extensions proposed.

Welcome to the 38th IEEE Conference on Local Computer Networks (LCN). We are pleased to continue the tradition of excellence of IEEE LCN in offering a high quality technical program in a friendly setting that facilitate close interactions among participants.

Among other statistical features, the analysis of fine-grained GPS traces from different outdoor scenarios has shown that human mobility statistically resembles Lévy Walks and led to the design of the Self-similar Least-Action Walk (SLAW) mobility model. It was concluded that human mobility is scale-free and that this feature is invariant irrespective of any geographic constraints. These constraints were considered too scenario-specific and were omitted in SLAW. However, we argue that geographic constraints should not be considered as an unnecessary detail, but as an important feature of a realistic mobility model for the simulative performance evaluation of mobile networks. Therefore, we introduce geographic restrictions to SLAW in the form of maps. Our evaluation of the extended model (called MSLAW) shows that the introduced restrictions have a significant impact on several performance metrics relevant for opportunistic networks.

Research in the area of Wireless Sensor Networks (WSNs) has been immense during the last years. Since highly resource-constrained, WSNs pose specific challenges to the design and implementation of protocols and algorithms. In order to test, compare, and verify the intended functionality of new approaches, performance evaluations must be conducted in a sound and credible manner. Instead of simulative evaluation, the WSN research community has mostly shown a preference towards experimental evaluation. Being especially important for the evaluation of routing protocols, node mobility is one of the more complex features to facilitate in testbeds. Robots have been the most common means for moving nodes so far, which, however, introduces more costs and limits reproducibility, scalability, and mobility patterns. We present a new software-based approach that essentially combines mobility modeling with link control. Mobility patterns taken from the mobility scenario generator BonnMotion are converted and replayed to create a virtual dynamic topology. Our approach drastically reduces costs, makes mobility reproducible and scalable, and enables the use of a variety of mobility models.

The growing range of Wireless Sensor Network (WSN) applications, their long-life and large-scale design, as well as various deployment fields necessitate the feasibility of remote maintenance and reprogramming of in-situ sensor nodes. The network-wide dissemination of program code is not appropriate in every WSN due to the heterogeneity of sensor hardware, the diversity of sensing tasks, and the event and location dependency of software. Thus, a flexible and group-wise selective Over-The-Air Programming (OTAP) is required in these scenarios. Furthermore, securing the OTAP protocol is imperative in order to prevent unauthorized and malicious reprogramming attempts. In this paper, we introduce SenSeOP, a selective and secure OTAP protocol for WSNs. For this purpose, the proposed protocol uses multicast transfer supported by asymmetric cryptography. We evaluate the performance of our approach in real testbeds, compare it with state-of-the-art protocols, and show that this approach enables efficient and reliable wireless reprogramming.

Security is one of the core challenges especially for wireless multi-hop networks in public safety scenarios. Sensitive data is transmitted via insecure links. Furthermore, there may be competing interests. Thus, there is a high probability of disturbance or eavesdropping of communication. Confidentiality, integrity, and authenticity of transmitted packets can be provided by cryptographic means. However, as the devices used in multi-hop networks are usually resource constrained, the goal of this paper is to scrutinize the feasibility of securing such networks using strong but computationally expensive cryptography. In this paper, we show generic benchmarking results for tactical multi-hop network devices as well as a case study comparing two routing security approaches.

Nowadays, networked sensors are deployed in various scenarios. To transmit sensor data like GPS, temperature, or vital signs data robust communication networks are needed. Different applications and sensor types may have specific requirements to the transmission of their data regarding loss-tolerance, timeliness, priority, or completeness. These requirements are typically met by appropriate transport protocols. At the lower layers, heterogeneous networks like wireless mesh networks or long thin networks lead to specific channel characteristics that may require the use of specifically adapted transport protocols. Additionally, the type of scenario can also affect the transport layer, e.g., a scenario with static nodes compared to moving nodes with high mobility. Of course, there are many variable requirements of different sensor data streams and applications. But there are also some basic functionalities that are common, such as registering sensors at a receiver, timestamping of sensor data as well as synchronization of all nodes. Thus, we specify a new protocol for sensor data transmission and management (STMP) that provides the basic functionalities required by many sensor data streams while at the same time allowing a flexible choice of the transport protocol to be used.

Wireless multi-hop networks meet the requirements of disaster area scenarios by their definition. Recently, different mesh and Wireless Sensor Network (WSN) testbeds were deployed. However, these deployments do not meet the specific characteristics of disaster area scenarios. Developing algorithms and protocols for public safety scenarios and deploying public safety networks is a huge challenge.

Public safety organizations need robust communication networks to transmit different kind of sensor information. These networks must be reliable even when all infrastructure has been destroyed. Wireless multi-hop networks (such as Mobile Ad-Hoc Networks (MANETs), Wireless Sensor Networks (WSNs), and Wireless Mesh Networks (WMNs)) are supposed to meet the requirements of (1) spontaneous deployment, (2) being independent of any kind of existing infrastructure, and (3) robustness in the sense of self-organization and self-healing by their very definition. These networks have been a topic in research for more than a decade now. Recently, real-world tests and deployments provide valuable insights concerning challenges and future research directions. There are different mesh and WSN testbeds (e.g., [4, 9, 10]) enabling the research community to run tests in static real-world networks. However, concerning public safety requirements, there are significant differences: (1) No spontaneous deployment, (2) no or at least no mobility typical for public safety, (3) no typical applications and traffic for public safety scenarios. Due to these characteristics, developing algorithms and protocols for public safety scenarios and deploying public safety networks is a huge challenge.

Abstract Topology graph based anomaly detection (TOGBAD) is a centralized approach to detect routing attacks in tactical multi‐hop networks. It uses anomaly detection based on topology graphs to identify attackers trying to launch routing attacks. Such attacks are a serious threat to multi‐hop networks since they may be used to disrupt, eavesdrop or manipulate the network. In this work, we present studies on the impact of an attacker launching a routing attack. Furthermore, we show a reasonable choice of attack parameters for the attacker. In our main contribution, we introduce TOGBAD and an evaluation of TOGBAD concerning its detection rate with and without packet loss. Finally, we illustrate TOGBAD's detection rate with an attacker trying to inure TOGBAD's anomaly detection to his attack. Copyright © 2010 John Wiley & Sons, Ltd.

Realistic modeling of the nodes' mobility is essential for obtaining credible results from the simulative performance evaluation of wireless multi-hop networks. However, most of the mobility models in the literature have not been validated with real world movement traces. To overcome this issue, we follow a trace-based approach to mobility modeling, where movement traces from the real world scenario are statistically analyzed and used for parametrization. In this paper, we introduce and evaluate a new realistic mobility model for first responder scenarios based on such an analysis while also considering geographic restrictions by incorporating free publicly available map data.

It is my pleasure to welcome you to the first session on Demonstration of the IEEE Conference on Local Computer Networks (LCN). For the 34thIEEE LCN conference the first time in the IEEE LCN history a call for demonstrations was launched. We were looking for demonstrations for all topics covered by the main conference as well as all the workshops held in conjunction with the conference. The technical demonstrations were strongly encouraged to show innovative and original research. The main purpose of the demo session is to provide demonstrations that validate important research issues and/or show innovative prototypes.

Welcome to the 5thLCN Workshop on Security in Communication Networks (SICK Workshop).

In the recent years voice over IP (VoIP) telephony started to migrate from research to the market. In the future, all-IP networks will substitute the classical public switched telephone networks (PSTNs). Nowadays, there is no All-IP network yet, but many VoIP-providers already enable calls from VoIP to a PSTN and vice versa. By doing so, critical infrastructures within the PSTN like public safety answering points (PSAP), are accessible from the VoIP network (e.g. the Internet). Thus, there is a need for reliable performance modeling and evaluation. One aspect of particular interest e.g. for the performance evaluation of intrusion detection architectures for emergency call services is the characterization and modeling of emergency call length and frequency. In this paper, we provide a detailed analysis of traces from different PSAPs. Our work is based on empirical long-time measurements at two PSAPs. Based on these traces we characterize the load's interarrival times and call lengths concerning variation of the load, dependencies, and scalability. Furthermore, we provide fittings of the empirical data to standard probability distributions.

In the recent years Voice over IP (VoIP) telephony started to migrate from research to the market. In the future, All-IP networks will substitute the classical Public Switched Telephone Networks (PSTNs). Nowadays, there is no All-IP network yet, but many VoIP-providers already enable calls from VoIP to a PSTN and vice versa. Thus, critical infrastructures within the PSTN like the emergency call service, are accessible from the VoIP network (e.g. the Internet) and get exposed to new security threats. In particular, there is the risk of Denial of Service (DoS) attacks originating from the VoIP network. An attacker could jam the emergency call service by generating a massive load of faked emergency calls, which could lead to the loss of lives in the worst case. For us, this was the motivation to analyse the applicability of the concept of Intrusion Detection (ID) in the emergency call context and develop an adapted ID-architecture including its implementation. In an evaluation of the ID-architecture, using real emergency call traces from the fire department of Cologne, we show that the developed concept can reliably detect emerging DoS attacks from VoIP networks up to a certain VoIP diffusion rate.

Analyzing and modelling traffic is one important step in the performance evaluation of communication systems. In this paper we focus on evaluation and parameterization of traffic models for disaster area scenarios invented in our previous work. The goal is to provide scalable models for public safety scenarios as realistic and intuitive as possible. During our evaluation we examine synthetic traces for different parameters and levels of detail. As measures we examine traffic intensity, burstiness, and dependencies. The evaluation shows that the models are scalable without loosing specific characteristics. Finally, we provide an intuitive and scalable parameterization for our models that eases up using them.

This paper deals with the characteristics of multicast push to talk voice traffic sources for disaster area scenarios. The goal is to design models that can be used to assign voice traffic sources to nodes. The modelling is based on an analysis of real-life measurements during a catastrophe maneuver. The analysis shows that about half of all calls originate from the communication head of a talk group. Based on this characteristic, different models are considered. Synthetic distributions of traffic sources for the different models are generated and evaluated by statistical analysis. Finally, the impact of the different models is evaluated in an exemplary simulative network performance analysis.

Analysing and modelling traffic is one important step in the performance evaluation of communication systems. In this paper we focus on first responder (FR) networks. The goal is to figure out models that can be used to generate realistic synthetic push to talk voice traffic for single talk groups to be used in network simulation. Our work is based on an empirical long-time measurement of one FR channel. The analysis of the trace shows significant short- and long-range correlations as well as variations of load over time. As the characteristics analysed are similar to ones observed in disaster area (DA) traces, we consider n-state and 3-state (semi)-Markov models. After fitting the parameters of these models to the traces, synthetic traffic streams for the different models are generated and finally evaluated by both visual and statistical analysis.

Black hole attacks are a serious threat to communication in tactical MANETs. In this work we present TOGBAD a new centralised approach, using topology graphs to identify nodes attempting to create a black hole. We use well-established techniques to gain knowledge about the network topology and use this knowledge to perform plausibility checks of the routing information propagated by the nodes in the network. We consider a node generating fake routing information as malicious. Therefore, we trigger an alarm if the plausibility check fails. Furthermore, we present promising first simulation results. With our new approach, it is possible to already detect the attempt to create a black hole before the actual impact occurs.

This paper provides a model that realistically represents the movements in a disaster area scenario. The model is based on an analysis of tactical issues of civil protection. This analysis provides characteristics influencing network performance in public safety communication networks like heterogeneous area-based movement, obstacles, and joining/leaving of nodes. As these characteristics cannot be modelled with existing mobility models, we introduce a new disaster area mobility model. To examine the impact of our more realistic modelling, we compare it to existing ones (modelling the same scenario) using different pure movement and link based metrics. The new model shows specific characteristics like heterogeneous node density. Finally, the impact of the new model is evaluated in an exemplary simulative network performance analysis. The simulations show that the new model discloses new information and has a significant impact on performance analysis.

This paper deals with voice communication models for disaster area scenarios. The goal is to design models that can be used to generate realistic push to talk traffic for single talk groups. The modelling is based on an analysis of empirical measurements during a catastrophe maneuver. The analysis shows that the time series comprise heavy load periods and significant correlations. Based on these characteristics, different Markov and semi-Markov models are considered. Synthetic traffic streams for the different models are generated and evaluated by visual and statistical analysis. Finally, a case study outlines the impact of the different traffic models in network performance simulation

Disaster areas have been figured out as a typical usage scenario for mobile wireless ad-hoc networks (MANETs). In contrast to this, there are no specific mobility or traffic models for MANETs. We present a realistic approach to realize mobility in disaster areas based on tactical issues of civil protection. The new model is analyzed and compared to Gauss-Markov and random waypoint mobility models. Furthermore, we present first simulation results. The mobility model analysis as well as the simulation are based on two real disasters that occurred in Germany in 1999 and 2001. We show that disaster area scenarios have specific characteristics. Thus, they should be considered in MANET performance evaluation.