Emerging sixth-generation (6G) solutions demand learning agents that are not only autonomous and efficient, but also robust in dynamic environments and transparent in decision making. However, conventional deep reinforcement learning (DRL) approaches for mobile networks lack sufficient explainability and suffer catastrophic forgetting under dynamic conditions. In this paper, we propose a Bayesian reasoning-based explainable deep active inference (BRAIN) model, the embodied AI-inspired approach applied to mobile networks. BRAIN employs a deep generative model and minimizes variational free energy to unify perception (Bayesian state estimation) and action (resource allocation) in a single framework. Unlike DRL baselines, our agent inherently eliminates catastrophic forgetting through continuous belief updates without retraining and provides built-in explainability by exposing posterior beliefs and free energy components at runtime. Deployed as an eXtended application (xApp) on an Open, GPU-accelerated, AI-RAN testbed; BRAIN demonstrates (i) causal reasoning for slice-specific quality of service (QoS) adherence (throughput/latency/reliability), (ii) 28.3% higher robustness to traffic shifts versus DRL baselines, and (iii) real-time interpretability of resource-allocation decisions via operator-accessible belief states.

The increasing complexity of modern industrial and manufacturing systems, featuring numerous sensors and mobile components, demands reliable, low-latency communication over wireless networks. WiFi 7 addresses these requirements through enhancements such as Multi-Link Operation (MLO), enabling simultaneous use of multiple frequency bands and offering inherent link diversity. This raises the question of whether MLO can be effectively leveraged for reliability in critical systems. In this paper, we explore the integration of IEEE 802.1CB Frame Replication and Elimination for Reliability (FRER), a core Time-Sensitive Networking (TSN) standard, over MLO to address this question. We present an open-source implementation of FRER over MLO in the OMNeT++ simulator, highlight key challenges in combining these technologies, and evaluate its effectiveness in improving reliability under wireless-specific conditions such as mobility and congestion. Our results demonstrate that FRER can enhance packet delivery ratio and ensure bounded latency, albeit at the expense of reduced channel efficiency due to redundancy.

Federated learning (FL) enables collaborative model training across distributed devices while preserving privacy. However, growing heterogeneity in device resources and communication links challenges conventional FL, especially when relying on a single central server. Hierarchical federated learning (HFL) mitigates these issues by organizing devices into clusters coordinated through intermediate aggregators. Yet, the effectiveness of HFL critically depends on how clusters are formed: intra-cluster communication must be efficient, device computational capacities should be balanced to reduce stragglers, and data heterogeneity must be managed to ensure stable convergence. In this work, we propose a learning-topology co-optimization framework for HFL in networks where nodes communicate with each other with links of varying quality (e.g., device-to-device (D2D) or mesh networks). Our method jointly optimizes device connection topology and learning directions, leading to communication-efficient clusters that remain well aligned in optimization space. We provide a convergence analysis under mild assumptions, showing how inter- and intra-cluster divergence affect learning stability. Extensive experiments demonstrate that our approach consistently improves HFL performance, yielding at least a$6 \%$accuracy gain under unbalanced data distributions and over$16\%$reduction in training time for regression tasks compared with existing clustering algorithms.

This paper studies an efficient federated learning (FL) problem involving multiple edge-based clients with heterogeneous constrained resources. Compared with numerous training parameters, the computing and communication resources of clients in edge scenarios are usually insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem, which delays the convergence of FL. To address these issues, we proposeFed-RAA: aResource-AdaptiveAsynchronousFederated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates submodels of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned submodel and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, an online greedy-based algorithm is designed for asynchronous submodel assignment in Fed-RAA, improving the convergence of Fed-RAA by optimal minimization on the training delay bound of submodels. Compared to state-of-the-art methods, our Fed-RAA algorithm reduces the time required to achieve the target accuracy by an average of$ 30.89\%$, demonstrating its superior efficiency on heterogeneous constrained computing and communication resources. To the best of our knowledge, this paper is the first resource-adaptive asynchronous method for submodel-based FL with guaranteed theoretical convergence.

This article proposes a new cyberattack on decentralized federated learning (DFL), named user isolation poisoning (UIP). While following the standard DFL protocol of receiving and aggregating benign local models, a malicious user strategically generates and distributes compromised updates to undermine the learning process. The objective of the new UIP attack is to diminish the impact of benign users by isolating their model updates, thereby manipulating the shared model to reduce the learning accuracy. To realize this attack, we design a novel threat model that leverages an adversarial message-passing graph (MPG) neural network. Through iterative message passing, the adversarial MPG progressively refines the representations (also known as embeddings or hidden states) of each benign local model update. By orchestrating feature exchanges among connected nodes in a targeted manner, the malicious users effectively curtail the genuine data features of benign local models, thereby diminishing their overall influence within the DFL process. The MPG-based UIP attack is implemented in PyTorch, demonstrating that it effectively reduces the test accuracy of DFL by 49.5% and successfully evades existing cosine similarity- and Euclidean distance-based defense strategies.

Byzantine resilience is essential in federated learning (FL) to safeguard model training from malicious or faulty participants. However, existing Byzantine-resilient methods struggle when faced with heavy-tailed gradient noise, a common challenge in heterogeneous environments. In this work, we propose a Byzantine-resilient FL framework specifically designed to handle both heterogeneity and heavy-tailed noise. Our approach builds on robust distributed stochastic heavy-ball optimization, incorporating update normalization and gradient/momentum clipping to mitigate the effects of heavy-tailed noise. We establish the first high-probability convergence guarantees for Byzantine-resilient FL under these conditions, showing that our algorithms achieve optimal Byzantine resilience and align with known lower bounds. Additionally, we introduce an efficient variant of the nearest neighbor mixing technique, leveraging random projections to significantly reduce computational costs in high-dimensional settings. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that our methods outperform existing approaches in robustness against both Byzantine failures and heavy-tailed noise.

The growing resource demands of large-scale transformer models pose significant challenges for resource-constrained users, particularly in distributed environments. To address this issue, we propose a federated learning framework called Fed-Grow, which enables multiple participants to collaboratively learn a lightweight scaling operation that transfers knowledge from pretrained small models to a large transformer model. In Fed-Grow, we introduce the Dual-LiGO (Dual Linear Growth Operator) architecture, consisting of Local-LiGO and Global-LiGO components. Local-LiGO addresses model heterogeneity by adapting each participant's pre-trained model to a common intermediate form, while Global-LiGO facilitates knowledge sharing across participants without sharing local models or raw data, ensuring privacy preservation. This federated approach offers a scalable solution for growing large transformers in a distributed manner, where only the Global-LiGO is shared, significantly reducing communication overhead while maintaining comparable model performance under the same communication constraints. Experimental results demonstrate that Fed-Grow outperforms state-of-the-art methods in terms of accuracy and precision, while reducing the number of trainable parameters by 59.25% and communication costs by 73.01%. These improvements allow for higher efficiency in training large models in distributed environments, without sacrificing performance. To the best of our knowledge, Fed-Grow is the first method that enables cooperative transformer scaling in a distributed setting, making it a practical solution for resource-constrained users.

Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. IRS elements are configured to adjust the phase of the reflected signal for a user’s location and enhance the perceived signal-to-noise ratio (SNR). In this way, an IRS improves the communication link but inevitably introduces more communication overhead. This occurs especially in mobile scenarios, where the user’s position must be frequently estimated to re-adjust the IRS elements periodically. Such an operation requires balancing the amount of training versus the data time slots to optimize the communication performance in the link. Aiming to study this balance with the age of information (AoI) framework, we address the question of how often an IRS needs to be updated with the lowest possible overhead and the maximum of freshness of information. We derive the corresponding analytical solution for a mobile scenario, where the transmitter is static and the mobile user (MU) follows a random waypoint mobility model. We provide a closed-form expression for the average peak age of information (PAoI), as a metric to evaluate the impact of the IRS update frequency. As for the performance evaluation, we consider a realistic scenario following the IEEE 802.11ad standard, targeting the mmWave band. Our results reveal that the minimum achievable average PAoI is in the microsecond range and the optimal IRS update period is in the seconds range, causing 9% overhead in the link when the MU moves at a velocity of 1m/s.

Traditional rule-based or static management approaches struggle to cope with the dynamic, multi-layered nature of 5G/6G networks, creating a strong motivation for AI-native solutions – management systems built from the ground up with artificial intelligence – to enable autonomous, real-time network control. In this work, we introduce GNN-ATIVE, an AI-native orchestration framework that leverages Graph Neural Networks (GNNs) and knowledge graphs (KGs) in a unified graph-based paradigm for network management. GNN-ATIVE uses a semantic knowledge graph to represent the network’s state and context, employing standard ontologies to ensure consistency and interoperability. Building on this foundation, we design Knowledge Graph enabled Generative Pretrained Transformer (KG-GPT), a novel graph-to-graph Transformer model that performs knowledge-driven reasoning on the KG. KG ingests the structured network state (nodes, links, and attributes) and infers optimal configurations or management actions, serving as a high-level decision engine for the orchestrator. We implement and evaluate GNN-ATIVE on an Optical Transport Network (OTN) testbed using real network components. The results demonstrate that GNN-ATIVE can effectively manage OTN resources and adapt to network changes while achieving low-latency inference for decision making.

Most works on intelligent reconfigurable surface (IRS)-assisted networks consider link-level simulations and do not address the upper protocol layers. This limitation becomes particularly relevant in practical systems like 802.11 Wi-Fi, where channel access is based on listen-before-talk (LBT) scheme, making conventional IRS scheduling strategies (time-division or centralized coordination) difficult to apply. Given these reasons, we shift the focus to random channel access based on carrier sense multiple access with collision avoidance (CSMA/CA) to explore how an IRS can be beneficial in such scenarios. We first discover that improving the SNR of links from stations (STAs) to an access point (AP) introduces the potential risk of hidden terminals among the STAs in the uplink. To analyze this, we introduce ns3IRS, a framework that integrates a model of an IRS into the ns-3 network simulator, which allows us to run the full WiFi stack within an IRS-assisted wireless network. We propose two solutions to mitigate the hidden terminal problem caused by the IRS also by assuring mutual carrier sensing: (i) splitting a single centralized IRS, or (ii) using additional small IRSs nearby the STAs – to create direct links among them, with the purpose of enabling mutual carrier sensing. Results show that these two solutions can improve the throughput compared to the traditional approach where virtual carrier sensing, i.e., CSMA/CA with Request-to-Send/Clear-to-Send (RTS/CTS), is used. For instance, for a system with four users, the proposed approaches improve the throughput from 25Mbit/s to around 35Mbit/s compared to the baseline scenario with RTS/CTS. Additionally, latency and jitter are decreased. However, as the number of users increases, achieving performance gains requires a larger IRS, since the gain scales with the number of reflecting elements, especially for the centralized IRS. In scenarios where scaling the IRS is impractical, CSMA/CA with RTS/CTS may become a more effective alternative.

EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending source data to a server is bandwidth-intensive and may compromise privacy. Instead, federated learning allows each device to upload a shared machine-learning model update with locally processed data. However, this technique, which depends on aggregating model updates from various IoT devices, is vulnerable to attacks from malicious entities that may inject harmful data into the learning process. This paper introduces a new attack method targeting federated learning in EdgeIoT, known as data-independent model manipulation attack. This attack does not rely on training data from the IoT devices but instead uses an adversarial variational graph auto-encoder (AV-GAE) to create malicious model updates by analyzing benign model updates intercepted during communication. AV-GAE identifies and exploits structural relationships between benign models and their training data features. By manipulating these structural correlations, the attack maximizes the training loss of the federated learning system, compromising its overall effectiveness.

Cloud radio access network (C-RAN) in cellular systems offers improved coverage and efficiency due to statistical multiplexing. Applying centralization to Wi-Fi networks could yield similar benefits, particularly in dense deployments like campus networks. However, this is challenging due to the usage of unlicensed, shared spectrum and strict protocol timing constraints like carrier sensing, making such systems highly sensitive to C-RAN induced delays. Existing approaches typically rely on radio-over-fiber and either demand very low delay or neglect neighboring Wi-Fi networks. In this paper, we present Ce-Fi, a centralized Wi-Fi architecture operating over a packet-based fronthaul (FH) connecting the central unit with the remote radio heads. Ce-Fi employs a two-level design with two bands: the network discovery and user association is handled standard compliantly on one band while another band is used for the actual data transmission with the centralized Wi-Fi. To tolerate the FH-induced delay, we propose minor changes to the 802.11 medium access control protocol like NAV extension and the usage of a piggybacking mechanism enabling the C-RAN-AP to transmit without prior channel contention. We evaluate Ce-Fi through simulations, demonstrating that it supports high throughput even under high FH-delays and in the presence of neighboring legacy networks.

Accurate motion tracking in snow avalanche environments remains challenging. Recently, ultra-wideband (UWB)-based ranging systems have been shown to offer centimeter-level accuracy under ideal conditions. However, their performance degrades significantly in dynamic, obstructed snow to multipath and signal attenuation. This can be compensated using multisensor fusion based on inertial measurement unit (IMU) data. To address these limitations, we propose a bidirectional LSTM (Bi-LSTM) network for robust 3D localization. The proposed model leverages time-synchronized sliding windows to learn spatiotemporal correlations between high-frequency IMU dynamics and low-frequency UWB updates. Unlike traditional Kalman filters, which degrade under nonlinear motion and intermittent UWB outages, the Bi-LSTM model adaptively learns to weight each sensor modality while preserving long-term motion context. We evaluate our approach using real-world field data, using a RTK-GNSS as ground truth for validation. The proposed method achieves a 3D localization root mean square error of 0.31m, representing a 68% improvement over adaptive Kalman filter fusion and a 72% gain over UWB-only localization. These results highlight the efficacy of deep recurrent sensor fusion for localization in dynamic, extreme environments.

Channel state information (CSI) is widely used for joint communication and sensing in sub-6 GHz wireless networks, but its application at mmWave frequencies remains under-explored. This work applies a quantized amplitude-based CSI similarity metric to data from a 28 GHz indoor testbed, showing the feasibility of CSI-based ambient sensing in next-generation wireless networks.

The integration of IEEE 802.1 Time-sensitive Networking (TSN) with wireless technologies promises to support realtime applications over hybrid networks. Although TSN over Ethernet is well established, extending its capabilities to WiFi remains a technical challenge due to hardware and protocol differences. In this work, we integrate a core TSN scheduling mechanism, IEEE 802.1Qbv Time-aware Shaper (TAS), with WiFi interfaces in the Linux kernel. Our approach enables scheduling for mixed-criticality traffic over wired and wireless links using standard Linux tools. We provide our implementation and evaluation scenarios as open source to support further research in TSN-WiFi integration.

This paper introduces a novel analytical framework for evaluating energy-efficient, QoS-aware network-sharing strategies in cellular networks. Leveraging stochastic geometry, our framework enables the systematic assessment of network performance across a range of sharing paradigms, including both conventional single-operator scenarios and advanced hybrid strategies that enable full integration and cooperation among multiple mobile network operators. Our framework incorporates diverse user densities, rate requirements, and energy consumption models to ensure comprehensive analysis. Applying our results to real-world datasets from French mobile network operators, we demonstrate that hybrid network sharing can yield substantial energy savings, up to 35%, while maintaining QoS. Furthermore, our results allow us to characterize how the benefits of network sharing vary as a function of the geographical and functional characteristics of the deployment area. These findings highlight the potential of collaborative sharing strategies to enhance operational efficiency and sustainability in next-generation cellular networks.

Network management systems for beyond 5G (B5G) and 6G networks today require efficient approaches for handling increased heterogeneity, network-function dis-aggregation, performance requirements, and optimizing networks to support highly diverse use cases. While the Open-Radio Access Network (O-RAN) Service Management and Orchestration (SMO) frameworks efficiently manage RAN and cloud infrastructure, the fragmentation of management platforms across RAN, Core Network (CN), and Transport Network (TN) introduces operational inefficiencies, particularly in Non-Public Networks (NPNs) deployments. This paper proposes a novel extension to the O-RAN SMO architecture, integrating CN and TN management functionalities into a unified control framework. By exploiting AI/ML-driven$\mathrm{x} / \text{rApps}$and a converged data analytics pipeline, the proposed architecture enhances SMO's fault management, resource optimization, and service continuity capabilities. Our implementation validates the feasibility of the proposed SMO extension, demonstrating subscriber-specific QoS assurance through O-RAN-based mobility management mechanisms. The proposed approach successfully shows how RAN-/Core-converged SMOs enable significant enhancements to O-RAN's x/rApps, allowing for subscriber-specific as well as application-specific differentiated QoS assurance.

We investigate the problem of finding second-order stationary points (SOSP) in differentially private (DP) stochastic non-convex optimization. Existing methods suffer from two key limitations: \textbf{(i)} inaccurate convergence error rate due to overlooking gradient variance in the saddle point escape analysis, and \textbf{(ii)} dependence on auxiliary private model selection procedures for identifying DP-SOSP, which can significantly impair utility, particularly in distributed settings. To address these issues, we propose a generic perturbed stochastic gradient descent (PSGD) framework built upon Gaussian noise injection and general gradient oracles. A core innovation of our framework is using model drift distance to determine whether PSGD escapes saddle points, ensuring convergence to approximate local minima without relying on second-order information or additional DP-SOSP identification. By leveraging the adaptive DP-SPIDER estimator as a specific gradient oracle, we develop a new DP algorithm that rectifies the convergence error rates reported in prior work. We further extend this algorithm to distributed learning with heterogeneous data, providing the first formal guarantees for finding DP-SOSP in such settings. Our analysis also highlights the detrimental impacts of private selection procedures in distributed learning under high-dimensional models, underscoring the practical benefits of our design. Numerical experiments on real-world datasets validate the efficacy of our approach.

Millimeter wave (mmWave) spectrum is associated with abundance of available spectrum and excellent spectrum reuse, but also has the significant downside of decreased resilience. Communication can be disrupted when the line of sight (LOS) path is blocked, or the link quality may degrade due to obstacles. These issues are exacerbated by mobility. One possible countermeasure against such signal shadowing and blockage is the use of intelligent reconfigurable surfaces (IRSs). However, while IRSs have the advantage to form a beam directly targeting the user, user-tracking and the need to orchestrate the IRS are very challenging. A practical solution towards improving the resilience is the use of non-reconfigurable reflecting surfaces (NRRSs), which enable communication through reflections by creating additional signal paths via a pre-configured reflection pattern. With sufficient number of reflections through NRRSs, the probability of communication outage is significantly reduced. The additional signal paths in NRRS-assisted networks can be very useful in scenarios characterized by repetitive mobility patters, especially since the cost and complexity are heavily reduced compared to IRS. While the benefits of IRS deployment have been extensively studied, most analyses are limited to the simulation domain. In contrast, this paper provides practical insights derived from measurements conducted on a mmWave band testbed. Results reveal that even with a few NRRSs, the resilience of a mmWave communication link could be dramatically improved resulting in close-to-zero packet loss. However, the channel becomes highly frequency-selective, which makes the use of advanced physical layers like IEEE 802.11be with adaptive modulation & coding per resource unit (RU) a necessity.

Next-generation Wi-Fi networks are expected to have an ultra-dense deployment of access points (APs), thus, interference from overlapping basic service sets (OBSSs) poses challenges for interference management. Wi-Fi 8 aims at mitigating such interference using multi-access point coordination (MAPC). One of the MAPC variants is coordinated beamforming (Co-BF), where neighboring APs direct their signals towards specific users. Besides beam steering, APs can also perform null steering, which is more complex but can bring greater performance gains. In this paper, we present a centralized approach named intelligent null steering by reinforcement learning (IntelliNull), designed to reduce interference from neighboring transmitters by coordinated nulling while maximizing the signal quality at each station. We show that training the beam and null steering mechanism with a deep deterministic policy gradient (DDPG), it is possible to steer beams toward associated stations while intelligently nulling the most destructive interference from OBSS rather than nulling random interference directions. This method enhances communication between the AP and neighboring stations by reducing channel access contention, enabling transmissions at full power, and reducing worst-case latency. The proposed IntelliNull agent continuously adapts to changes in the network environment, including node mobility using channel state information (CSI) collected in real-time. We also compare our IntelliNull, which is based on beamforming plus nulling, with the baseline which is based on beamforming only. Our results demonstrate that IntelliNull outperforms the baseline by effectively mitigating interference, leading to higher throughput and better signal-to-interference-plus-noise ratio (SINR), especially in dense deployment scenarios where beamforming alone fails to sufficiently suppress OBSS interference.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks. In this context, the concept of vehicular micro clouds (VMCs) has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks. As many tasks in this application domain are time critical, offloading to the cloud is prohibitive. Additionally, task deadlines have to be dealt with. This paper addresses two main challenges. First, we present a task migration algorithm supporting deadlines in vehicular edge computing. The algorithm is following the earliest deadline first model but in presence of dynamic processing resources, i.e., vehicles joining and leaving a VMC. This task offloading is very sensitive to the mobility of vehicles in a VMC, i.e., the so-called dwell time a vehicles spends in the VMC. Thus, secondly, we propose a machine learning-based solution for dwell time prediction. Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC. Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya. Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions, advancing the state of the art in vehicular edge computing.

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as least privilege access, continuous verification, data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) methods. This paper addresses a variant of MAPC called coordinated spatial reuse (C-SR), where devices transmit simultaneously on the same channel, with the power adjusted to minimize interference. The C-SR scheduling problem is selecting which devices transmit concurrently and with what settings. We provide a theoretical upper bound model, optimized for either throughput or fairness, which finds the best possible transmission schedule using mixed-integer linear programming. Then, a practical, probing-based approach is proposed which uses multi-armed bandits (MABs), a type of reinforcement learning, to solve the C-SR scheduling problem. We validate both classical (flat) MAB and hierarchical MAB (H-MAB) schemes with simulations and in a testbed. Using H-MABs for C-SR improves aggregate throughput over legacy IEEE 802.11 (on average by 80% in random scenarios), without reducing the number of transmission opportunities per station. Finally, our framework is lightweight and ready for implementation in Wi-Fi devices.

The openness of wireless networks opens the door to Byzantine attacks on the physical channels, making the communications unreliable and resulting in more challenges in achieving consensus among mobile devices. To address this issue, this paper studies the Byzantine-fault-tolerant (BFT) consensus problem based on an unreliable Byzantine communication model. Different from the previous works requiring stable communications between the honest nodes, considering the unreliable communication makes our problem more realistic but also harder. Based on the unreliable communication model, we first implement a BFT abstract MAC (absMAC) layer with a distributed and randomized multi-channel communication algorithm. In the implemented absMAC layer, its acknowledgement and progress operations can be completed within$O\left ({{\frac {kn}{k-f}\log n}}\right)$and$O\left ({{\frac {k}{k-f}\log n}}\right)$rounds, respectively. n, f, and k are the numbers of nodes, Byzantine nodes, and channels, respectively. With the implemented absMAC layer, an efficient and elegant BFT consensus algorithm is designed, which can solve the binary consensus problem within$O\left ({{\frac {kn}{k-f}\log n}}\right) ^{^{^{^{}}}}$rounds in expectation. Even though a series of works have discussed how to achieve consensus with a specific absMAC layer provided, to the best of our knowledge, this paper is the first one that implements a BFT absMAC layer.

Multi-band splicing offers a promising solution to extend existing band-limited communication systems to support high-precision sensing applications. This technique involves performing narrow-band measurements at multiple center frequencies, which are then combined to effectively increase the bandwidth without changing the sampling rate. In this paper, we introduce a mmWave channel sounder based on multiband splicing, leveraging the sparse nature of wireless channels through compressed sensing and sparse recovery techniques for channel reconstruction. We focus on three sparse recovery methods: the widely used grid-based orthogonal matching pursuit (OMP) algorithm as a baseline, our newly developed two-stage mmSplicer algorithm, which extends the OMP method by introducing an additional stage for improving its performance for our application, and our adaptation of sparse reconstruction by separable approximation (SpaRSA), named Net-SpaRSA, optimized for wireless applications. All three algorithms are integrated into an experimental OFDM-based IEEE 802.11ac system. Our analysis centers on evaluating the performance of these algorithms under limited number of narrow-band measurements, demonstrating that accurate CIR estimation is achievable even using only 50% of the full wideband spectrum. Additionally, we analyze and compare the computational complexity of these algorithms to assess their practical feasibility

Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub-gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub.

Many emerging applications in edge computing require processing of huge volumes of data generated by end devices, using the freshest available information. In this paper, we address the distributed optimization of multi-user long-term average Age-of-Information (AoI) objectives in edge networks that use NOMA transmission. This poses a challenge of non-convex online optimization, which in existing work often requires either decision making in a combinatorial space or a global view of entire network states. To overcome this challenge, we propose a reinforcement learning-based framework that adopts a novel hierarchical decomposition of decision making. Specifically, we propose three different types of distributed agents to learn with respect to efficiency of AoI scheduling, fairness of AoI scheduling, as well as a high-level policy balancing these potentially conflicting design objectives. Not only does the proposed decomposition improve learning performance due to disentanglement of different design objectives/rewards, but it also enables the algorithm to learn the best policy while also learning the explanations – as actions can be directly compared in terms of the design objectives. Our evaluations show that the proposed algorithm improves the long-term average AoI by$200\%{-}300\%$and 400% compared to prior works with NOMA and the optimal solution without NOMA, respectively.

This article puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL). The new MP attack extends an adversarial variational graph autoencoder (VGAE) to create malicious local models based solely on the benign local models overheard without any access to the training data of FL. Such an advancement leads to the VGAE-MP attack that is not only efficacious but also remains elusive to detection. VGAE-MP attack extracts graph structural correlations among the benign local models and the training data features, adversarially regenerates the graph structure, and generates malicious local models using the adversarial graph structure and benign models' features. Moreover, a new attacking algorithm is presented to train the malicious local models using VGAE and sub-gradient descent, while enabling an optimal selection of the benign local models for training the VGAE. Experiments demonstrate a gradual drop in FL accuracy under the proposed VGAE-MP attack and the ineffectiveness of existing defense mechanisms in detecting the attack, posing a severe threat to FL.

As an integration of digital twin and edge computing, the digital twin edge networks (DITENs) have been proposed in recent years to fill the gap between physical edge networks and digital systems. Meanwhile, the multi-access wireless environments in edge computing make it hard to provide ultra-reliable and low-latency communications for digital twin, especially when the jamming attacks can be launched by the adversaries. This paper studies the jamming-resilient physical-to-virtual communication (PTVC) problem in DITENs despite strong cooperative jamming. Note that the previous jamming models mainly focus on the jamming behaviors from individual adversaries and are restricted by the energy budget limitation and uniform jamming assumption. In this paper, we consider a more comprehensive jamming model, in whichfadversaries can cooperatively launch their jamming attacks in totallykwireless channels with unlimited power budget and non-uniform jamming signals. Then, based on the new proposed$(k,f)$-cooperative jamming model, we show that$k\gt f$is the necessary and also sufficient condition to solve the PTVC problem. On one hand, we prove that the PTVC problem is insoluble when$f\leq k$; on the other hand, two distributed algorithms are given as the solutions of the PTVC problem amongnphysical objectives and one sink node when$k\gt f$, the time complexity of which are$O\left ({{\frac {n \log n}{k (\log k-\log f)}}}\right)$and$O\left ({{\frac {n \log n}{\log k-\log f}}}\right)$based on the communication modes with/without acknowledgement, respectively. Both of the theoretical results and empirical simulations are conducted to show the resilience of our algorithms despite such a strong cooperative jamming model.

In this paper, we study a stable Age-of-Information (AoI) scheduling problem to handle the massive packet aggregation from arbitrary total ofnend devices to an edge device via a single hop wireless channel when information constantly arrives at the end side. Specifically, we consider the arrivals of the information with anonlineinjection mode, i.e., information is injected to end devices with an unknown injection rate in each time slot. After the information is injected, the AoI with respect to those end devices constantly increases until their fresh messages are received by the edge device, which characterizes the freshness of the information at destination. Based on the online injection mode, we propose the first distributed stable AoI scheduling algorithm combining NOMA (Non-Orthogonal Multiple-Access) technique in this paper, to minimize the expected average peak AoI (EAP-AoI) of our end-to-edge information system. Adopting NOMA technique enableskmessages decoded from a mixed signal by the edge device in our algorithm, with parameter$k\gt 1$. We prove that our algorithm is stable even under the asymptotically maximum injection rate of$O(k/n)$that any stable AoI scheduling algorithm may handle, and the EAP-AoI of our information system is bounded by$O(\sqrt [{3}]{nk})$time slots under the injection rate of$O(k/n)$. Comparing with two existing results, the EAP-AoI in our algorithm is$O\left ({{\frac {n^{2/3}}{k^{4/3}}}}\right)$and$O\left ({{\frac {n^{2/3}}{k^{1/3}}}}\right)$times smaller. Numerical results also verify the stability and efficiency of our algorithm.

Adaptive beamforming is an enabling technology for millimeter-wave-based wireless communication which is used by many standards like 3GPP NR and IEEE 802.11ay. Supporting user mobility is challenging as efficient beam tracking is required. Therefore, a variety of beam tracking techniques have been proposed, many of which are machine learning (ML)-based. However, ML approaches are often not of practical use due to the long and complex learning phase. In this paper, we show the feasibility of virtual to real-world transfer learning. Our solution significantly speeds up the learning process as the learning happens mostly in a simulated environment and requires only little additional learning in the real-world deployment. As proof-of-concept, we implemented and evaluated a low-complexity beam tracking based on deep Q-network (DQN) reinforcement learning. The results reveal a substantial speed-up by a factor of 3× using transfer learning.

A crucial step for reducing emissions in the transport sector is the shift towards public transportation and bicycling. However, due to the lack of perceived safety, people are reluctant to commute by bicycle. A potential solution to this problem could be group cycling, allowing cyclists to form a group with others in order to ride together. Depending on local traffic laws, such groups allow for cycling next to each other and for special rights for intersection crossing. In this paper, we outline how group cycling commutes may be coordinated using communication capabilities of contemporary smartphones. We showcase how group cycling can reduce waiting times and, thus, improve ride comfort and safety in a simulation-based case study.

Resilient wireless networks ensure that the network maintains functionality despite adverse conditions like hardware and software failures, adversarial jamming attacks, or environmental changes like wireless channel fading which can disrupt connectivity. We show that Cross-Technology Communication (CTC), which was developed to enable direct communication between otherwise incompatible radio technologies, can be utilized to improve the resilience in wireless networks. This is achieved by increasing the number of independent wireless links between adjacent wireless nodes as well as by the increase in communication distance resulting in increased path diversity. Results from our analysis reveal that the same level of resilience can be achieved with reduced number of radio interfaces per node while tolerating an order of magnitude higher node failure rate. Moreover, such CTC-enabled networks are more resilient to advanced technology-specific jamming attacks.

6G mobile communication networks are envisioned to be AI-native, that is, the provision of AI services to users as well as the network itself would be one of the most essential aspect for its system architecture and design. To provide these services and in order to make correct design decisions, deep understanding of the relationship between the wireless networks and the different AI paradigms along with their various functional aspects is needed. In this paper, we explore the relationship in one such AI paradigm, namely federated learning (FL). More specifically, we aim to look at the problem of hyper-parameter optimization (HPO) for hierarchical federated learning by exploring two design scenarios: (1) HPO is carried out at intermediary aggregators which are implemented at edge servers and (2) HPO is realized by a global server such as a cloud-based approach. Empirical results show that although offloading the HPO algorithm completely to the edge-servers does lower the costs, the hybridized approach whereby the global server realizes the HPO algorithm and the edge-server offers some assistance, can provide at least twice as much savings on training costs. We further discuss the two approaches for providing FL as a native service from the mobile communication network and the implications on the network architecture in light of the discussed design scenarios.

LiFi has become an interesting and now more mature WiFi complement. To overcome the coverage limitations of LiFi and to create robustness against signal blockage, we use analog relays that cooperate with each other in both transmissions and signal reception. We present an ultra low-cost cascaded PLC-LiFi communication system comprising Power-Line Communication (PLC) for backhauling and Light Fidelity (LiFi) for wireless access. The interworking between the two technologies is achieved through low-complexity analog Amplify-and-Forward Relaying (AFR), i.e., the luminaries act as simple media converter. Results reveal the impact of amplification of AFR on the SNR as well as the relationship between the PLC and the optical wireless link length. Moreover, even so the gain from cooperative communication is substantial it is limited due to noise propagation in AFR.

Appropriate data rate selection at the physical layer is crucial for Wi-Fi network performance: too high rates lead to loss of data frames, while too low rates cause increased latency and inefficient channel use. Most existing methods adopt a probing approach and empirically assess the transmission success probability for each available rate. However, a transmission failure can also be caused by frame collisions. Thus, each collision leads to an unnecessary decrease in the data rate. We avoid this issue by resorting to the fine timing measurement (FTM) procedure, part of IEEE 802.11, which allows stations to perform ranging, i.e., measure their spatial distance to the AP. Since distance is not affected by sporadic distortions such as internal and external channel interference, we use this knowledge for data rate selection. Specifically, we propose FTMRate, which applies statistical learning (a form of machine learning) to estimate the distance based on measurements, predicts channel quality from the distance, and selects data rates based on channel quality. We define three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. Then, with a thorough performance evaluation using simulations and an experimental validation with real-world devices, we show that our approach has several positive features: it is resilient to collisions, provides near-instantaneous convergence, is compatible with commercial-off-the-shelf devices, and supports pedestrian mobility. Thanks to these features, FTMRate outperforms existing solutions in a variety of line-of-sight scenarios, providing close to optimal results. Additionally, we introduce Hybrid FTMRate, which can intelligently fall back to a probing-based approach to cover non-line-of-sight cases. Finally, we discuss the applicability of the method and its usefulness in various scenarios.

Abstract Coherent multi‐band splicing is an optimal solution for extending existing band‐limited communication systems to support high‐precision sensing applications. Conceptually, the communication system performs narrow‐band measurements at different centre frequencies, which are then concatenated to increase the effective bandwidth without altering the sampling rate. This can be done in parallel for multiple non‐contiguous subbands or by hopping across the different bands. However, multi‐band splicing poses significant challenges, particularly in compensating for phase offsets and hardware distortions before stitching the acquired samples, which can be distributed in contiguous or non‐contiguous manners. This survey paper studies the state of the art in coherent multi‐band splicing and identify open research questions. For beginners in the field, this review serves as a guide to the most relevant literature, enabling them to quickly catch up with the current achievements. For experts, open research questions that require further investigation are highlighted.

Precision medicine applications supported by nanotechnologies enforce designing a communication interface between in-body nanosensors and external gateways. Such a communication interface will enable both a data and a control channel between nanodevices operating within the human body and external control units. In this direction, recent literature focuses on deriving analytic channel models for intra-body links through the human tissues, including the analysis of achievable communication capacities in the terahertz band. A yet missing component, however, is a synchronization module to implement communication schemes in the intra-body link. Such synchronization module will ultimately bound the communication performance regarding the perceived signal to noise ratio (SNR) and bit error rate (BER), for instance. This paper contributes to the state of the art in two directions: (a) evaluating the bounds on the communication performance with the Cramer-Rao lower bound (CRLB) for the synchronization symbol timing offset (STO) and (b) designing a low-complex mechanism to synchronize communication. This analysis considers a communication link between external gateways located on the skin and nanosensor devices flowing in the human vessels. Using envelope and slope detectors, we devise a low-complex solution that relies on the received signal strength (RSS) metric to trigger data emissions. The method estimates the peak of the received RSS metric to ignite communication in the most favorable location, i.e., when the nanosensor is located at the shortest distance in the communication range with external gateways. Our findings illustrate the feasibility of such a low-complex synchronization method. Performance illustrates a BER less than 1×10−5for those nanosensors traveling close to the upper vessel wall.

The Open Radio Access Network (Open RAN) vision is based on the three principles of (i) open interfaces; (ii) cloudification; and (iii) automation through closed-loop control. It is a network architecture paradigm embodied and augmented through technical reference specifications of the 3GPP and the O-RAN Alliance. At the centre of Open RAN are open, programmable, and virtualized components, connected to each other through open interfaces that enable closed-loop, data-driven, and intelligent control. For instance, the O-RAN Alliance introduced two RAN Intelligent Controllers (or RICs) that connect through open interfaces to the disaggregated components of the RAN, and implement control loops that run at different time scales.

Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage. In this paper, we highlight the transformative potential of embracingnovel cellular architecturesby transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.

Platooning is a promising cooperative driving application for future intelligent transportation systems. In order to assign vehicles to platoons, some algorithm for platoon formation is required. Such vehicle-to-platoon assignments have to be computed on-demand, e.g., when vehicles join or leave the freeways. In order to get best results from platooning, individual properties of involved vehicles have to be considered during the assignment computation. In this paper, we explore the computation of vehicle-to-platoon assignments as an optimization problem based on similarity between vehicles. We define the similarity and, vice versa, the deviation among vehicles based on the desired driving speed of vehicles and their position on the road. We create three approaches to solve this assignment problem: centralized solver, centralized greedy, and distributed greedy, using a Mixed Integer Programming (MIP) solver and greedy heuristics, respectively. Conceptually, the approaches differ in both knowledge about vehicles as well as methodology. We perform a large-scale simulation study using PlaFoSim to compare all approaches. While the distributed greedy approach seems to have disadvantages due to the limited local knowledge, it performs as good as the centralized solver approach across most metrics. Both outperform the centralized greedy approach, which suffers from synchronization and greedy selection effects. The centralized solver approach however assumes global knowledge and requires a complex MIP solver to compute vehicle-to-platoon assignments. Overall, the distributed greedy approach achieves close to optimal results but requires the least assumptions and complexity. Therefore, we consider the distributed greedy approach the best approach among all presented approaches.

We investigate over-the-air federated learning (OTA-FL) that exploits over-the-air computing (AirComp) to integrate communication and computation seamlessly for FL. Privacy presents a serious obstacle for OTA-FL, as it can be compromised by maliciously manipulating channel state information (CSI). Moreover, the limited band at edge hinders OTA-FL from training large-scale models. It remains open how to enable a multitude of devices with constrained resources and sensitive data to collaboratively train a global model at band-limited edge. To tackle this, we design a novel algorithmPROBEbuilding upon a lightweight over-the-air gradients aggregation rulePB-O-GAR. Specifically,PB-O-GARcombines a random sparsification-like dimension reduction with Gaussian perturbation to provide rigorous privacy and band-adapted communication. It elaborately calibrates the transmission signal according to devices’ perceived CSI for heterogeneous power constraints accommodation and CSI attack resilience. We show that by utilizing the common randomness, which deviates from the conventional FL, random sparsification-like dimension reduction can augment privacy in addition to the intrinsic privacy amplification effect of AirComp. We establish near-optimal convergence rates and explicit trade-offs among privacy, communication and utility forPROBE. Finally, extensive experiments on benchmark datasets are conducted to validate our theoretical findings and showcase the superiority ofPROBEin realistic settings.

Federated learning is a fast-developing distributed learning scheme with promising applications in vertical domains such as industrial automation and connected automated driving. The heterogeneity of devices in data distribution, communication, and computation, when deployed in dynamic environments typically with wireless communication, poses challenges to traditional federated learning solutions, where successful learning depends on balanced contribution from participants. In this paper, we propose a flexible communication strategy for devices in asynchronous federated learning, which adapts the training and uploading actions based on the condition of the communication link. We propose a novel method of computing aggregation weight based on model distances and number of local optimizations, to control errors introduced in asynchronous aggregation while maximizing learning speed. We prove the convergence of the learning tasks analytically under the new scheme. The improved performance is rooted in the increased number of optimizations during training, which grows by 12% through opportunistically condensing model uploading during good link condition periods. By facilitating timely communication between devices and server, combined with the novel aggregation weight design, our method reduces the communication resources in dynamic environments by at least 5% while even slightly increasing the learning accuracy.

In this paper, we investigate the joint resource allocation and trajectory design for a multi-user, multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where the link capacity between a ground base station (BS) and the UAV is limited. The UAV conducts target sensing and information transmission in orthogonal time slots to prevent interference. As is common in practical systems, sensing is performed while the UAV hovers, allowing the UAV to acquire high-quality sensing data. Subsequently, the acquired sensing data is offloaded to the ground BS for further processing. We jointly optimize the UAV trajectory, UAV velocity, beamforming for the communication users, power allocated to the sensing beam, and time of hovering for sensing to minimize the power consumption of the UAV while ensuring the communication quality of service (QoS) and successful sensing. Due to the prohibitively high complexity of the resulting non-convex mixed integer non-linear program (MINLP), we employ a series of transformations and optimization techniques, including semidefinite relaxation, big-M method, penalty approach, and successive convex approximation, to obtain a low-complexity suboptimal solution. Our simulation results reveal that 1) the proposed design achieves significant power savings compared to two baseline schemes; 2) stricter sensing requirements lead to longer sensing times, highlighting the challenge of efficiently managing both sensing accuracy and sensing time; 3) the optimized trajectory design ensures precise hovering directly above the targets during sensing, enhancing sensing quality and enabling the application of energy-focused beams; and 4) the proposed trajectory design balances the capacity of the backhaul link and the downlink rate of the communication users.

Molecular communication (MC) is getting closer to becoming a next-generation communication technology with many applications in life sciences and other industrial applications. Multiple techniques have been proposed on how to design MC receivers depending on the channel characteristics. Experimentally, first testbeds also demonstrate the potentialities for communication using molecules as carriers. In this paper, we focus on developing a reinforcement learning (RL)-based receiver, targeting a realistic scenario with testbed measurements, and addressing transmitter mobility. Leveraging on reported solutions for machine learning (ML) methods, we demonstrate the usability of an RL agent to synchronize the receiver to the received signal. We evidence the learning capabilities of the agent to compensate for the impact of mobility, achieving a low probability of missed detection and small misalignment with the symbol time.

Offloading in edge computing scenarios is considered a prime solution to reduce computational time and also energy resources of the user equipment. This paper focuses on computation offloading from the user equipment to co-located vehicular microclouds. We derive the closed-form system metrics and cross-validate them with simulations. Through a comprehensive observation of vehicular microcloud behavior, the proposed two-state offloading algorithm (TSOA) provides the optimal offloading configuration in both planning and operating states. Finally, our evaluation demonstrates that the proposed TSOA performs optimally among the three offloading schemes.

Intrusion detection systems (IDS) represent a class of defensive security tools with the purpose of protecting the network from intruders in the network administrator’s arsenal. Despite the high precision of traditional signature-based IDS, its effectiveness is still under question due to the growth of a number of encrypted attacks and the volume of network traffic. This is considered one of the main motivations for researchers to develop anomaly-based IDS, which usually suffer from a higher false positive rate. In this paper, we propose and implement a lifelong-learning anomaly detection IDS (L-IDS) with the capability of the network environment’s adaption to limit the false positive rate of anomaly detector in the range of signature-based IDS. We consider Snort as a baseline and UNSW-NB15 as the ground truth in the evaluation of our proposal. We demonstrate how L-IDS achieves a higher level of precision in comparison with the existing signature-based IDS.

Signature-based Network Intrusion Detection Systems (NIDS) is state-of-the-art for precise attack detection. Using multiple instances of NIDS in parallel is considered the most promising solution for improving its processing speed in the scale of high speed network. This can be realized by (1) distributing the network traffic between multiple NIDS to reduce the network load per system or (2) distributing the signatures (rules) between multliple NIDS to reduce the work load per packet. In this paper, we study distribution strategies targeting application and transport layer for both traffic and rule distribution approaches. In addition, we investigate the importance of considering the processing speed optimization in the rule development phase. Our experiments show that in general traffic distribution performs slightly better in terms of packet drop and alert detection compared to rule distribution. The Transport layer distribution strategy shows traffic distribution parallelization detecting 1.6% more alerts and dropping 6% less packets. We also show that optimizing the rules sets further improves the processing speed significantly.

We present a systems approach for tracking of mobile systems in an avalanche scenario using an ultra-wideband (UWB)-based ranging and localization system. UWB-based positioning is particularly challenging in outdoor scenarios covering large distances in complex topography. In the long-term, we are interested in tracking the motion of snow avalanches; particularly their inner dynamics are still unknown since they remain hidden for most observation approaches. Our system model considers multiple anchors distributed with inter-node distances in the order of a few hundred meters. Mobile nodes, which move with the avalanche in the field, are tracked via UWB time-of-flight measurements and further supported by inertial measurement units. Our system further integrates LoRa and IEEE 802.11 mesh for configuration and management, with UWB for measurement and reporting. First measurement results in the field show good accuracy and confirm the design choice.

In the future communications, cell-free networks are considered very promising, owing to their robustness against inter-cell interference which degrades the user signal-to-interference-plus-noise-ratio (SINR). For an efficient network to achieve an enhanced capacity at a minimized power consumption, reconfigurable intelligent surfaces (RIS) are promoted as a key candidate 6G technology. In this paper, a joint precoding RIS aided cell-free Multiple-Input-Multiple-Output (MIMO) network is considered, where a weighted sum rate (WSR) maximization problem is formulated and decoupled into subproblems. A feasible solution is then obtained using fractional programming (FP). Finally, the performance of passive RIS and active RIS is then compared.

We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOMA) technique in the first phase. Subsequently, the UAV decodes and forwards this information to the remote device in the second phase. Our primary objective is to minimize the decoding error probability (DEP) at the remote device, which is influenced by the DEP at the UAV. To achieve this goal, we optimize the blocklength, transmission power, and location of the UAV. However, the underlying problem is highly non-convex and generally intractable to be solved directly. To overcome this challenge, we adopt an alternative optimization (AO) approach and decompose the original problem into three sub-problems. This approach leads to a sub-optimal solution, which effectively mitigates the non-convexity issue. In our simulations, we compare the performance of our proposed algorithm with baseline schemes. The results reveal that the proposed framework outperforms the baseline schemes, demonstrating its superiority in achieving lower DEP at the remote device. Furthermore, the simulation results illustrate the rapid convergence of our proposed algorithm, indicating its efficiency and effectiveness in solving the optimization problem.

Federated Learning is a fast-developing distributed learning scheme that has promising applications in vertical domains such as industrial automation and connected automated driving. In this paper we address the heterogeneity of the participation of devices in federated learning caused by: i) non-uniform distribution of local data; ii) uneven and varying computational resources across the devices; and iii) dynamic communication link. We propose a quasi-dynamic simulation scheme allowing realistic approximation of these three factors of heterogeneity. Aggregation schemes at the server based on the clients’ work status are implemented. We show that the new asynchronous aggregation algorithm does not require tuning of hyper-parameters such as the round time in synchronous federated learning and the aggregation weight in classic asynchronous aggregation, while providing better or comparable performance in terms of accuracy and convergence speed.

The recent move of the LoRa wireless technology towards the wider 2.4 GHz ISM spectrum band creates multiple opportunities for its broader usage in new IoT applications requiring larger data rates. However, this ISM band is already crowded with other wireless technologies like WiFi or Bluetooth, and cross-technology interference issues need to be considered. Moreover, the less favorable radio propagation properties in the 2.4 GHz band shorten the communication distance of LoRa transmissions and therefore make relaying necessary. Unfortunately, the direct usage of LoRa technology in a wireless mesh network is challenging due to the non-existent generic frame preamble and header, i.e. LoRa frames are not self-contained. This makes the dynamic adaptation of the modulation used by LoRa per link or even packet transmission challenging. We show in this paper that such limitation can be partially overcome by utilizing the macro spacial diversity provided by opportunistic routing (OR) protocols. In contrast to traditional routing OR selects a candidate set of nodes per hop along the path in the mesh network. Thus any temporal channel fading and local interference conditions will not result in a failed transmissions due to utilization of the spatial diversity provided by OR. The usage of OR achieves a much higher E2E packet delivery rate as well as lower E2E delay as compared to traditional routing while the overhead is reasonable. Moreover, with OR the same signal modulation can be used network-wide and its adjustment can be made on a long-term basis.

The pioneering field of tactile Internet (TI) will enable the transfer of human skills over long distances through haptic feedback. Realizing this demands a roundtrip latency of sub-5 ms. In this work, we investigate the capability of Wi-Fi 6 and existing TI scheduling/multiplexing schemes in meeting this stringent latency constraint. Taking the concrete example of the state-of-the-art video-haptic multiplexer (VH-multiplexer), we highlight the pitfalls of relying on the existing Wi-Fi 6 systems for TI communication. To circumvent this, we propose video-tactile latency scheduler (ViTaLS)—a novel link layer framework for tuning the video-tactile frame transmissions to suit their heterogeneous Quality of Service requirements. We present a mathematical model to characterize the packet transmission duration of ViTaLS. Using a custom simulator, we validate our model and measure the objective performance improvement of ViTaLS over VH-multiplexer. We also present ViTaLS-optimal—a variant of ViTaLS, for further 4 reducing the tactile latency. Objectively, we show that ViTaLS-optimal yields a latency improvement of up to 82 %. Based on experiments conducted on a real TI testbed, we subjectively demonstrate that ViTaLS-optimal outperforms the VH-multiplexer.

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

The standardization of the fifth-generation (5G) communications has been completed, and the visioning and planning of the sixth-generation (6G) communications have begun, with an objective of casting the high technical standard of new spectrum, high time and phase synchronization accuracy, and 100% geographical coverage to flexibly and efficiently connect upper trillion-level devices in the future. The transition from 5G to 6G is expected to integrate all operational networks, especially the Internet of Things (IoT), which involves massive heterogeneous devices to interact with our physical world.

The Internet of Vehicles (IoV) technology enables vehicles to communicate with each other, with pedestrians and with roadside infrastructures, to realize more efficient, safer and more environmentally friendly transportation. IoV also promises rich location-based services for vehicles, such as parking and toll highway. However, preserving privacy for location-based service payments emerges as a critical and challenging problem in IoV. Existing schemes rely on centralized banks for payment processing, resulting in location privacy leakage to centralized entities. In this paper, we propose a decentralized privacy-preserving payment scheme named Vehicloak for IoV based on the blockchain technology. The biggest challenge is to provide location privacy for vehicles while guaranteeing correct service payments using the transparent blockchain. To tackle this challenge, we introduce a new cryptographic technique called zk-GSigproof that integrates zero-knowledge proof with group signature. Vehicloak implements this technique in a smart contract to process payment, which verifies zero-knowledge proof and group signature without leaking location information. It is not limited to IoV and can be applied in many payment scenarios. To evaluate the performance of our scheme, we implement Vehicloak on a private blockchain of 100 nodes on Aliyun, and conduct a test with up to 4,000 transactions. The experimental results prove the feasibility of Vehicloak.

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version ofPlexe, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naïve approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments.

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies.

We presentHy-Fi, a system that aggregates light fidelity (LiFi) and radio frequency (RF)-based communication on the 802.11 (WiFi) physical layer by utilizing the MIMO capabilities in IEEE 802.11-compliant commodity WiFi chips.Hy-Fiis based on two key ideas. First, we use inexpensive commodity hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing from WiFi to aggregate LiFi and radio signals directly at the physical layer.Hy-Fiwas implemented as a prototype and evaluated in a small testbed. Experimental results reveal that our approach offers excellent robustness against signal fading, blockage and external interference in both, the optical and radio channels making it suitable for applications with very strict requirements to the packet delay and loss ratio. Moreover, the two channels, LiFi and RF, can be aggregated to double the link capacity in the best case. Finally, we demonstrate howHy-Ficould be used as wireless access technology in next-generation indoor enterprise networks providing both high capacity and seamless mobility.

In the field of vehicular networks, multiple approaches have been proposed to share and reuse data acquired by participating hosts. In this context, vehicular micro clouds extend the concept of Mobile Edge Computing (MEC) and bring data storage and processing to the vehicles, solving application tasks that need to be done in real-time. A critical point in shared computing tasks and storage is to keep all nodes synchronized and to maintain consistency. For the first time, we study the relevance of data versions in micro clouds offering intersection management service at four-way stop intersections and we investigate how different versions of data affect both road traffic and wireless communications. Our results validate the intuition that an increase in the amount of different data versions in the micro cloud has negative effects on both road traffic and wireless channel usage. Yet, the choice of the data sharing algorithm can make a huge difference and reduce these effects on a large scale. We found that synchronicity of data versions can be increased by up to 20% through small changes, e.g., keeping small amounts of data history in the applied algorithm.

Ultrasonic communication is one of the frequently discussed communication technologies for connecting in-body nanosensors with out-of-body gateway systems. Such communication has even been explored in various experimental setups. Yet, the impact of mobility remains primarily unexplored, which is critical for in-body applications. This paper aims to fill this gap by studying the impact of mobility on ultrasonic communication channels as the link between traveling nanosensors in the blood flow and external gateways fixed to the skin. We study both Doppler shift as well as signal gain both in an analytical model as well as in an extensive set of simulations. Our results indicate a significant impact of Doppler shift that needs to be compensated for real communication. We also show that the position of the nanosensors with respect to the gateway plays a very important role. Thus, we open the path for new research on protocol design for in-body to out-of-body communication using ultrasound.

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using Reynolds-averaged Navier-Stokes equations coupled with realizable$k-\epsilon$model and the discrete random walk model, respectively. Via the simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection which shows itself by the multimodal distributions as a weighted sum of normal and Weibull distributions.

In wireless powered sensor networks (WPSN), data of ground sensors can be collected or relayed by an unmanned aerial vehicle (UAV) while the battery of the ground sensor can be charged via wireless power transfer. A key challenge of resource allocation in UAV-aided WPSN is to prevent battery drainage and buffer overflow of the ground sensors in the presence of highly dynamic lossy airborne channels which can result in packet reception errors. Moreover, state and action spaces of the resource allocation problem are large, which is hardly explored online. To address the challenges, a new data-driven deep reinforcement learning framework, DDRL-RA, is proposed to train flight resource allocation online so that the data packet loss is minimized. Due to time-varying airborne channels, DDRL- RA firstly leverages long short-term memory (LSTM) with precollected offline datasets for channel randomness predictions. Then, Deep Deterministic Policy Gradient (DDPG) is studied to control the flight trajectory of the UAV, and schedule the ground sensor to transmit data and harvest energy. To evaluate the performance of DDRL-RA, a UAV-ground sensor testbed is built, where real-world datasets of channel gains are collected. DDRL-RA is implemented on Tensorflow, and numerical results show that DDRL-RA achieves 19% lower packet loss than other learning-based frameworks.

The recent move of LoRa towards the unlicensed 2.4GHz ISM spectrum band creates multiple opportunities for its broader usage in new IoT applications. However, the ISM band is already highly populated with diverse wireless systems, and interference issues are expected. We believe that cross-technology communication (CTC) can help to speed up LoRa adaptation and mitigate the coexistence problems. Therefore, in this paper, we introduce Wi-Lo, a novel signal emulation-based CTC approach, which allows a WiFi device to generate a valid LoRa waveform. Our scheme requires no hardware modifications but depends only on the careful selection of WiFi frame payload bits. We build our Wi-Lo prototype with commodity hardware. Our evaluation reveals that Wi-Lo enables reliable communication from WiFi to LoRa devices, which is comparable to the configurations using native LoRa nodes. Moreover, by leveraging the high link budget of LoRa, a long distance CTC can be established.

Recently, the growing popularity of Spatial Crowd-sourcing (SC), allowing untrusted platforms to obtain a great quantity of information about workers and tasks’ locations, has raised numerous privacy concerns. In this paper, we investigate the privacy-preserving task assignment in the online scenario, where workers and tasks arrive at the platform in real time and tasks should be assigned to workers immediately. Traditional online task assignments usually make a benchmark to decide the following task assignment. However, when location privacy is considered, the benchmark does not work anymore. Hence, how to assign tasks in real time based on workers and tasks’ obfuscated locations is a challenging problem. Especially when many tasks could be assigned to one worker, path planning should be considered, making the assignment more challenging. To this end, we propose a Planar Laplace distribution based Privacy mechanism (PLP) to obfuscate real locations of workers and tasks, where the obfuscation does not change the ranking of these locations’ relative distances. Furthermore, we design a Threshold-based Online task Assignment mechanism (TOA), which could deal with the one-worker-many-tasks assignment and achieve a satisfactory competitive ratio. Simulations based on two real-world datasets show that the proposed algorithm consistently outperforms the state-of-the-art approach.

Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.

Vehicular Micro Clouds (VMCs) are an emerging development in the domain of vehicular networks posed to provide local services to users without the need for external infrastructure. This can significantly improve the user experience, in particular due to the low latencies that such systems can achieve. Due to the distributed nature of such a VMC, effective local coordination is important while using minimal communication resources. To this end, it is important to know, how long vehicles will be participating in, and contributing to a VMC. In this work, we investigate, how previous, heuristic-based approaches can be improved by incorporating local, learning-based techniques. Our analysis indicates a potential improvement of the accuracy of the prediction, and resulted in an improved simulation environment within which the learning-based approach can be deployed.

The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). The most frequently used definition of AoI, however, does only account for the generation time of the data but not for application-specific aspects. In ITS, for example, the distance of vehicles is not considered and nodes farther away may experience an increased AoI due to effects of the wireless communication channel. We propose a new way of interpreting the AoI in such a context, also considering the location of the transmitting vehicle as a metric of importance to the information. In particular, we introduce a weighting coefficient used in combination with the peak age of information (PAoI) metric to describe the AoI requirement, emphasizing on packets from more important neighbors. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. We use the derived model to focus on timely updates of relevant vehicles for meeting a given AoI requirement, which can save resources on the wireless channel while keeping the AoI minimal.

The continuous growth of the urban population and the high development and maintenance costs of infrastructure-based approaches make it necessary the utilization of distributed schemes. Among distributed systems, there is an increasing interest in edge cloud models, both for vehicular and pedestrian applications. In this paper, we developed a distributed application based on the micro cloud concept formulated in the field of vehicular edge computing for spreading content items in an indoor pedestrian environment. Results highlight how it is possible to reach both 100% content items spread and up to 66% reduction over channel collisions.

Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSNs). Due to UAV’s maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor. This article proposes a new deep reinforcement learning-based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on deep deterministic policy gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices. To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to the existing deep reinforcement learning solutions.

Signature-based Network Intrusion Detection Systems (NIDS) have become state-of-the-art in modern network security solutions. However, most systems are not designed for modern high-speed network links. In the field of network monitoring, an alternative solution has become the choice for such high-speed networks. Flow-monitoring, typically based on the Internet Protocol Flow Information Export (IPFIX) standard, now goes well beyond collecting statistical information about network connections. Current solutions are even able to include selected parts of the payload in these Flows to be used in conjunction with NIDS. Recently, we extended this concept to application layer HTTP Flows. We now present our improved version of the IPFIX-based Signature-based Intrusion Detection System (FIXIDS).Fixidsmakes use of HTTP intrusion detection signatures from the popular Snort system and applies them to incoming IPFIX-conforming HTTP Flows. Our evaluation shows thatFixidscan deal with four times higher network data rates without drops compared to Snort, while maintaining the same event detection rate. Furthermore, a substantial part of the data traffic can be outsourced toFixidsso that Snort can be relieved of a significant portion of rules and traffic. This increases both the detection rate and the data rate the overall security appliance can handle.

Energy-efficient communication technologies are a key enabler for many IoT applications. Many existing communication protocols are based on duty-cycling techniques, that have an inherent tradeoff between delay and energy consumption. In the field of sensor networks, wake-up receivers have been investigated to overcome these problems and to further reduce energy consumption. We now go one step further and investigate the use of wake-up receivers in combination with IEEE 802.11 WLAN. We extend the protocol used to communicate between the access point and the client to introduce a wake-up signal. This can be implemented in a way that is fully compatible with existing wireless LAN (WLAN) standards, thus, it can be deployed gradually with little effort and no need to change existing systems. As a proof of concept and to perform first lab experiments, we developed a hardware prototype using a selective wake-up receiver and off-the-shelf USB-WLAN dongles. All experimental results are verified using an analytical model and a detailed simulation study. We show that our wake-up WLAN can provide connectivity for low-power devices with low delays and low energy consumption at the same time.

Thanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone’s maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new deep deterministic policy gradient based maneuver control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step. Numerical results demonstrate that the proposed DDPG-MC achieves 15.2 and 47.6 percent lower packet loss rate than deep Q-learning-based flight control and non-learning scheduling policies, respectively.

Nanomachines are envisioned for a variety of applications in the industry and health sectors operating as sensors and actuators. Considering their potential mobility, it is relevant to study the capability of nanomachines to cooperate in molecular communication scenarios. To this end, we provide new insights into the leader-follower dynamics when a mobile leader node moves randomly in three-dimensional space and emits molecules into a diffusive environment to send information about its position to a follower node. In this paper, we investigate the random distance between the two nodes due to decision errors at the follower and analyze an upper bound for the average distance as a function of time. Simulations are provided to validate our analytical results. Moreover, by comparing to the benchmark scenario of uncoordinated movement of leader and follower, we investigate for which parameters the follower can reliably follow the leader.

The industrial Internet of Things (IoT) is becoming a core component of future industrial automation supporting smart manufacturing systems. One of the main building blocks here is the continuous health monitoring of the working machinery. Very recently, the use of strobing light in combination with cheap low-Frames Per Second (FPS) camera has been suggested as a non-invasive but very accurate measurement technology. We combine the sensing task with data communication using the same LED light sources. Indoor Visible Light Communication (VLC) is already popular as it helps reducing load on the quite crowded RF spectrum. In this work, we not only make use of the on-phase of the LEDs producing the strobing light for VLC-based data communication but also combine it with Dark Light Communication (DLC) in the off-phase. Here, only very short light pulses are used for communication, which cannot be seen by the human eye. The challenge was to maintain a constant energy envelope of the light signal for the camera-based frequency detector. We present now a fully-featured Visible Light Communication and Sensing (VLCS) system for application, e.g., in industrial environments.

Ultra-low power communication is critical for supporting the next generation of battery-operated or energy harvesting battery-less Internet of Things (IoT) devices. Duty cycling protocols and wake-up receiver (WuRx) technologies, and their combinations, have been investigated as energy-efficient mechanisms to support selective, event-driven activation of devices. In this paper, we go one step further and show how WuRx can be used for an efficient and multi-purpose low power downlink (LPD) communication channel. We demonstrate how to (a) extend the wake-up signal to support low-power flexible and extensible unicast, multicast, and broadcast downlink communication and (b) utilize the WuRx-based LPD to also improve the energy efficiency of uplink data transfer. In addition, we show how the non-negligible energy overhead of conventional microcontroller based decoding of LPD communication can be substantially reduced by using the low-power universal asynchronous receiver/transmitter (LPUART) module of modern microcontrollers. Via experimental studies, involving both a functioning prototype and larger-scale simulations, we show that our proposed approach is compatible with conventional WLAN and offers a two-orders-of-magnitude improvement in uplink throughput and energy overheads over a competitive, IEEE 802.11 PSM-based baseline. This new LPD capability can also be used to improve the RF-based energy harvesting efficiency of battery-less IoT devices.

With improvements to ranges and a growing charging infrastructure, electric vehicles are becoming increasingly popular. However, many prospective owners can not charge their vehicle at home. They thus would have to use public charging infrastructure, which reduces the appeal of electric vehicles to them due to the extra effort and time spent during charging. Many previous works tried to enhance this situation by using intelligent charging station scheduling and/or route planning to reduce the time overhead. But most solutions are focused on long distance travel or single trips without considering the often regular schedule of drivers in urban areas. We propose a charge stop planner that takes into account the activities of a driver's daily schedule to minimize the additional time the driver has to spend charging the vehicle. The charging stops can either be en-route charging at fast charging stations between two activities or destination charging at slow charging stations. To reduce waiting times, we coordinate the charging station visits between the vehicles with a centralized service. In an extensive set of simulation experiments, we demonstrate that our approach reduces the additional time for charging by about 40 %, compared to only destination charging and only en-route charging. The charging station coordination further reduces the waiting time by about 50 %.

Autonomous UAV cruising is gaining attention due to its flexible deployment in remote sensing, surveillance, and reconnaissance. A critical challenge in data collection with the autonomous UAV is the buffer overflows at the ground sensors and packet loss due to lossy airborne channels. Trajectory planning of the UAV is vital to alleviate buffer overflows as well as channel fading. In this work, we propose a Deep Deterministic Policy Gradient based Cruise Control (DDPG-CC) to reduce the overall packet loss through online training of headings and cruise velocity of the UAV, as well as the selection of the ground sensors for data collection. Preliminary performance evaluation demonstrates that DDPG-CC reduces the packet loss rate by under 5% when sufficient training is provided to the UAV.

Vehicular Visible Light Communications (V-VLC) has emerged as a technology complementing RF-based Vehicle-toVehicle (V2V) communication. Indeed, such RF-based protocols have certain disadvantages due to the limited radio resources and the, in general, omnidirectional interference characteristics. Making use of LED head- and taillights, V-VLC can readily be used in vehicular scenarios. One of the challenging problems in this field is medium access; most approaches fall back to ALOHA or CSMA-based concepts. Thanks to modern matrix lights, VVLC can now also make use of Space Division Multiple Access (SDMA) features. In this paper, we present a novel approach for medium access in V-VLC systems. We follow a location-aware cross-layer concept, in which dedicated light sectors of matrix lights are used to avoid interference and thus collisions. We assess the performance of our protocol in an extensive simulation study using both a simple static scenario as well as a realistic urban downtown configuration. Our results clearly indicate the advantages of our location-aware protocol that exploits the space-division features of the matrix lights.

Simulation is a key tool in the development of wireless systems, protocols, and applications. This is especially true for large and mobile networks such as Vehicular Ad-hoc Networks (VANETs), as real-world experiments quickly become too expensive and complex with increasing numbers of nodes. However, accurate simulation of such wireless networks may take a long time to compute. This is mainly due to the sequential processing in event-based simulation cores like OMNeT++ or ns-3. This is especially troubling when debugging new protocols or when interfacing with Hardware in the Loop (HIL) or other real-time simulation. In this paper, we propose a new approach to accelerate simulation of wireless communication by utilizing asynchronous background computations. Expensive computations that can be isolated from the simulation kernel are started as early as possible and pushed to background threads. At the point in time when the result is needed, the computation may already be complete. This allows for parallelization without an explicit lookahead value. As a proof-of-concept, we implemented the concept in Veins, a state-of-the-art VANET simulator. By offloading wireless signal attenuation model computation to the background, we achieve speedups of up to 3.5, depending on the scenario.

One of the major challenges in indoor vehicular visible light communications (VVLC) links is associated with the mobility leading to frequent handovers, consequently resulting in increased latency and processing time. Therefore, to address this challenge, an acceptable light coverage within the indoor channel, i.e., cell planning, is of paramount importance. In this paper we investigate the performance of a VLC link for an autonomous indoor vehicle and provide insights on the impact of the distance between light access points, and the light client’s (LC’s) angular field of view (AFOV) employing an imaging optical concentrator with a photodiode. We derive a mathematical expression to determine the optimum AFOV of the LC as a function of other system parameters such as the vertical link distance and incidence angles. Results show step responses in the bit-error-rate (BER) performance as the incidence angle increases outside the AFOV of the LC with the BER degrading sharply from 3.8 × 10−3to 4.4 × 10−1with a small increment in the incidence angle over 7.35 °.

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.

With the emerging 5G solutions for vehicular networking, the spectrum of radio communication technologies also extends into the mmWave band. This is further supported by the recent move towards RADar based COMmunication (RADCOM), i.e., the deeply integrated use of the 77 GHz band for communication and sensing. mmWave communication has widely been explored both analytically as well as in experiments, particularly for indoor usage and semi-stationary outdoor scenarios. In this paper, we explore the capabilities of existing simulators for the vehicular use case. We selected WinProp using deterministic ray-tracing techniques and NYUSIM relying on stochastic simulation of the channel. We compare both with a strong focus on simulation accuracy, usability, and computational performance.

The use of millimeter wave communication gets more and more attention with the growth of 5G, particularly with regard to promising applications like platooning or intersection assistant. However, for the design of those applications, a clear understanding of the channel behaviour at these frequencies is mandatory. Aiming to fill the gap of missing channel models for frequencies above 60 GHz, this paper investigates the channel characteristics necessary for future vehicular applications. Based on the commercially available channel simulation tool WinProp, Altair, a typical vehicular scenario in the city center of Berlin (Germany) is analysed. The simulation results are verified with channel sounding measurements at 60 GHz. Hereby, the dependency of the channel with respect to scatterers like trucks and cars is evaluated. Reflection paths were found to be up to 30 dB higher in received power compared to scattering ones. Further, a platooning scene, consisting of 20 vehicles driving towards each other while communicating, is investigated for various time steps and receiver points. In this context, different antenna types are introduced to reduce interfering signals nearby the major transmitter, achieving a decrease in interference power of up to 20 dB.

Full-Duplex (FD) relay systems have the potential to overcome various system-inherent deficiencies of existing infrastructure relays. In contrast to traditional Half-Duplex (HD) relaying systems, in-band FD relays can simultaneously receive and forward, which improve the channel utilization and reduces the end-to-end delay in a multi-hop network significantly. Despite many theoretical works on FD relaying, there is still no widely available implementation that is compliant with standards such as IEEE 802.11a/g/p. Building upon our previous work, we present such an implementation using the GNU Radio framework. In this paper, we present the performance comparison results with our IEEE 802.11a/g/p compliant relay implementation from a system-level perspective by means of over-the-air experiments in an indoor setup. The first results demonstrate the potential of our Full-Duplex Relay (FDR) implementation and the practical performance in comparison to HD relaying in terms of packet delivery ratio, and physical layer latency.

Listen-Before-Talk (LBT) is an essential function of many MAC protocols and a key mechanism of sharing spectrum in an uncoordinated manner. However, this simple but effective concept remains a major challenge for Software-Defined Radios (SDRs). If performing the protocol stack on a host PC, due to their structure, SDRs have inherent latencies built in. These latencies increase their reaction time, the so-called turnaround time, significantly, compared to their conventional radio counterparts. Unfortunately, this means that they cannot comply with the LBT channel access procedures used in modern protocols like IEEE 802.11 or LTE Licensed-Assisted Access (LTE-LAA). Given the flexibility and rapid-prototyping capabilities of SDRs, these protocols could clearly benefit from such SDR-based implementations. In this paper, we fill this gap and present a design approach for SDRs supporting LBT. We particularly focus on the rather complicated timing issues. As a proof-of-concept, we showcase our LBT-enabled implementation of the srsLTE software stack for LTE.

We present Hy-Fi, a system which combines light fidelity (LiFi) and radio based on the WiFi physical layer waveform by using the MIMO features available in IEEE 802.11-compliant commodity chip sets. Hy-Fi is based on two key ideas. First, we use inexpensive COTS hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing to aggregate LiFi and radio signals at the physical layer. The system was implemented as a prototype and evaluated in a small testbed. Experimental results show that our approach offers robustness against signal blockage (Shadowing) and external interference in both, the optical and RF channels. Moreover, the two media (LiFi and WiFi) can be aggregated to double the capacity in the best case.

Dynamicity is one of the most challenging, yet, key aspects of wireless networks. It can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. In this work, we propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model makes use of the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted by most existing research. Under the proposed dynamic model, we develop an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhnet al.[24]. Our solution paves the way for many new fast algorithms to solve high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulation studies indicate that our algorithm exhibits good performance in realistic environments with dynamic network behaviors.

Visible light communications (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor vehicular VLC (V-VLC) network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor V-VLC networks. We have developed a small-scale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for low-outage duration handovers in V-VLC. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions.

In this paper, we investigate distributed broadcasting in dynamic networks, where the topology changes continually over time. We propose a network model that captures the dynamicity caused by both churn and mobility of nodes. In contrast to existing work on dynamic networks, our model defines the dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a local dynamic model suits distributed algorithms better than a global one. The proposed dynamic model uses the more realistic SINR model to depict wireless interference, instead of oversimplified graph-based models adopted in most existing work. We consider the fundamental communication primitive of global broadcast, which is to disseminate a message from a source node to the whole network. Specifically, we present a randomized distributed algorithm that can accomplish dynamic broadcasting in an asymptotically optimal running time of$O(D_{T})$with a high probability guarantee, under the assumption of reasonably constantdynamicity rate, where$D_{T}$is thedynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. We believe our local dynamic model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks.

The byzantine model is widely used to depict a variety of node faults in networks. Previous studies on byzantine-resilient protocols in wireless networks assume reliable communications and do not consider the jamming behavior of byzantine nodes. Such jamming, however, is a very critical and realistic behavior to be considered in modern wireless networks. In this paper, for the first time, we integrate the jamming behavior of byzantine nodes into the network setting. We show that, in this much more comprehensive and harsh model, efficient distributed communication protocols can be still devised with elaborate protocol design. In particular, we developed an algorithm that can accomplish the basic multiple-message dissemination task close to the optimal solution in terms of running time. Empirical results validate the byzantine-resilience and efficiency of our algorithm.

Next generation intelligent transportation systems aim at many cooperative perception and cooperative driving functions that need significant computational resources. Offloading such tasks to some mobile edge computing solutions is considered part of the solution, which is currently investigated in the scope of 5G networks. In the automotive context, such edge systems could be road-side units (RSU), which, however, can easily be overloaded at peak times. Vehicular micro-cloud approaches have been proposed to overcome such problems by sharing computational resources of nearby cars. In this study, we propose an offloading system architecture to enable such offloading such vehicular micro-cloud interconnected by a 5G core network. We model the system as a queueing model to derive closed-form solutions for selected performance metrics. Based on these insights, we propose the Double-Check Offloading Algorithm (DCOA) to obtain the best offloading ratio to the vehicular micro-cloud. Our simulation results show the proposed DCOA has better system performances compared with four other offloading schemes.

Vision-based techniques for advanced monitoring of industrial systems is gaining more attention recently. A good example is the use of cheap low frame rate cameras in combination with strobing light to non-intrusively measure high frequency vibrations. At the same time, we see an uprise of Visible Light Communication (VLC), particularly for indoor usage. In this paper, we bridge the gap between these two concepts and propose an integrated communication and sensing system based on strobing light. Our system can reproduce the same signal envelope while VLC is in operation, thus, hiding communication from the camera-based sensing. We explored the performance of the system using a GNU Radio implementation. Our results demonstrate the huge potentials of this integrated communication and sensing concept.

As the radio spectrum has become the bottleneck resource with increasing volume of mobile data and ultra-dense network deployments, it is crucial to use spectrum more flexibly in time, space, and frequency dimensions. However, higher efficiency in spectrum usage facilitated by flexible spectrum allocation comes with a cost, namely the increased complexity of spectrum monitoring and management. Identifying the transmitters is at the interest of particularly spectrum enforcement authorities to ensure that spectrum is used as intended by the legitimate users of the spectrum. For a scalable, efficient, and highly-accurate operation, we propose a crowd-sensing based solution where sensing devices report their measured receive power levels to a central entity which later fuses the collected information for localizing an unknown number of transmitters. Our solution, referred to as DeepTxFinder, leverages deep learning to handle many sources of uncertainty in the operation environment: namely number of transmitters, their transmission power levels, and channel conditions (shadowing). Using deep-learning, DeepTxFinder distinguishes itself from the prior state-of-the art which requires knowledge of the number and transmission power of transmitters or require the transmitters to be well separated in space by tens to hundreds of meters making them ill-suited for application in expected ultra-dense deployment of small-cells. Moreover, we propose a tiling-based approach to increase the scalability of our proposal by reducing the computational complexity. Our simulation studies show that DeepTxFinder can provide a high detection accuracy even only by collecting data from a very small number of sensors. More specifically, with 1 %-2 % sensor density DeepTxFinder can estimate the number of transmitters and their locations with high probability which proves that sparse sensing is feasible.

Commercial off-the-shelf (COTS) IEEE 802.11ac WiFi systems only use a low number of antennas. This limits the multi-user MIMO (MU-MIMO) performance and, hence, the throughput of such systems, especially in dense environments. We present a unique solution based on hybrid digital-analog (HDA) beamforming to overcome the limitation of COTS WiFi hardware and effectively increase the MU-MIMO gain and to unlock the potential of MU-MIMO in WiFi. We use COTS WiFi hardware in combination with a self-developed beamforming module and novel algorithms for the HDA approach, leveraging the MU-MIMO precoding of the WiFi system. The implemented control and signal processing software is fully transparent to the WiFi part, i.e., no protocol changes are needed. We demonstrate the increase in MU-MIMO gain using unmodified COTS end-user terminals.

In this paper, power allocation problem for cooperative THz MIMO-NOMA system is studied to maximize the minimum achievable rate of the available users. The cooperative system consists of a base station (Bs) that communicates directly with a nearby user, while a relay node (RN) is used to help the Bs to communicate with the far user. The RN operates in HD and FD modes using DF protocol. The Bs is equipped with multiple antennas, while both the relay and the user nodes are equipped with single antenna. The optimization problem for power allocation is solved analytically and validated numerically.

We present a flexible real-time software-based multi-band channel sounder, which can flexibly be used in a variety of measurement campaigns, and fills the gap of expensive commercial channel sounders with limitations of operation frequency, sounding bandwidth, and ease of use. In general, channel sounding is a very valuable tool for designing wireless communication systems. Even though commercial channel sounders allow for thorough and detailed measurements, they are typically very expensive. In contrast, existing software-based solutions are often limited in scope and complexity of measurements. Our system closes this gap. Using GNU Radio and USRP radio front-ends, the system provides a high degree of freedom with respect to supported carrier frequencies and bandwidth. We also support features such as automatic spectrum-sweeping and transmitter gain management for dynamic channel characterization. We can use the system for both live visualization of the channel characteristics, e.g., Channel Impulse Response, Channel Frequency Response, Power Delay Profile, and Channel Phase Response, as well as for fully automated and repeatable experiments.

Every year, approximately 350 000 vulnerable road users (VRUs) still lose their lives in road traffic accidents world-wide. To reduce this number, cooperative VRU collision avoidance systems aim at complementing current vehicle or infrastructure-based systems for VRU protection. The cooperative approach assumes user equipment (UE) like a smartphone or a smartwatch on the VRU side, which allows to obtain and exchange movement and contextual information with other road users for determining the current risk level for a collision. In recent publications, the usage of additional contextual information, such as the pedestrian's current activity (e.g., standing, walking or crossing a curb), has been shown to improve the collision detection accuracy. However, those approaches focused on the detection accuracy, but did not investigate how time delays for both detection and communication of VRU contexts affect the ability to detect collisions. We fill this gap by investigating the influence of both activity recognition and communication delays on the collision detection performance. As a baseline, we use the standardized Euro NCAP test protocol. For the evaluation, we exemplarily consider the usage of a curb detection module for position correction, which improves the collision detection accuracy at the moment the pedestrian crosses the curb.

We investigate a novel approach of utilizing modern Adaptive Front-Lighting System (AFS) to reduce multiuser interference in Vehicular Visible Light Communications (V-VLC) by applying Space-Division Multiple Access (SDMA). V-VLC is a promising technology that can complement Radio Frequency (RF) communications to fulfill the high reliability and low latency requirements of Intelligent Transportation Systems (ITS). To achieve this, however, limitations of bandwidth efficiency and multiuser interference need to be addressed. A possible solution is to take advantage of SDMA, which allows spatial reuse of the resources and a simple but efficient multiple access method. State-of-the-art AFS, consisting of multiple controllable Light Emitting Diodes (LEDs) with sharply separated radiation patterns, offer a great opportunity to implement SDMA. Therefore, we investigate the applicability of an AFS-based SDMA system for V-VLC and compare its communication performance with a common LED-based high beam. Simulation results show that our system can be used to reduce multiuser interference in V-VLC.

In the last decade, infrastructure relays have been adopted by wireless standards, such as WiMAX and LTE, to substantially enhance the coverage and performance of wireless systems. These relays operate in a half-duplex mode, which not only increases the overall latency but also causes spectral losses. In contrast, an in-band full-duplex relay could effectively cope with these issues by simultaneously receiving packets from source and forwarding them towards destination. This becomes particularly interesting given the recent advancement in self-interference suppression techniques. In this work, we present an SDR-based real-time implementation of a full-duplex decode and forward relay in GNU Radio. Based on open-source and programmable hardware and software, the implementation is completely transparent and can be studied in all details and modified if needed. With an extensive set of experiments, we validate the practical performance of the proposed relay system, and measure achievable throughput gains. Our results demonstrate and underline the huge advantage of switching from the classical half-duplex relaying to full-duplex relay systems.

We present a complete Visible Light Communication (VLC) system for experimental Vehicular VLC (V-VLC) research activities. Visible light is becoming an important technology complementing existing Radio Frequency (RF) technologies such as Cellular V2X (C-V2X) and Dedicated Short Range Communication (DSRC). In this scope, first works helped introducing new simulation models to explore V-VLC capabilities, technologies, and algorithms. Yet, experimental prototypes are still in an early phase. We aim bridging this gap with our system, which integrates a custom-made driver hardware, commercial vehicle light modules, and an Open Source signal processing implementation in GNU Radio, which explicitly offers rapid prototyping. Our system supports OFDM with a variety of Modulation and Coding Schemes (MCS) and is compliant to IEEE 802.11; this is in line with the upcoming IEEE 802.11 LC standard as well. In an extensive series of experiments, we assessed the communication performance by looking at realistic inter vehicle distances. Our results clearly show that our system supports even higher order MCS with very low error rates over long distances.

We study reliable communication in heterogeneous sensor networks. In many application scenarios, more energy-constrained (mobile) nodes are distinguished from more powerful base stations (or ground nodes). Wildlife monitoring is just one of many examples within the Internet of Things research community. In order to improve the communication reliability (and, thus, also the energy footprint), these ground nodes often apply macro-diversity to reduce transmission failures and to avoid costly retransmissions. In recent years, the concept of using distributed sensor networks as antenna arrays for receive diversity has been proposed. We address one of the key challenges in such networks, which is the huge cost of forwarding signal samples to a (central) sink through the ground network, where diversity algorithms are eventually applied. In particular, we present two algorithms, a cluster and a tree based one, that help reducing the data transfers in the ground network. Ground nodes try applying diversity techniques early whenever they receive signal samples from multiple receivers. In extensive simulations, we show that the algorithms substantially outperform naïve centralized solutions.

Road traffic is continuously increasing worldwide and Vulnerable Road Users (VRUs)such as bicyclists are ever more susceptible to injuries from crashes. Vehicular Ad-hoc Network (VANET)technologies in combination with Advanced Driver Assistance Systems (ADAS)are currently being evaluated to enhance road traffic safety. The same technology is now also considered for warning cyclists about imminent crashes. Prototypes for such VANET -enabled bicycles already exist, however, the primary problem here is verification and test due to immediate safety concerns for the participants. Computer simulation is considered a more feasible alternative; however, realistic behavior models of VRUs are not available in the required quality and quantity. We developed a virtual cycling environment to address this issue, allowing to study and record cyclists' behavior. We collected (and make available)traces from a variety of subjects cycling towards an intersection for three different safety critical scenarios. As proof of concept, we then used these traces in our vehicular networking simulation framework to assess the impact of a simple VANET solution on the cyclists' safety. Our results demonstrate the need for such an integrated framework for empirical studies as well as for simulation-based exploration of system configurations.

Vehicular micro clouds are considered a prime building block for next generation Intelligent Transportation Systems (ITS)also supporting a variety of Information and Communication Systems (ICT)applications in smart cities. Such micro clouds are established by multiple cars equipped with communication, storage, and computational resources. We recently presented the concept of hierarchical vehicular cloud computing, which is meant to extend on Mobile Edge Computing (MEC). Based on clustering algorithms, we can set up and maintain such micro clouds and eventually make use of the distributed resources. Looking at the high mobility of cars, it is very difficult to reliably maintain data collected by cars at a given location in space that is geographically relevant, e.g., at intersections. In this paper, we propose a new protocol which encourages coordination between neighboring micro clouds to help keeping local data current, i.e., cars moving out of the micro cloud may take data to neighboring clouds, hand data over to cars moving towards the original micro cloud, and, thus, returning the data to its original geographical location. We evaluate the performance of the protocol with different vehicle densities in a Manhattan Grid scenario and our results show the benefits of our proposed inter micro cloud coordination protocol.

The trend toward autonomous driving and the recent advances in vehicular networking led to a number of very successful proposals in cooperative driving. Maneuvers can be coordinated among participating vehicles and controlled by means of wireless communications. One of the most challenging scenarios or applications in this context is cooperative adaptive cruise control (CACC) or platooning. When it comes to realizing safety gaps between the cars of less than 5 m, very strong requirements on the communication system need to be satisfied. The underlying distributed control system needs regular updates of sensor information from the other cars in the order of about 10 Hz. This leads to message rates in the order of up to 10 kHz for large networks, which, given the possibly unreliable wireless communication and the critical network congestion, is beyond the capabilities of current vehicular networking concepts. In this paper, we summarize the concepts of networked control systems and revisit the capabilities of current vehicular networking approaches. We then present opportunities of Tactile Internet concepts that integrate interdisciplinary approaches from control theory, mechanical engineering, and communication protocol design. This way, it becomes possible to solve the high reliability and latency issues in this context.

Recent works have shown that it would be beneficial for nodes in wireless networks with very dynamic topology to maintain a list of 2-hop neighbors, namely, the neighbors of its neighbors. This is important, for example, for routing, clustering, and message dissemination to all the nodes in a given geographic vicinity. In this paper, we propose a scheme that uses Bloom filters for maintaining 2-hop neighborship information. Furthermore, we developed a novel 2-hop broadcast algorithm making use of the specific nature of our Bloom filter encoded neighbor information. We particularly focus on the Vehicular Ad Hoc Networks (VANETs) application scenario. Here, beaconing is a periodic broadcast of awareness messages by each vehicle to its immediate neighbors. A na€ıve approach would be to include all 2-hop neighbors in each beacon message, which, however, would work only for small or sparse scenarios. We show that our approach significantly reduces the length of the beacon messages, thereby keeping channel load and collision probability considerably lower than in the na€ıve scheme. We further demonstrate the application of our Bloom filter based 2-hop neighbor table for developing higher layer protocols and introduce a multi-hop broadcast protocol called Bloom Hopping.

Recent advances in the field of sensor networks helped miniaturizing nodes and, thus, enabling novel application domains for, e.g., remote monitoring or more generally the Internet of Things (IoT). One of the most critical issues in this context is the limited energy capacity of a sensor node, and its implication on communication reliability. Recently, we have investigated the use of diversity combining in a distributed sensor network to improve both the communication reliability and the energy footprint of mobile sensors transmitting to stationary receiver nodes. In this work, we go one step further and integrate the idea of preamble-less communication proposed by mSync with diversity combining to gain the added advantage of both. The elimination of preambles allows to further minimize the transmission energy. We implemented the proposed integrated model in GNU Radio and evaluated its performance both in simulations as well as lab experiments. Our results demonstrate that applying diversity combining together with mSync not only reduces the energy required for transmission but also improves the communication performance in terms of Packet Delivery Ratio (PDR), especially at higher Signal-to-Noise Ratio (SNR).

Signature-based Network Intrusion Detection Systems (NIDS) are the state-of-the-art when it comes to precise attack detection and intrusion prevention. However, they experience critical performance problems in modern high-speed networks. At the same time, flow-based network monitoring has been investigated for high data rates. In the last years, such flow-monitoring went beyond collecting statistical information about network connections and more recent techniques are able to include selected samples of the payload of these flows. Most recently, we extended this concept to HTTP flows. We now go one step further and combine IPFIX-based flow monitoring with NIDS. We developed IPFIX-based Signature-based Intrusion Detection System (FIXIDS), a system that exploits the recently introduced HTTP related flow Information Elements (IEs) to do signature-based flow intrusion detection in high-speed networks on commodity hardware. FIXIDS makes use of HTTP intrusion signatures from the widely used Snort NIDS and applies them to incoming IPFIX Flows. In the experimental evaluation, we are able to show a performance gain of a factor of three compared to Snort while maintaining the same detection ratio.

The emerging concept of vehicle cloudification is a promising solution to deal with ever-growing computational and communication demands of connected vehicles. A key idea is to have connected vehicles in the vicinity form a cluster, called vehicular micro cloud, and collaborate with other cluster members over vehicle-to-vehicle (V2V) networks to offer data processing, data storage, sensing and communication services. It allows us to use vehicles as virtual edge servers that complement traditional cloud and physical edge servers in the backbone network. In this paper, we design a mechanism to intelligently schedule where and when to form such vehicular micro clouds. A remote server maintains statistics of the amount of available on-board computational resources spatio-temporally, and analyzes these statistics to identify the locations where vehicles can consistently offer a sufficient amount of resources for service provisioning. The results from our proof-of-concept simulations show that our system can significantly reduce the risk of resource scarcity in vehicular micro clouds.

One of the most recent applications in the vehicular networking domain is distributed data processing using cars as sensors of information. In recent work, the concept of vehicular cloud computing has been explored to provide the necessary scalability and to improve the communication between clusters of cars being called vehicular micro clouds and other participants (cars, bicyclists, pedestrians). In order to provide a bigger picture and also to interconnect such micro clouds, data centers or cloud servers are considered bridging the gap. We study the uplink capabilities from connected cars to such data centers. Options include direct LTE uplinks from all cars, selected use of Roadside Units (RSUs) with back-end connectivity or LTE uplinks from the vehicular micro clouds, and finally hybrid solutions taking network quality and available channel resources into account. Our findings clearly show the advantages of such hybrid solutions both in terms of throughput as well as of optimizing operational costs.

We present a novel physical layer frame format and a corresponding decoding strategy for energy-constraint single-carrier transceivers, often used in sensor networks and cyber-physical systems. The main advantage of our approach is that decoding does not rely on dedicated preamble symbols, which usually introduce considerable overhead in terms of energy consumption and utilization of the wireless channel. We show that omitting the preamble can be achieved by buffering the signal in the receiver and processing the samples twice; first to synchronize and in a second iteration to decode the actual data. To introduce our approach, we provide a theoretical description, including a discussion of the implications of synchronizing on data symbols instead of optimized preamble sequences. The practical feasibility of the algorithm is shown by simulations and experiments using prototype implementations based on software defined radio. We implemented our algorithm for two technologies, a custom ultra low-power BPSK transceiver and the O-QPSK physical layer of the IEEE 802.15.4 standard. Finally, we present an extension of the algorithm that allows us to reduce the buffered data to a small constant number of samples, making our algorithm applicable to physical layers independent from their maximum frame size.

We present a complete simulation and experimentation framework for IEEE 802.11p. The core of the framework is a Software Defined Radio (SDR)-based Orthogonal Frequency Division Multiplexing (OFDM) transceiver that we validated extensively by means of simulations, interoperability tests, and, ultimately, by conducting a field test. Being SDR-based, the transceiver offers important benefits: It provides access to all data down to and including the physical layer, allowing for a better understanding of the system. Based on open and programmable hardware and software, the transceiver is completely transparent and all implementation details can be studied and, if needed, modified. Finally, it enables a seamless switch between simulations and experiments and, thus, helps to bridge the gap between theory and practice. Comparing the transceiver's performance with independent results from simulations and experiments, we underline its potential to be used as a tool for further studies of IEEE 802.11p networks both in field operational tests as well as for simulation-based development of novel physical layer solutions. To make the framework accessible to fellow researchers and to allow reproduction of the results, we released it under an Open Source license.

A major building block of Vehicular Ad Hoc Networks (VANETs) is broadcasting: the use of wireless communication for sharing information among vehicles, or between the vehicles and infrastructure. Dozens of broadcast protocols have been developed in recent years, including protocols for 1-hop broadcasting of vehicle status information (beaconing) and for geocasting-based applications. However, most of these protocols were designed for one application and cannot co-exist, nor can one broadcast solution meet the demands of all applications. These observations motivated our effort to develop a holistic network layer for VANETs. We identify the need for making VANET broadcast context-aware, and for supporting four different classes of broadcast protocols, each with its own properties. These classes are not only able to co-exist on the same network layer, but also to complement one another's functionality. Thus, large applications as well as more holistic Transport protocols can be designed by combining two or more broadcast classes. We discuss the specific characteristics of these classes and design candidate protocols for each class.

We investigate the impact of realistic light radiation pattern on Vehicular VLC (V-VLC) based on an empirical study for typical car headlight and taillight modules. Our research community identified visible light as a communication technology that has a number of advantages to radio communication. Besides the huge spectrum that is fully unlicensed, the technology allows for very high data rates and reduced interference from parallel communications. After seeing a number of breakthroughs in indoor environments, the vehicular networking community got interested in the technology for a number of reasons as well. First successful applications have been reported but practical evidence is still limited. This is due to the use of very abstract simulation models and limited field testing as of now. We aim at bridging this gap by developing a realistic simulation model based on empirical data from a measurement campaign. Based on this model, we particularly investigate the impact of the light radiation pattern of typical car light modules on the communication performance. We can report that the angle and distance of the communicating cars have a significant impact and need to be carefully considered for developing V-VLC applications.

Receive diversity increases the reliability and robustness of ultra-low power Wireless Sensor Networks (WSNs) by using spatially separated antennas without modifying the physical layer. We consider a distributed ground network in the wild to track bats in their natural habitat. The bats are equipped with a sensor node of only 2 g that limits the energy budget available for communication. In this work, we exploit the distributed nature of the ground network to employ diversity combining, i.e., we use the ground nodes as a geographically distributed multi-antenna array. However, this causes several research challenges given the limited bandwidth between nodes and the need for accurate synchronization to combine signal copies constructively at a central node. Sending all signal samples from all ground nodes to the central node is prohibitive due to the required data rates in the network. As a novel concept, we propose a system that only forwards selected signal samples belonging to a packet with high probability. We study the performance in simulations as well as in a testbed using a Software Defined Radio (SDR) prototype implementation. Our results clearly indicate a substantial performance gain while keeping the data rate in the ground network in a feasible range.

Signature-based Network Intrusion Detection Systems (NIDS) are an integral part of modern network security solutions. They help to detect and prevent network attacks and intrusions. However, they show critical performance problems in today's high speed networks. Filters have been proposed to reduce the amount of traffic to be analyzed by a NIDS, yet, such filters need to be very carefully designed in order not to miss relevant data. We address this problem by proposing a novel concept for filtering taking into account the pipelining architecture of modern web traffic. Our concept, which we named HTTP-based Payload Aggregation (HPA), is able to retain the first N bytes of the basic Protocol Data Unit (PDU) of an application layer protocol and discard the rest, arguing that the retained payload portion contains almost all relevant data for intrusion detection. We demonstrate the feasibility of our approach focusing on HTTP traffic as the most prominent protocol in many Internet applications. The idea is, thus, to capture the first N bytes of every pipelined session and forward this data to a NIDS. In our evaluation, we show that for the used traces we still detect more than 97% of the events with only 2.5% of the network traffic to be analyzed. We achieve an increase in packet throughput of up to 44 in our experiments.

We present LAN Radio, an Open Source prototyping system to couple simulation and real world for the wireless channel of IEEE 802.11p based communication. Our main focus is the use in a vehicular networking environment, where field testing and real world experimentation is becoming more relevant these days. Large Field Operational Tests (FOTs) are expensive and difficult to handle in terms of reproducibility and controllability -- particularly for application development and integration tests for automotive Electronic Control Units (ECUs). To support such experiments, we developed an approach to integrate a real system to test into a large scale simulation scenario without the need to change the physical and access layer parts of the communication stack of this system. We closely followed the Hardware In The Loop (HIL) simulation concept but also integrated a wireless communication channel into the picture. LAN Radio is building upon the well established and Open Source development platform OpenWRT to provide optimal extensibility.

We present OpenC2X, an Open Source approach to field testing of vehicular networking solutions. Field Operational Test (FOT) and real-world experimentation are becoming more relevant to our research community as well as to industry and regulation. Unfortunately, available commercial solutions make experimental modifications to the protocol stack time consuming or prohibit it completely. To overcome this limitation, we implemented the ETSI ITS-G5 stack on a standard embedded PC hardware and running Linux system. OpenC2X is the first complete Open Source experimentation and prototyping platform. Our system is fully interoperable to commercial solutions, yet easily extensible with new protocols and applications for vehicular networks.

Diversity combining is a popular technique to in- crease the robustness of wireless communications. Multiple independent paths are needed for successful combining to recover a signal; which is particularly helpful in fading scenarios. Typically, this is achieved using multiple, sufficiently spaced apart antennas at a single receiver. We consider a Wireless Sensor Network (WSN) to monitor bats in the wild by equipping them with sensor nodes weighing only 2g and, therefore, having very tight energy budgets. A distributed ground network is used to receive the signals from bats at multiple nodes. We propose to exploit the distributed nature of these receivers to cooperatively decode the received signal. This scenario poses a number of research challenges related to the necessary synchronization of the receivers as well as the very limited energy budget at the nodes. To optimize link utilization in the ground network, we study the performance of low-complexity soft-bit diversity and unequal gain combining for robust packet-based communication. To assess the performance of diversity combining strategies, we conduct simulations and measurements using MATLAB as well as a Software Defined Radio (SDR)-based prototype. Finally, our application-specific diversity techniques are adapted to provide a more general solution.

The papers in this special section focus on the Internet of Things for use in smart cities and urban informatics. CITIES around the world are currently under quick transition toward a low-carbon environment, high quality of living, and resource-efficient economy. Smart systems are crucial components for building sustainable cities and aggregating comprehensive urban informatics. Internet-of-Things (IoT) is an emerging technology evolved from the convergence of wireless technologies, embedded systems, and the Internet. Extensive research on IoT is taking place on sensing and automatic control, network infrastructure and communication, and big data analytics. This facilitates a multidisciplinary approach for developing integrated solutions and creating novel applications to build a sustainable society. Combinations of IoT with autonomous systems, machine intelligence, sensor and actuator networks provide smart functions in wide application domains, including environmental monitoring, smart transportation, industrial production, and security surveillance. This special section on Internet-of-Things for smart cities and urban informatics is focused on the development, adoption, and application of IoT technology to improve the efficiency of cities and enhance urban services in society. It covers topics including smart transportation, smart buildings, data transmission and analytics, system designs, and experiments of IoT for smart cities.

Advances in miniaturization of sensor nodes enable a wide range of novel application scenarios. At the same time, however, this miniaturization drastically reduces the energy available for communication. We focus on wildlife monitoring applications for bats, which set a weight limit of 2 g for the sensor node including the battery. Here, the protocol design is complicated by the need to recharge a capacitor before each communication attempt. For communications with ground stations, wake-up receivers are used that inherently help mitigate synchronization demands and to provide a superframe structure. We study the not obvious choice of transmission slots within these synchronized superframes. Our findings clearly indicate that slotted access outperforms simple random channel access. Well-planned TDMA schedules only bring little gain compared to random slot selection.

The efficient usage of multiple channels allocated for vehicular communications is deemed crucial to support the increasing spectrum demand of current and upcoming Intelligent Transportation Systems (ITS). In this context, an accurate modeling of adjacent channel interference (ACI) is needed to better understand its adverse effects on simultaneous communications on different channels. In this paper, we modeled ACI in the simulation framework Veins by strictly following the standard specifications in what concerns spectral emission masks. The model was validated in a set of experiments using off-the-shelf chipsets and a software defined radio. We then studied ACI effects at the physical and packet levels in multi-channel vehicular networks through extensive simulations. Our results clearly indicate that slightly larger delays and significantly lower reliability can be experienced when ACI is modeled. Insights are also provided about the usability of the devised ACI simulation model in future studies.

Network monitoring is the basis to analyze communications for preventing network attacks and misuse. Common practice for today's high throughput networks are flow-based network monitoring techniques like IPFIX. However, these solutions do not support the observation of HTTP/1.1 request/response dialogs. At the same time, we observe an increasing trend to use HTTP pipelining in the Internet as well as the use of HTTP as a generic transport protocol. IPFIX-based network monitoring and attack detection is no longer able to identify misbehavior in the complex and often interlaced HTTP dialogs. This work presents a new monitoring concept, which is able to aggregate HTTP/1.1 dialog information into bidirectional IPFIX flows. It is thereby laying the foundation for network security tools like IDS to process the resulting flows faster and more efficiently. The evaluation shows that the implemented parsing mechanism can even deal with complex HTTP traffic and clearly outperforms existing solutions.

Computational offloading from smartphones into the cloud has proved to be one of useful approaches for improving energy efficiency. To assess the benefit of offloading and decide the most suitable offloading strategies, it is essential to account the energy consumption of smartphones. In this paper, we investigate the accuracy and capabilities of the Android smart battery interface. For comparison, we measure the energy consumption using an oscilloscope. We experimentally investigate the energy consumption of different applications on a modern smartphones including local computation and network communication over WiFi. Our results show that both of methods bring high accuracy. Our work builds the basis for next generation offloading algorithms.

Localization and tracking of small animals in the wild using sensor networks require nodes with ultra-low power consumption, which are particularly challenging to design. Here, we target the tracking of bats in their natural habitat and have to limit the weight of the mote to 2g. To optimize the energy consumption in this scenario, the combination of data communication and ranging is essential. The limitations of the platform and the specific use case ask for a dedicated signal design. We start exploring the use of Binary Offset Carrier (BOC), which is known to be well suited for localization. In this paper, we concentrate on the data communication part of the system. We develop a BOC transceiver in Software Defined Radio (SDR) and perform simulations as well as lab measurements to evaluate its performance and compare it to Binary Phase-Shift Keying (BPSK), which is often used in low-power sensor systems. Most importantly, we conducted realistic field measurements to study the effects of multipath fading and shadowing. Our results clearly show that BOC is perfectly suited for ultra-low power communication in forest environments.

Duty cycling has been the main concept for saving energy in sensor networks for a long time. Yet, additional overhead for synchronization and the fact that overhearing and idle listening cannot completely be prevented, motivated further research. Wake-up receiver, i.e., additional ultra-low power radios that are not switched off and can receive a so called wake-up signal, aim to fill this gap. The wake-up signal can either be a simple energy burst that wakes up all nodes in reception range or can include an address to wake up individual nodes. In this paper, we introduce a novel addressing scheme for wake-up receivers that supports broadcast, multicast, and unicast wake-ups, and which can be realized in hardware with little additional complexity at the receiver. We implemented a prototype based on a commercially available wake-up receiver to show the feasibility of our approach. We discuss the tradeoff between the length of the wake-up signal and the possible energy savings and show an application example for sending software updates to sensor nodes in an energy saving way.

An Intersection Assistance System aim to assist road users in avoiding collisions at intersections, either by warning the driver or by triggering automated actions. Such a system can be realized based on passive scanning only (e.g., using LiDAR) or supported by active Inter-Vehicle Communication (IVC). The main reason to use Inter-Vehicle Communication (IVC) is its ability to provide situation awareness even when a possible crash candidate is not yet in visual range. The IVC research community has identified beaconing, i.e., one-hop broadcast, as the primary communication primitive for vehicular safety applications. Recently, adaptive beaconing approaches have been studied and different congestion control mechanisms have been proposed to cope with the diverse demands of vehicular networks. In this paper, we show that current state-of-the-art congestion control mechanisms are not able to support IAS adequately. Specifically, current approaches fail due to their inherent fairness postulation, i.e., they lack fine grained prioritization. We propose a solution that extends congestion control mechanisms by allowing temporary exceptions for vehicles in dangerous situations, that is, situation-based rate adaptation. We show the applicability for two state-of-the-art congestion control mechanisms, namely Transmit Rate Control (TRC) and Dynamic Beaconing (DynB), in two different vehicular environments, rural and downtown.

Last encounter routing (LER) is an excellent routing paradigm that exploits distributed mobility diffusion to achieve both moving object tracking and packet networking services in dynamic mobile networks. From our observations, we discover that LER can be easily extended to a mobile DHT protocol that introduces excellent performance to high-speed mobility environments. This is of particular interest when higher level of mobility and membership dynamics go hand in hand. In our simple but powerful DHT paradigm, a data publish/look-up process consists of a sequence of spatial motion tracking of the rendezvous node that is responsible for the data resource. Thus, we name the protocol MX-DHT (Motion-MiX-DHT). As opposed to existing topology based DHT schemes, MX-DHT does not require additional management of logical or overlying look-up topologies, except for the one-hop encounter records of logical metadata carried by mobile nodes. Therefore, in high-speed mobility and dynamic membership environments, MX-DHT achieves a significant reduction in the communication costs of the publish/look-up and join/leave operations as compared to existing mobile DHT schemes. An extensive set of experiments showed that MX-DHT is a cost-effective solution to providing a content centric networking service in different types of networks with dynamic mobility and membership changes.

We explore the advantages of using Erasure Codes (ECs) in a very challenging sensor networking scenario, namely, monitoring and tracking bats in the wild. The mobile bat nodes collect contact information that needs to be transmitted to stationary base stations whenever they are in communication range. We are particularly interested in improving the overall communication reliability of the wireless communication. The mobile nodes are capable of storing a few 100kB of data and to exchange contact information in aggregated form. Due to the continuous flight of the bats and the forest environment, the wireless channel quality varies quickly and, thus, the communication is in general assumed to be highly unreliable. Given the very strict energy constraints of the mobile node and the inherently asymmetric channels, conventional techniques such as full data replication or Automatic Repeat Request to improve the communication reliability are prohibitive. In this work, we investigate the tradeoff between reliability achieved and the cost in form of additional transmissions, that is, the additional energy costs. Our energy measurements on a real platform combined with larger-scale simulation of the wireless communication clearly indicate the advantages of using ECs in our scenario. The results are also applicable in other configurations when unreliable communication channels meet tight energy budgets.

We present a novel concept for a wake-up system based ultra-low power communication protocol for sensor networks. The main application field is monitoring contacts and even tracks of bats in the wild. Our sensor nodes can weigh at most 2 g out of which 1 g remains for the battery. We investigate a novel communication protocol design applicable to these systems and also showing great potentials for other ultra-low power sensor networks. In particular, we investigate the bat to ground communication by combining duty cycling with a multi-stage wake-up receiver. We employ Binary Offset Carrier (BOC) modulated signals that allow to accurately localize and track the bats while transmitting data in parallel. In a first step, we evaluated the conceptual design using a software-defined radio system to demonstrate the feasibility of the protocol design.

We study the effect of radio signal shadowing dynamics, caused by vehicles and by buildings, on the performance of beaconing protocols in Inter-Vehicular Communication (IVC). Recent research indicates that beaconing, i.e., one hop message broadcast, shows excellent characteristics and can outperform other communication approaches for both safety and efficiency applications, which require low latency and wide area information dissemination, respectively. To mitigate the broadcast storm problem, adaptive beaconing solutions have been proposed and designed. We show how shadowing dynamics of moving obstacles hurt IVC, reducing the performance of beaconing protocols. To the best of our knowledge, this is one of the first studies on identifying the problem and the underlying challenges and proposing the opportunities presented by such challenges. Shadowing also limits the risk of overloading the wireless channel. We demonstrate how these challenges and opportunities can be taken into account and outline a novel approach to dynamic beaconing. It provides low-latency communication (i.e., very short beaconing intervals), while ensuring not to overload the wireless channel. The presented simulation results substantiate our theoretical considerations.

Many people use smart phones on a daily basis, yet, their energy consumption is pretty high and the battery power lasts typically only for a single day. In the scope of the EnAct project, we investigate potential energy savings on smart phones by offloading computationally expensive tasks into the cloud. Obviously, also the wireless communication for uploading tasks requires energy. For that reason, it is crucial to understand the trade-off between energy consumption for wireless communication and local computation in order to assert that the overall power consumption is decreased. In this paper, we investigate the communications part of that trade-off. We conducted an extensive set of measurement experiments using typical smart phones. This is the first step towards the development of accurate energy models allowing to predict the energy required for offloading a given task. Our measurements include WiFi, 2G, and 3G networks as well as a set of two different devices. According to our findings, WiFi consumes by far the least energy per time unit, yet, this advantage seems to be due to its higher throughput and the implied shorter download time and not due to lower power consumption over time.

Performance evaluation of communication protocols is usually carried out using typical network metrics as delay, jitter, or goodput. However, recent studies in the context of Inter-Vehicle Communication (IVC) have shown that using these metrics is not sufficient for evaluating vehicular safety applications. To highlight the importance of safety metrics and their applicability, we extended our existing simulation framework Veins to visualize these metrics live while the road traffic and network simulation are running in parallel. In particular, we demonstrate the impact of communication on intersection assistance applications. To simulate different intersection approaches, we implemented a simulation model that resembles different kinds of driver behavior and enables crashes at intersections. The resulting situations are displayed in the road traffic simulator and give the visitor insights on the current state of endangered vehicles. Furthermore, an autonomous controller has been implemented which tries to avoid accidents and hence shows the real-world impact, i.e., accidents can be avoided using advanced beaconing techniques. To increase interactivity of the demo, visitors will have the possibility to interact with and take control over endangered vehicles.

We study the ability of Inter-Vehicle Communication (IVC) solutions to handle real-time requirements in safety scenarios using beaconing as a communication primitive. One of the envisioned safety applications is intersection assistance. The objective of such applications is to either warn the driver or even to act autonomously if other approaching vehicles endanger the vehicle. Fairness, combined with aggressive channel access for low-latency safety messages, has been one of the main research line according to which state of the art congestion control mechanisms have been developed. We show that these solutions are not able to sufficiently support intersection assistance applications. Specifically, we show that current approaches fail exactly due to their fairness postulation. We propose a new situation-aware solution to this fairness dilemma by allowing temporary exceptions for vehicles in dangerous situations. We show the applicability for two state of the art congestion control mechanisms, namely ETSI Transmit Rate Control (TRC) and Dynamic Beaconing (DynB). Our investigation also reveals important research objectives for future IVC protocols, namely how much reactivity and situation-awareness is needed in the highly dynamic environment of vehicular networks.

We study the effectiveness of Inter-Vehicle Communication (IVC) in urban and suburban environments at low node densities, with a particular focus on cooperative awareness and traffic safety. The recently standardized DSRC/WAVE protocol suite defines a platform for such applications, which mainly focus on beaconing, i.e., periodic 1-hop-broadcast. In general, such safety relevant transmissions are defined by time criticality. One of the major problems to be solved is how to tackle the very difficult and complex radio signal attenuation due to buildings and other obstacles, especially in cities. Typical concepts address this problem by requiring all vehicles to also act as relays or by using dedicated Roadside Units (RSUs). We show how such systems may be operated more efficiently and how the situation can be further improved by relying on parked vehicles in addition to, or as a replacement for, RSUs. Given the fact that the U.S. DOT is already evaluating whether to make DSRC mandatory for new cars, wide availability of radio equipped cars can be predicted; also the impact in terms of energy consumption is negligible. We performed an extensive set of simulations to evaluate the negative impact of buildings at low node densities and the benefit of our proposal. Our results clearly indicate that situation awareness can be significantly improved. When disseminating safety critical events in a realistic scenario, reasonable numbers of parked cars can increase cooperative awareness by up to 25%, a factor which requires an unreasonably costly number of RSUs. To the best of our knowledge, we are the first to propose the utilization of parked vehicles as relay nodes for safety applications in vehicular networks.

We study the effect of radio signal shadowing dynamics, caused by vehicles and by buildings, on the performance of beaconing protocols in Inter-Vehicular Communication (IVC). Recent research indicates that beaconing, i.e., one hop message broadcast, shows excellent characteristics and can outperform other communication approaches for both safety and efficiency applications, which require low latency and wide area information dissemination, respectively. We show how shadowing dynamics of moving obstacles hurt IVC, reducing the performance of beaconing protocols. At the same time, shadowing also limits the risk of overloading the wireless channel. To the best of our knowledge, this is the first study identifying the problems and resulting possibilities of such dynamic radio shadowing. We demonstrate how these challenges and opportunities can be taken into account and outline a novel approach to dynamic beaconing. It provides low-latency communication (i.e., very short beaconing intervals), while ensuring not to overload the wireless channel. The presented simulation results substantiate our theoretical considerations.

Security and privacy requirements in vehicular networks are typically addressed using a Public Key Infrastructure (PKI) and pools of pseudonymous certificates for each vehicle. Messages are signed with these certificates, so that misbehaving vehicles can be excluded from the network by disseminating Certificate Revocation Lists (CRLs). We present SmartRevoc, a novel revocation architecture, to solve the main challenges of CRL distribution in vehicular networks: CRL size, dissemination speed, and preservation of location privacy. Using two hash chains, we compute CRLs on vehicles, substantially reducing communication overhead. At the same time our design is privacy preserving, preventing the linking of past pseudonyms to revoked ones. We additionally utilize parked vehicles to epidemically disseminate CRLs and show that even few parked vehicles can outperform an unreasonably high number of Roadside Units (RSUs). Our simulation results clearly indicate that SmartRevoc is a major step forward toward more efficient and secure vehicular networks.

Chirp Spread Spectrum (CSS) based localization techniques are becoming more attractive as they provide improved localization accuracy and robustness compared to WiFi or ZigBee based approaches. However, a remaining problem is the necessary update frequency: In existing CSS based localization systems, the positions of the objects are determined one by one via unicast with nearby anchors instead of using broadcasts. We propose a faster distributed localization scheme for CSS based systems. A portion of nodes are considered cluster heads; they determine the locations of un-localized nodes by dynamically increasing the transmission power. Our novel scheme not only fully utilizes the spatial redundancy, which is crucial for speeding up the localization process. By also allowing to establish new anchors in a two-hop range, we can further increase speed without significantly influencing localization error. The performance of the proposed method is demonstrated through simulation.

We just released an Open Source receiver that is able to decode IEEE 802.11a/g/p Orthogonal Frequency Division Multiplexing (OFDM) frames in software. This is the first Software Defined Radio (SDR) based OFDM receiver supporting channel bandwidths up to 20MHz that is not relying on additional FPGA code. Our receiver comprises all layers from the physical up to decoding the MAC packet and extracting the payload of IEEE 802.11a/g/p frames. In our demonstration, visitors can interact live with the receiver while it is decoding frames that are sent over the air. The impact of moving the antennas and changing the settings are displayed live in time and frequency domain. Furthermore, the decoded frames are fed to Wireshark where the WiFi traffic can be further investigated. It is possible to access and visualize the data in every decoding step from the raw samples, the autocorrelation used for frame detection, the subcarriers before and after equalization, up to the decoded MAC packets. The receiver is completely Open Source and represents one step towards experimental research with SDR.

Self-deployment describes the task of spreading an autonomously moving swarm of mobile robots over a given area. All these robots have to move to locations such that the set of robot locations satisfies a desired property. In this work, we describe a fully distributed deployment algorithm executed locally at each robot. The approach requires only few local information per node: the distances and very coarse angular information to immediate neighbors. It has been developed for use on small robots with very restricted memory, communication, and processing capabilities. In this paper, we specify the algorithm and evaluate it in an empirical study. This includes both simulation studies and real test bed experiments. For the test bed, we consider two different platforms: ground moving robots and aerial robots. The results of our simulations show that our local deployment rules achieve almost globally optimal results. The test bed study supports and substantiates our simulation study and shows as a proof of concept that our algorithm works both with real ground based and aerial based robot swarms.

Nano communication is one of the fastest growing emerging research fields. In recent years, much progress has been achieved in developing nano machines supporting our needs in health care and other scenarios. However, experts agree that only the interaction among nano machines allows to address the very complex requirements in the field. Drug delivery and environmental control are only two of the many interesting application domains, which, at the same time, pose many new challenging problems. Very relevant communication concepts have been investigated such as RF radio communication in the terra hertz band or molecular communication based on transmitter molecules. Yet, one question has not been considered so far and that is nano communication security, i.e., will it be possible to protect such systems from manipulation by malicious parties? Our objective is to provide some first insights into the security challenges and to highlight some of the open research challenges in this field. The main observation is that especially for molecular communication existing security and cryptographic solutions might not be applicable. In this context, we coin the term biochemical cryptography that might lead to significant improvements in the field of molecular communication. We also point to relevant problems that have similarities with typical network architectures but also completely new challenges.

Indoor hovering objects such as quadrotors need to be controlled continuously to hold their position in space. In order to support fully autonomous flights of these copters, the necessary position control including all related information transfers have to be provided in a fully decentralized and autonomous manner. We discuss challenges related to flight control based on our Autonomous Localization Framework (ALF), which provides scalable and decentralized localization in GPS-denied areas. Using a sensor network based on the IEEE 802.15.4 communication protocol, continuous position maintenance is feasible but, unfortunately, in no way stable. Therefore, we introduce a low-cost sensor array, which reduces the system dynamics and allows a robust position control of the platform.

Inter-protocol interference is one of the most critical issues in wireless communication. For example, this becomes extremely problematic in environments where robustness and real-time communication need to be considered, e.g., in industrial automation or health care applications. Recently, possible approaches for interference mitigation have been described in the literature assuming that the interferer is known in advance. We contribute to this line of research with a framework for interferer detection and classification. Essentially, we use a simple IEEE 802.15.4 transceiver as for example used on the TelosB sensor motes to scan the 2.4 GHz ISM band. This band is used by different technologies including Bluetooth, WiFi, and cordless phones. The key challenge is the accurate timing of the scanning of the frequency band. The presented framework supports flexible descriptions of such scan jobs allowing to adapt to the detectors requirements, depending on the interfering protocols.

Automated platooning is one of the most challenging fields in the domain of ITS. Conceptually, platooning means creating clusters of vehicles which closely follow each other autonomously without action of the driver, neither for accelerating, nor for braking.

Content access and downloading in vehicular environments is expected to heavily rely on the availability of roadside infrastructure. Although vehicle-to-vehicle communication is foreseen, data will mostly flow through roadside access points, or RSUs (RoadSide Units), which suffer from less connectivity problems. However, at least in the early stages of deployment, the RSU coverage will be spotty, or limited to main avenues in urban areas. In this paper, we try to address such shortcomings by investigating the possibility of exploiting parked vehicles to extend the RSU service coverage. Our approach leverages optimization models aiming at maximizing both the freshness of the content that downloaders retrieve and the efficiency in the utilization of radio resources. Performance evaluation highlights that the use of parked vehicles enhances the benefits of the content downloading process and leads to a significant offload of the RSUs, with respect to the case where only mobile relays are used.

In the scientific community, Inter-Vehicle Communication (IVC) protocols are frequently evaluated using simulation techniques, often using variants of WiFi stacks instead of IEEE 802.11p, which constitutes the basis for the new DSRC/WAVE standard. We discuss the necessity of using accurate WAVE models based on an extensive set of simulation experiments using: an IEEE 802.11b model, an IEEE 802.11b model tweaked to work in the same frequency range and using similar timings like IEEE 802.11p, as well as a fully featured channel hopping WAVE model. Even though, intuitively, the use of the different protocols will lead to a different network behavior, to the best of our knowledge, there has been no qualitative and quantitative evaluation or comparison of both worlds. According to our results, we can conclude that the simple WiFi model may indeed be used, but only for extremely sparse scenarios - this is exactly what has been validated using field tests. In denser traffic scenarios there is a significant deviation of the protocol behavior between WiFi (and its adapted variant) compared to WAVE. Thus, especially in dense scenarios, the application behavior is strongly influenced if simulated with the wrong model - leading to unrealistic results.

Beacon-synchronized operation allows wireless networks to operate very efficiently, in particular focusing on energy requirements. It is, however, unclear, to what extend this beacon-based synchronization suffers in case of independently operating networks with overlapping communication ranges. We study the effects and implications of beacon collisions in co-located IEEE 802.15.4 networks. This protocol has become the de-facto standard in many areas of wireless communications including wireless sensor and body area networks, but also in industrial network domains. IEEE 802.15.4 defines a node synchronization strategy using beacons for achieving robust and real-time capable low energy communication. Nonetheless, collisions are inevitable as some packets, e.g., the synchronization beacons indicating the start of a new superframe, are sent without carrier sensing. Consequently, co-located networks may substantially suffer from beacon collisions. In an extensive set of simulation experiments of co-located networks, we found that the number of lost beacons is independent of the amount of superframe overlap, but is a major cause of performance degradations.

Many safety applications in Intelligent Transportation Systems (ITS) require vehicles to be aware of the presence of nearby cars, but - especially in urban and suburban regions - buildings and other obstacles may block radio transmissions. In the literature, multi-hop relaying by neighboring cars has been demonstrated to perform well at disseminating safety broadcasts in the presence of obstacles. At night, in areas with low traffic density, or when the penetration rate of Car-2-X devices is low, however, there are likely to be too few relaying cars available. This again leads to the problem that vehicles which are not in line-of-sight frequently cannot be sensed either. To the best of our knowledge, we are the first to help overcome this problem by utilizing parked cars as relay nodes. We study the effectiveness and the necessity of this approach with the help of extensive simulative studies and real life experiments. We show how, for scenarios with few equipped cars, the utilization of parked cars proves crucial to support safety applications. When disseminating safety critical events in a realistic scenario, parked cars can increase cooperative awareness by over 40% in total.

Virtual position-based routing protocols have many attractive characteristics for wireless sensor networks. Typically, such protocols use a proactive scheme for updating routing tables. Because sensor networks can have very low data rate, sending periodic beacons to update routing tables can be very expensive. Instead, reactive approaches might be more appropriate in such scenarios. MANET-inspired reactive routing protocols do not scale well because of the effort in the order of O(n) for each routing information update. In this paper, we present Reactive Virtual Cord Protocol (RVCP), a data-centric reactive virtual position based routing protocol for use in sensor networks. Route discovery is directed towards the destination and hence there is no need to flood the entire network to discover a route. Our approach is based on Virtual Cord Protocol (VCP), an efficient, virtual relative position based routing protocol that also provides support for data management as known from typical Distributed Hash Table (DHT) services. To minimize the end-to-end delay and energy consumption, we used adaptive techniques for the development of RVCP.

We present a fully self-organizing approach for creating and maintaining a reference coordinate system for self-localization in Sensor and Actor Networks (SANETs). GPS technology has become a de facto standard for outdoor localization, however, self-localization in GPS denied scenarios is still extremely challenging. Typically, anchor nodes or global state information are used to update the nodes' location information. In contrast, we present a fully self-organizing strategy to generate a distributed reference coordinate system. In particular, we use autonomous robot systems to span and maintain this coordinate system. In particular, we investigated the capabilities of the Mass-Spring-Relaxation (MSR) algorithm, which is frequently used for fault-tolerant and robust localization. Unfortunately, this algorithm needs certain globally valid state information. We extended the MSR algorithm in two ways: First, we made the algorithm independent of a priori global knowledge, and, secondly, we provide extensions that make the algorithm more reliable and robust, and to reduce the number of necessary information exchanges between the nodes. As can be seen from the simulation results, our advanced MSR is very accurate and clearly outperforms the classical MSR for increasing network sizes. We also validated the simulations in an experimental setting. The obtained results confirm the very high localization accuracy.

We describe a load balancing system for parallel intrusion detection on multi-core systems using a novel model allowing fine-grained selection of the network traffic to be analyzed. The system receives data from a network and distributes it to multiple IDSs running on individual CPU cores. In contrast to related approaches, we do not assume a static association of flows to IDS processes but adaptively determine the load of each IDS process to allocate network flows for a limited time window. We developed a priority model for the selection of network data and the assignment process. Special emphasis is given to environments with highly dynamic network traffic, where only a fraction of all data can be analyzed due to system constraints. We show that IDSs analyzing packet payload data disproportionately suffer from random packet drops due to overload. Our proposed system ensures loss-free analysis for selected data streams in a specified time interval. Our primary focus lies on the treatment of dynamic network behavior: neither data should be lost unintentionally, nor analysis processes should be needlessly idle. To evaluate the priority model and assignment systems, we implemented a prototype and evaluated it with real network traffic.

The aviation community is currently working on the standardization of data communication systems for the future air traffic management. In this context, the International Civil Aviation Organization (ICAO) has initiated a work on standardization of an IPv6-based aeronautical telecommunications network and on future radio access technologies, respectively. In this paper, we integrate L-Band Digital Aeronautical Communications System Option 1 (L-DACS 1), which is one candidate for future radio access technologies, with realistic IPv6-based network layer functionality and analyze Deficit Round Robin (DRR) with fragmentation algorithm for the forward and return link in L-DACS 1. Our analysis mainly covers two application domains: file transfer and real-time services. We show that DRR with fragmentation scheduler provides good performance results in terms of throughput, delay, and bandwidth fairness.

We present the Virtual Cord Protocol (VCP), which exploits virtual coordinates to provide efficient and failure tolerant routing and data management in sensor networks. VCP maintains a virtual cord interconnecting all the nodes in the network and which, operating similar to a Distributed Hash Table (DHT), provides means for inserting data fragments into sensor nodes and retrieving them. Furthermore, it supports service discovery using indirections. VCP uses two mechanisms for finding paths to nodes and associated data items: First, it relies on the virtual cord that always provides a path toward the destination. Second, locally available neighborhood information is exploited for greedy routing. Our simulation results show that VCP is able to find paths close to the shortest path (achieving a stretch ratio of less than 125 percent) with very low overhead. We also extended VCP with data replication mechanisms to improve failure handling. The routing performance of VCP, which clearly outperforms other ad hoc routing protocols such as Dynamic MANET On Demand (DYMO), is similar to other virtual addressing schemes, e.g., Virtual Ring Routing (VRR). However, we improved VCP to handle frequent node failures in an optimized way. The presented results outline the capabilities of VCP to handle such cases more efficiently compared to other protocols. We also compared the capabilities to reliably store and retrieve data in the network to Geographic Hash Tables (GHTs). VCP, in the worst case, performs similar to GHTs, but outperforms this protocol in most cases, especially when complex routing is involved.

Recently, many efforts have been made to develop more efficient Inter-Vehicle Communication (IVC) protocols for on-demand route planning according to observed traffic congestion or incidents, as well as for safety applications. Because practical experiments are often not feasible, simulation of network protocol behavior in Vehicular Ad Hoc Network (VANET) scenarios is strongly demanded for evaluating the applicability of developed network protocols. In this work, we discuss the need for bidirectional coupling of network simulation and road traffic microsimulation for evaluating IVC protocols. As the selection of a mobility model influences the outcome of simulations to a great extent, the use of a representative model is necessary for producing meaningful evaluation results. Based on these observations, we developed the hybrid simulation framework Veins (Vehicles in Network Simulation), composed of the network simulator OMNeT++ and the road traffic simulator SUMO. In a proof-of-concept study, we demonstrate its advantages and the need for bidirectionally coupled simulation based on the evaluation of two protocols for incident warning over VANETs. With our developed methodology, we can advance the state-of-the-art in performance evaluation of IVC and provide means to evaluate developed protocols more accurately.

Network-based Intrusion Detection Systems (IDSs) such as Snort or Bro that have to analyze the packet payload for all the received data show severe performance problems if used in high-speed networks. Recent research results improve pattern matchers based on efficient algorithms or using specialized hardware. We approach the problem in a completely different way by considerably reducing the amount of data to be analyzed with only marginal impact on the detection quality. Dialog-based Payload Aggregation (DPA) uses TCP sequence numbers to decide which parts of the payload need to be analyzed by the IDS. Whenever a connection starts, or if the direction of the data transmission between peers changes, we forward the next N bytes of traffic to an attached IDS. All data transferred after the window is discarded. Our analysis using live network traffic and multiple Snort rulesets shows that most of the pattern matches occur at the beginning of connections or directly after direction changes in the data streams. According to our experimental results, our method reduces the data rate to be processed to around 1% in a typical network while retaining more than 98% of all detected events. Assuming a linear relationship between the data rate and processing time of an IDS, this results in a speedup of two magnitudes in the best case.

The aviation community is currently working on the standardization of data communication systems for the future air traffic management. In this context, the ICAO and EUROCONTROL are working on the standardization of IP-based aeronautical telecommunications network and future radio access technologies, respectively. With this work, for the first time, we integrate L-DACS 1, which is one candidate for future radio access technologies, with realistic IP-based network layer functionality and analyze the handover delay performance. We first investigate the effect of link layer retransmissions on handover delay performance. We realized that for regions with low signal-to-noise ratio (BER of 1e-3), link layer retransmissions improve the total handover delay (layer 2 and 3) by about 80%. Considering regions with high signal-to-noise ratio (BER of 1e-5), the benefit of link layer retransmissions becomes negligible due to the reduced number of packet losses. During our analysis, we notice frequent transmission of router advertisement messages causing significant overhead on L-DACS 1 and propose two approaches in order to decrease the overhead to an acceptable range. In the last section, we tackle the increase in handover delay due to a limited number of return link resource request opportunities in congested cells.

In recent work it has been shown that the use of virtual coordinates or identifiers for efficient routing and data management has several advantages compared to the use of predefined addresses or geographical coordinates. However, these advantages only hold for single domain networks with limited mobility. In this paper, we discuss the challenges arising from using virtual coordinates for routing (to a particular sensor ID or to indexed data or resources) in ad hoc and sensor networks in multi-domain network scenarios. We show the feasibility of inter-domain routing by exploiting a concept that is central to most virtual coordinate approaches: the availability of data management operation using a DHT like mechanism. Based on the Virtual Cord Protocol (VCP), we show how inter-domain routing can be realized using appropriate indirections. Furthermore, we investigate the possibility of replicating data among different networks, or DHTs, to provide seamless data access in multi-domain environments. Our simulation results clearly show that both functions can be realized with only marginal overhead.

We propose “mode switch”, an adaptive load-sensitive solution that supports both an energy-efficient operation mode for transmitting normal sensor data and an QoS-aware low-latency mode for high priority emergency messages. Typically, sensor networks are considered to operate efficiently w.r.t. energy consumption. A good example is the IEEE 802.15.4 protocol, which has mainly been designed for this purpose. The standard IEEE 802.15.4 beacon-enabled cluster-tree topology supports energy-efficient operation for low-rate data transmissions. However, in the case of detected alarms, high-priority emergency messages need to be transmitted with low-latency and guaranteed delivery. In this case, the cluster-tree topology appears to be inefficient and cannot provide the needed support. In this paper, we show how energy-efficiency and delay-sensitivity can be combined by developing an adaptive solution that completely switches between the fundamentally different operation modes beacon-enabled cluster-tree mode and non-beacon-enabled mesh mode. The key challenge is to provide a fast mode switch that also keeps track of necessary topology information. We present a protocol for this mode switch and demonstrate, based on extensive simulations, that the performance demands can be fulfilled with only reasonable overhead even for mid-size networks. Our simulation results clearly indicate that the system achieves the objected QoS and energy-efficiency with “mode switch” in real-time.

We present an integrated approach for supporting in-network sensor data processing in dynamic and heterogeneous sensor networks. The concept relies on data stream processing techniques that define and optimize the distribution of queries and their operators. We anticipate a high degree of dynamics in the network, which can for example be expected in the case of wildlife monitoring applications. The distribution of operators to individual nodes demands system level capabilities not available in current sensor node operating systems. In particular, we present a system for seamless and on demand operator migration between sensor nodes. Our framework, which we implemented for Contiki running on TelosB nodes, supports stateful module migration including selected parts of the code and data sections.

We investigate the impact of the human driver behavior on Intelligent Transportation Systems (ITS). Motivated by early studies, which investigated the driver behavior and which provided a rough classification of different reactions on presented traffic information, we integrated such models into our simulation framework Veins. We were able to determine a significant change in the performance of a technically optimized system due to suboptimal reactions of the drivers. The main objective of this study is to outline the need for integrated driver models into the development process of Traffic Information System (TIS) solutions. Based on our simulation framework, not only the driver model can be accurately represented but we were also able to show that simple probabilistic models show exactly the same behavior compared to sophisticated empirical measurement based ones.

We discuss the need for taking the environmental impact as a primary metric for evaluating the quality of algorithms for Intelligent Transportation Systems (ITS). In many studies, the travel time, or its minimization, has been used to demonstrate the advantages of Inter-Vehicle Communication (IVC) solutions combined with dynamic rerouting. Such evaluations are frequently based on simulation experiments. Recently, much progress has been achieved in this domain by coupling network simulation with road traffic microsimulation. We now investigate the relationship between different metrics used for the evaluation, in particular the environmental impact represented by gas consumption and emissions versus the travel time, highlighting cases where both metrics are conflicting.

Technology is taking us to a world where myriads of heavily networked devices interact with the physical world in multiple ways, and at multiple scales, from the global Internet scale down to micro- and nano-devices. Many of these devices are highly mobile and autonomous, and must adapt to the surrounding environment in a totally distributed and unsupervised way. fundamental research challenge is the design of robust decentralized computing systems, which are capable of operating under changing environments, and yet exhibit the desired behavior and response time, under unpredictable operating constraints, such as traffic demand, energy consumption, size, and processing power. These systems should be able to adapt and learn how to react to unforeseen scenarios, as well as to display desired emergent properties.

Abstract The IEEE 802.15.4 protocol has become the primary solution for many low‐rate wireless personal area network (LR‐WPAN) applications. This is especially the case for industrial sensor network applications such as automation control. We contribute to the better understanding of the protocol behavior by presenting a set of results of simulation experiments. Our results outline the capabilities of this protocol in the selected scenarios but also the limitations. In particular, we investigated the dependency of the protocol on protocol‐inherent parameters such as the beacon order and the superframe order but also to different traffic load. Our results can be used for planning and deploying IEEE 802.15.4‐ based sensor networks with specific performance demands. We put a special focus on application scenarios in industrial sensor network applications. The primary requirements are reduced end‐to‐end latency and energy consumption. Our studies are based on our new implementation of IEEE 802.15.4 developed for the simulation framework OMNeT++. Copyright © 2009 John Wiley & Sons, Ltd.

We discuss the need for accurate analysis of TCP connections based on aggregated flow information. Due to increasing bandwidths in the Internet, flow metering is thought to be a promising solution for network monitoring, as packet-oriented state-based analysis reaches its limits and fast hardware support for flow metering is available. Motivated by earlier work on flow-based connection analysis, we investigate the quality of several stateless classifiers that can be used to determine the TCP connection state as either successful or failed. This information is strongly needed especially in the domain of attack detection and is usually produced by fine-grained analysis in the packet level. Furthermore, we determine appropriate configuration parameters for optimal flow metering by introducing a new statistical property, the maximum packet gap. We evaluated both, the classifiers and the packet gap analysis using a number of representative packet traces. Our best classifiers are able to correctly identify 95% of all connections with a fraction of the processing costs required for packet-based stateful connection tracking.

The use of wireless technology is continuously gaining interest in industrial automation. With the standardization of the IEEE 802.15.4 protocol, low-power sensor network protocols have been introduced in this field. Recently, we investigated the real-time communication capabilities of this protocol. This study is now extended by incorporating the security mechanisms as provided by the protocol standard. In contrast to other papers, which studied the effectiveness of these security techniques, we are interested in whether the real-time capabilities are affected by encryption and message authentication and to which extend. Based on extensive simulations, we investigated the interdependency of protocol parameters and available security options. The results can be used for optimally selecting such parameters according to the quality of service requirements of the application scenario.

Network lifetime has become the key characteristic for evaluating sensor networks in an application-specific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to lifetime considerations. A great number of algorithms and methods were proposed to increase the lifetime of a sensor network—while their evaluations were always based on a particular definition of network lifetime. Motivated by the great differences in existing definitions of sensor network lifetime that are used in relevant publications, we reviewed the state of the art in lifetime definitions, their differences, advantages, and limitations. This survey was the starting point for our work towards a generic definition of sensor network lifetime for use in analytic evaluations as well as in simulation models—focusing on a formal and concise definition of accumulated network lifetime and total network lifetime. Our definition incorporates the components of existing lifetime definitions, and introduces some additional measures. One new concept is the ability to express the service disruption tolerance of a network. Another new concept is the notion of time-integration: in many cases, it is sufficient if a requirement is fulfilled over a certain period of time, instead of at every point in time. In addition, we combine coverage and connectivity to form a single requirement called connected coverage. We show that connected coverage is different from requiring noncombined coverage and connectivity. Finally, our definition also supports the concept of graceful degradation by providing means of estimating the degree of compliance with the application requirements. We demonstrate the applicability of our definition based on the surveyed lifetime definitions as well as using some example scenarios to explain the various aspects influencing sensor network lifetime.

Debugging and analyzing wireless sensor networks (WSNs) are important tasks for improving the quality and performance of the network. In this paper, Pimoto is to be presented, which is a distributed passive monitoring system implemented for debugging and analyzing WSNs. It is based on a hierarchical structure allowing to monitor different networks simultaneously and to analyze the obtained information at a dedicated PC. The system relies on three components. The first element is the monitoring node. It intercepts the radio packets in the vicinity and sends received packet information to a gateway using a second radio interface in order to prevent intrinsic interactions with the sensor network operation. The gateway has the ability to communicate directly with the monitoring node and to transfer all the collected monitoring data to the third component, a dedicated PC (server), in the hierarchy using standard TCP/IP communication. The packets are analyzed and visualized on the server using the standard network monitoring and analyzing tool Wireshark. The most important characteristic of this monitoring concept is the passive operation, i.e. the normal operation in the WSN is not influenced by the analyzer.

Efficient data management techniques are needed in wireless sensor networks (WSNs) to counteract issues related to limited resources, e.g. energy, memory, bandwidth, as well as limited connectivity. Self-organizing and cooperative algorithms are thought to be the optimal solution to overcome these limitations. On an abstract level, structured peer-to-peer protocols provide O(1) complexity for storing and retrieving data in the network. However, they rely on underlayer routing techniques. In this paper, we present the virtual cord protocol (VCP), a virtual relative position based efficient routing protocol that also provides means for data management, e.g. insert, get, and delete, as known from typical distributed hash table (DHT) services. The key contributions of this protocol are independence of real location information by relying on relative positions of neighboring nodes, short virtual paths because successors and predecessors are in their vicinity, and high scalability because only information about direct neighbors is needed for routing. Furthermore, VCP inherently prevents dead-ends and it is easy to be implemented.

Early approaches for Traffic Information Systems (TISs) primarily focused on centralized systems using unidirectional downlink communication and employing wireless broadcast or similar techniques. In general, these centralized TISs were operated by public radio stations, thus there were almost no security issues related to this approach. The situation changes when new Inter Vehicle Communication (IVC) techniques are investigated. The advantages of improved timeliness and accuracy of available traffic information come with a number of security concerns. This paper reviews the requirements and objectives for secure TISs. We outline possible solutions to face the security concerns and clearly depict open issues. In conclusion, we advocate more secure TISs that benefit from recent IVC technologies in a more secure and privacy protecting way.

We present Virtual Cord Protocol (VCP), a virtual relative position based routing protocol for sensor networks that also provides methods for data management as known from standard DHT services. Self-organizing and cooperative algorithms are thought to be the optimal solution to overcome the inherent resource limitations in sensor networks. On an abstract level, DHT techniques offer O(1) complexity data lookup. Unfortunately, they usually rely on underlayer routing techniques. The key contributions of VCP are the independence of real location information by relying on relative positions of neighboring nodes, successors and predecessors in the cord are always in their vicinity, and the high scalability because only information about direct neighbors are needed for routing. Furthermore, VCP inherently prevents dead-ends and it is easy to be implemented.

Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.

After three years of organizing the wireless and sensor network security (WSNS) workshop, it is time for some retrospection to the workshop objectives and the visible outcome. This review is focusing on the the quality and performance of the workshop. On the one hand, it represents a good source to recapitulate recent research topics related to security in wireless ad hoc and sensor networks. Additionally, it extrapolates future research directions that seem to be interesting and challenging for forthcoming research activities.

Coupling Vehicular Ad Hoc Networks (VANETs) with wired networks such as the Internet via access points creates a difficult mix of highly mobile nodes and a static infrastructure. In order to evaluate the performance of typical ad hoc routing protocols-in particular, we used Dynamic MANET On Demand (DYMO)-in such VANET scenarios, we combined microsimulation of road traffic and event-driven network simulation. Thus, we were able to analyze protocols of the Internet protocol suite in VANET scenarios with highly accurate mobility models. Varying parameters of DYMO for a multitude of traffic and communication scenarios helped point out approaches for improving the overall performance and revealed problems with the deployment. It could be shown that in realistic scenarios, even for medium densities of active nodes and low network load, overload behavior leads to a drastic decrease of the perceived network quality. Cross-layer optimization of transport and routing protocols therefore seems highly advisable.

Wireless sensor networks (WSN) are composed of battery-driven communication entities performing multiple, usually different tasks. In order to complete a given task, all sensor nodes, which are deployed in an ad-hoc fashion have to collaborate by exchanging and forwarding measurement data. We define the behavior of the overall sensor network based on the parameters lifetime and functional density. The functional density describes the distribution of all necessary tasks in a given geographical area. The lifetime is primarily given by the time each task is successfully performed by at least one node, i.e. the functional density of all necessary tasks. Nodes can become unavailable due to insufficient remaining energy. We assume that sensor nodes can be reconfigured or reprogrammed by a mobile robot system. There are various reasons for considering robots for this reconfiguration, e.g. reliability, security, and deployment issues. In this paper, we evaluate the advantages of exploiting reconfiguration and reprogramming schemes WSN using mobile robots. The primary objective is to increase the lifetime of the overall network. This goal is achieved by optimizing the functional density of heterogeneous tasks. Based on a developed simulation model, we discuss the advantages and performance characteristics

Network monitoring is a major building block for many domains in communication networks. Besides typical accounting mechanisms and the emerging area of charging in next generation networks, especially network security solutions rely on efficient and optimized monitoring. Network monitoring in high-speed networks is usually based on flow accounting and aggregation techniques represent a necessary enhancement in order to cope with increasing amounts of monitoring data that accrue with the ever-growing network capacities. In this paper, we propose a flexible flow aggregation mechanism that can be directly employed on a monitoring probe to reduce the memory and processing demands. Alternatively, it can work as a concentrator that collects flow data from multiple monitoring probes, combines and aggregates them and forwards the results to an analyzer. We verified and evaluated the aggregation mechanism by integrating it into our monitoring probe Vermont. Our approach opens new prospects for high-speed network monitoring and allows coping with special situations that cannot be treated satisfyingly by traditional flow accounting, such as distributed denial-of-service attacks causing very high numbers of flows. Aggregated flow data are an easy-to-handle form of packet information especially for anomaly detection and accounting issues

Security has become a major concern for many real-world applications for wireless sensor networks (WSN). In this domain, many security solutions have been proposed. Usually, all these approaches are based on well-known cryptographic algorithms. At the same time, performance studies have shown that the applicability of sensor networks strongly depends on effective routing decisions or energy aware wireless communication. This observation correlates with the emergence of new application scenarios. Security mechanisms were rarely included in such measurements and studies and only few approaches were analyzed in experimental setups. Therefore, only theoretical measures were applied to demonstrate and discuss the behavior of security solutions. In this paper, we used an experimental setup for the verification of runtime behavior of several cryptographic algorithms including MD5, SHA-1, and AES. We used typical sensor hardware to get reasonable results. Based on our experiments, we provide some analysis and considerations on practical feasibility of such cryptographic algorithms in sensor networks.

Self-organization mechanisms have been developed to efficiently operate networked embedded systems. Special focus was given to wireless sensor networks (WSN) and sensor/actuator networks (SANET). Looking at the most pressing issues in such networks, the limited resources and the huge amount of interoperating nodes, the proposed solutions primarily intend to solve the scalability problems by reducing the overhead in data communication. Well-known examples are data-centric routing approaches and probabilistic techniques. In this paper, we intend to go one step further. We are about to also move the operation and control for WSN and SANET into the network. Inspired by the operation of complex biological systems such as the cellular information exchange, we propose a network-centric approach. Our method is based on three concepts: data-centric operation, specific reaction on received data, and simple local behavior control using a policy-based state machine. In summary, these mechanisms lead to an emergent system behavior that allows to control the operation of even large-scale sensor/actuator networks

Many security solutions have been proposed in the domain of wireless sensor networks (WSN). Usually, all these approaches are based on well-known cryptographic algorithms. The efficiency of WSN not only depends on effective routing decisions or energy aware wireless communication. In contrast, the efficiency strongly depends on the application scenario that includes appropriate security mechanisms. Previously, only few approaches were analyzed in experimental setups. Therefore, only theoretical measures were applied to demonstrate and discuss the behavior of security solutions. In this paper, we used an experimental setup for the verification of runtime behavior of several cryptographic algorithms including MD5, SHA-1, and AES. We used typical sensor hardware to get reasonable results. Based on our experiments, we provide some analysis and considerations on practical feasibility of such cryptographic algorithms in sensor networks

Netconf is a new protocol for configuration and management of network devices, based on a flexible XML-encoded message format. Netconf aims to overcome the short-comings of SNMP and CLIs that are predominantly used for configuration tasks. We demonstrate that Netconf is highly suitable for the configuration of IPFIX/PSAMP monitoring probes, as required in order to dynamically and remotely adapt to the varying needs of applications that receive and process monitoring data. In this regard, we present an XML-based data model covering all common configurable parameters for flow metering and aggregation, packet sampling, and data export. Finally, we describe how we implemented the Netconf-based configuration approach based on Web Services and SOAP.

A typical characteristic of wireless ad hoc sensor networks is the error-proneness and, therefore, the unreliability of communication paths. The search for reliable communication methodologies in this area in combination with appropriate security mechanisms has become a main research activity in the networking community. In this paper we describe a new approach and an according protocol for usage in ad hoc networks that provides reliable as well as semi-reliable communication services. Additionally, the proposed methodology allows to ensure data integrity and message authentication. The main aspects during the development were the limitations of typical sensor nodes in terms of available resources such as storage, processing power, and energy. Our solution provides the capability of acknowledging correct receptions as well as the check of data integrity and message authentication in a single step. Therefore, a low overhead solution was created providing all the mentioned communication goals