The new 5G category RedCap has been introduced to address small, energy-efficient 5G devices with relaxed requirements on data rates. This work performs an empirical study on the performance and the impact of complexity reduction in 5G RedCap devices on data rates and power consumption. For this purpose, measurements were carried out with first off-the-shelf 5G RedCap devices in a hardware-in-the-loop laboratory setup using a channel emulator for realistic signal conditions. The results demonstrate that 5G RedCap devices can provide up to 80 Mbit/s downlink data rates in TDD networks even under multipath and fading conditions. In terms of energy efficiency, the reduced complexity enables up to two times longer battery life compared to 5G NR devices. Considering new power saving techniques, a case study demonstrates that future 5G RedCap devices require even lower power consumption for years of battery life. However, current devices already provide more than two months of battery life under good coverage conditions. Comparing the eDRX power consumption in 5G RedCap devices, we find that the RRC Idle state does not provide significant power saving benefits, and thus using the RRC Inactive state for longer battery life and good network reachability is recommended.

With the introduction of Reduced Capability (RedCap) and enhanced Reduced Capability (eRedCap) user equipment, 3GPP Release 17 and 18 define new device categories aimed at bridging the gap between traditional 5G New Radio (NR) devices and IoT-oriented solutions such as NB-IoT and eMTC. These categories target mid-tier applications, such as process / asset monitoring and electricity distribution automation, which demand reduced device complexity, high energy efficiency, and moderate data rates. In this paper, we present a comprehensive simulation-based performance analysis of 5G RedCap and eRedCap using an extended ns-3 5G-LENA implementation. Our simulation framework integrates an energy consumption model and Bandwidth Part-aware scheduling, calibrated with empirical energy measurements from commercial RedCap devices, ensuring realistic and reproducible simulation results. While eRedCap shows improved energy efficiency due to its reduced 5MHz bandwidth, its overall battery lifetime and latency tend to be inferior in practical scenarios. Our analysis proposes optimized eDRX power consumption and Release Assistance Indication (RAI) for (e)RedCap to reflect future energy-saving potential. Our results highlight both the limitations and opportunities of (e)RedCap in expanding the 5G ecosystem towards 6G.

The advancement of wireless communication technologies, especially in the context of Millimeter-Wave (mmWave) frequencies, is facilitating novel advancements in industrial applications. This study investigates the energy efficiency of mobile mmWave end devices deployed within industrial settings. Utilizing and extending an established laboratory setup, an experimental framework is developed to create device-specific energy profiles and conduct precise mobility testing. Measurements based on robotic evaluation scenarios are performed to analyze the interplay between energy consumption and performance under varying radio conditions caused by mobility, alignment, and Line-of-Sight (LOS) availability. The findings highlight improvement opportunities by optimizing end device orientation and incorporating additional network hardware to minimize Non-LOS (NLOS) conditions via Beyond-LOS (BLOS) links, which in turn enhance the overall energy efficiency. The optimization techniques implemented lead to a notable stabilization of the end device’s throughput alongside a 19.9 % decrease in power consumption resulting in a 29.1 % higher energy efficiency. These results are crucial for extending the operational duration of mobile applications while minimizing charging needs and offer significant insights for future research aimed at advancing mmWave-enabled devices for industrial robotics.

Adaptive 6G networks are crucial for maintaining reliable communication in dynamically changing environments. To this end, accurate propagation models are essential for network adaptation, but developing real-time, high-fidelity models is a significant challenge. Machine Learning (ML)-based channel models have gained attention in recent years, yet their limited generalizability often necessitates extensive retraining to ensure applicability in unseen environments. In this paper, we propose a process chain for rapid network adaptation in dynamic environments. Our fully automated process encompasses network Key Performance Indicator collection, LiDAR-based environmental modeling, Transfer Learning (TL)-assisted refinement of the ML-based IndoorDRaGon propagation model, and network planning. We demonstrate that Artificial Neural Networks (ANNs) achieve comparable accuracy to Random Forest (RF)-based models while offering significantly faster adaptation to new environments. Unlike RFs, ANNs do not need to be retrained from scratch using all the source data, which reduces computational effort and improves data privacy as only the new data is required. This enables efficient and dynamic network reconfiguration in previously unseen scenarios with just a few new measurements.

The adoption of Millimeter-Wave (mmWave) technology in industrial environments presents significant challenges in maintaining consistent Quality of Service (QoS) under dynamic and complex conditions. This study investigates the performance of a multi-cell mmWave network deployed in a large-scale industrial hall, emphasizing the mobility of end devices and their interaction with environmental factors. Measurements were conducted using a Non-Standalone (NSA) 5G network configuration with one sub-6 GHz anchor and two mmWave Radio Units (RUs) deployed for comprehensive coverage. The evaluation highlights key aspects such as Secondary Node (SN) changes, end device orientation, and network load conditions. Results from mobile measurements reveal the influence of device alignment and environmental changes on performance, transmission power and connectivity, particularly in SN change areas. Additionally, the impact of static load generation on multi-cell network performance is examined, demonstrating the interplay between mobility and network capacity. These findings underscore the importance of precise device orientation and environmental awareness in optimizing mmWave deployments. Index Terms-mmWave communications, indoor measurements, multi-cell, multi-user, mobility.

Throughput-critical teleoperation requires robust and low-latency communication to ensure safety and performance. Often, these kinds of applications are implemented in Linux-based operating systems and transmit over virtual private networks, which ensure encryption and ease of use by providing a dedicated tunneling interface (TUN) to user space applications. In this work, we identified a specific behavior in the Linux TUN driver, which results in significant performance degradation due to the sender stack silently dropping packets. This design issue drastically impacts real-time video streaming, inducing up to 29 % packet loss with noticeable video artifacts when the internal queue of the TUN driver is reduced to 25 packets to minimize latency. Furthermore, a small queue length also drastically reduces the throughput of TCP traffic due to many retransmissions. Instead, with our open-source NODROP Patch, we propose generating backpressure in case of burst traffic or network congestion. The patch effectively addresses the packet-dropping behavior, hardening real-time video streaming and improving TCP throughput by 36 % in high latency scenarios.

The increasing demand for efficient millimeter-wave (mmWave) beam management in wireless communication systems of 5G, 6G and beyond necessitates innovative approaches to mitigate the resource-intensive and latency-prone nature of exhaustive search methods. This paper presents a novel approach leveraging FPGA-accelerated real-time ray tracing for dynamic beam management. The proposed method dynamically reduces the number of beam directions and burst periodicity based on the mobility and density of mobile devices, as well as the presence of obstacles and available propagation paths. By utilizing a digital twin and real-time ray tracing, the system estimates the required beam directions, enabling adaptive beam management that allows to respond to dynamic obstacles and user mobility. The efficacy of the proposed methodology is showcased in an initial vehicular logistics scenario and can be transferred to further scenarios like intelligent transportation systems and others. Within the sample scenario, the resource overhead is reduced to roughly a third, while the utilization of the base station antenna's beamforming gain is significantly improved.

Throughput-critical applications, such as teleoperation, require robust and low-latency communication systems to ensure seamless performance. In this work, we propose the opensource predictive multi-link teleoperation video stream system for fading channels called Reliable Intelligent Stream Encoding system (RISE). It features a proactive bitrate control based on predicted channel conditions together with a modified version of the SEAMLESS multi-link protocol. Additionally, video stream queue feedback is evaluated to dynamically adjust the bitrate if predictions are too optimistic. Thereby, video transmission delays are minimized, preventing video frame drops due to an unacceptable age-of-information. We validate the performance of RISE in a laboratory setup using realistic radio channel emulation. The results demonstrate significant latency and video frame delivery improvements compared to conventional singlelink and constant bitrate approaches. By leveraging SEAMLESS in addition to using the predicted bitrate with queue feedback approach, the median data rate of the video stream was doubled. Furthermore, RISE prevents frame drops in all investigated channel configurations while minimizing the queue delay. Thus, the proposed approach demonstrates the potential for improvement in the overall reliability of real-world teleoperation systems.

Various technologies are being evaluated for wireless and flexible connectivity in future production processes, with a key focus on the suitability of private 5 G networks compared with industrial Wi-Fi networks. Current studies often derive technological advantages from isolated, idealized case studies, neglecting significant influences of the application environment and scalability. This paper proposes a locally distributed approach for comprehensive performance evaluation of relevant wireless technologies. The STING approach enables reproducible test procedures for evaluating environment-dependent system limits and application characteristics through integration into real operational processes. This approach was used in a case study at an operational washing machine production facility to compare a temporarily deployed 5 G campus network and an existing Wi-Fi 6 infrastructure. The results demonstrated that 5G technology achieved 75% higher reliability compared to Wi-Fi 6 for the analyzed AGV application in operational settings.

Energy efficiency is a major concern in the development of future mobile networks. Besides the infrastructure, a significant challenge is the power consumption of the user equipment, as it directly affects the quality of experience. We have conducted comprehensive laboratory measurements on the latest commercial 5G devices to assess power consumption, considering different frequencies, bandwidths and duplex patterns. A key result is the non-linear increase in power consumption with uplink transmit power. An empirical model with machine learning methods is proposed to enable quantitative analysis of the power consumption based on the communication behavior. By applying this model to an extensive data set, spatiotemporal predictions of real-world user equipment power consumption were performed. Depending on the deployment location and communication behavior, battery life can be as much as five times lower. These results can be utilized to perform energy-aware scheduling and deployment site selection to enhance the energy footprint of mobile platforms and Internet of Things devices.

Wireless 5G connectivity in private operation is rapidly evolving into a necessity component for business-critical services within professional industries, such as those related to manufacturing and logistics. The resulting large number of private 5G networks requires reliable methods for network planning and operation, for which current research is already presenting approaches for efficient AI-based automated network planning. However, there is often a significant gap between current research findings and their practical application in industry, resulting in insufficient delays for the utilization of research outcomes by industrial stakeholders. Hence, this paper introduces a solution that provides industry partners with free and open access to the latest research findings on automated network planning. As primary contribution, we present a web-based planning tool facilitating intuitive network planning within a few simple steps. As a result, radio environmental maps are generated using deep learning propagation modeling as well as AI-driven network planning outcomes are evaluated, balancing Quality of Service (QoS) levels against the requisite number of base stations. Video Abstract-Demonstration video is available online at https://tiny.cc/Private5GPlanner

Private networks represent a key innovation in current 5G and future 6G networks, offering significant benefits, particularly for vertical industries with mission-critical industrial applications. Compared to public networks, the deployment of numerous potential private networks demands automated network planning while simultaneously meeting higher performance requirements for targeted applications. Emerging approaches have successfully utilized AI-based methodologies as a basis for automated network planning in greenfield deployments within licensed but purely private frequency bands. However, these approaches fail to include and extend brownfield implementations in public mobile networks, which would be crucial for private networks running a shared operator model. Thus, this paper presents an AI-based automated network planning methodology augmented by our recently introduced and thoroughly validated data-driven channel modeling approach, DRaGon. Further, this combined AI-based planning methodology is extended to provide automated network planning solutions for shared private networks within public macro networks. The overall planning accuracy was successfully validated with only minor deviations using public network deployments as ground truth. As a key result, we demonstrate that the performance of the presented AI-based planning method can reliably and accurately plan demand-driven network expansions for professional applications with the highest quality requirements.

Private cellular network deployments are increasingly adopted globally to connect machines on industrial campuses. Millimeter-wave (mmWave) cells need to be utilized when high wireless traffic volumes are expected, e.g., owing to high application requirements in conjunction with large number of devices. However, mmWave connectivity is highly sensible to non-line-of-sight (NLOS) conditions. For cost- and energy-efficiency reasons, 6G aims to illuminate under-connected shadow regions within the targeted service area using intelligent reflecting surfaces (IRSs). We present promising indoor measurement results for the attained performance boost using commercial equipment, where the introduction of a custom-designed passive IRS seamlessly restores throughput to 2 Gbit/s with the new propagation path increasing the link margin by more than 10 dB. Video Abstract-Demonstration video is available online at: https://tiny.cc/mmWaveHeliosMeasurements.

Future 6G private networks rely on millimeter-wave (mmWave) cells for ultra-high local connectivity, but non-line-of-sight (NLOS) scenarios demand costly dense deployments. Instead, a new approach gaining much attention in research introduces intelligent reflecting surfaces (IRSs) to increase coverage with fewer full-blown active antennas. In an earlier work, we introduced and validated a new passive IRS architecture leveraging additive manufacturing to provide reflections tailored exactly to the needs of a specific scenario. The design goal for so-called HELIOS reflectors is to procure a geometry realizing high power gains for mobile devices in a well-defined service area. This contribution introduces a design process coupling electromagnetic (EM) simulations with differential evolution-based optimization to custom-tailor the reflector shape. Our indoor measurements confirm the specified reflection characteristics and find that up to, on average, 41.2 % higher gain in receive power is attained over a broad angular range compared with a narrow-reflecting IRS. By increasing the IRS size, the peak gain of 20 dB can be matched. Moreover, it is found that the designed HELIOS reflectors support the entire mmWave spectrum. An additional benefit is given by the fact that the sub-6 GHz anchor link attains power gains of up to 12 dB from the same reflectors.

Wireless communication plays an increasingly large role in modern industrial environments, enabling highly flexible manufacturing processes. While Wi-Fi is still a dominant technology, private 5G networks promise to provide a sophisticated, more reliable solution in licensed frequency bands. However, it remains uncertain in practice which level of end-to-end performance can actually be achieved. To this end, this contribution focuses on a technology-independent performance evaluation with distributed test devices based on the STING concept (Spatially Distributed Traffic and Interference Generation). We present a comparative analysis of 5G and $\mathrm{Wi}-\mathrm{Fi}$ technologies for private industrial networks under realistic network load scenarios, focusing on latency to determine their suitability within industrial contexts. It can be seen that the current Wi-Fi 6 technology can keep critical applications reliable in low to medium load scenarios, but struggles when multiple stations have critical Quality of Service (QoS) requirements. In contrast, it is shown that 5G technology maintains lower latency times and has fewer outlier effects that are considered unacceptable in functional safety applications, especially in scenarios with medium to high network utilization. In order to maintain the advantage of simple Wi-Fi technology while increasing latency stability, we have optimized channel access (EDCA) in a further step on the basis of an open source Wi-Fi stack. It is shown that Wi-Fi technology with suitable modifications beyond the standard can also be able to reliably serve several stations with high $\mathbf{Q o S}$ requirements.

Satellite-based connectivity is on the rise. More and more large companies worldwide announce the development of their own satellite constellation for commercial and private internet access. Hence, these orbital platforms are emerging as considerable broadband alternatives to terrestrial internet access. Besides the straightforward use as broadband for private users, particularly in remote locations, satellite networks offer considerable potential for providing fast and ubiquitous connectivity during disaster response and critical search and rescue missions. Especially in the latter category of scenarios, the use of semi-autonomous systems becomes increasingly important. To enable the reliable teleoperation and monitoring of these systems, robust connectivity is critical. As conventional survey trajectory planning focuses on maximum survey coverage and does not consider the geometry from the antenna to the satellite, continuous connectivity is sacrificed. In this paper, we propose the novel COMPASS algorithm, which leverages a digital twin-based communication-aware trajectory planning and steering, based on a self-conducted satellite connectivity campaign, including measurements for a geostationary and a low earth orbit system. Compared to the generic trajectory planning and more naive approaches, COMPASS improves the minimum SNR by 9 dB while still achieving 100 % survey coverage.

Traditional cellular Radio Access Networks (RANs) are associated with significant costs and low agility due to proprietary hard- and software resulting in vendor lock-ins. Open RAN promises to change this by harnessing open source and Commercial of-the-Shelf (COTS) solutions. Hence, the Open Radio Access Network (O-RAN) Alliance, a consortium of partners from industry and research, aims to identify and close gaps in 3rd Generation Partnership Project (3GPP) specifications. It defines a software-centric RAN architecture with open interfaces to increase interoperability, strengthen innovation and lower market entry barriers towards future 6G infrastructures. A core concept in this context is the near-Real-Time RAN Intelligent Controller (RIC), a virtual platform for hosting so-called xApps. These software-based network functions provide functionalities such as monitoring or network slicing. Using proactive resource management, slicing has the potential of enabling concurrent service profiles such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB), while rising spectral efficiency and lowering latency. Thus, this work introduces an O-RAN-based framework for predictive uplink slicing. An xApp is presented, harnessing deep learning to dynamically reconfigure RAN scheduling via the RIC's E2 interface. The evaluation is performed on the challenging example of URLLC traffic from the Smart Grid domain via an experimental laboratory setup. O-RAN introduces additional interfaces, yet the framework performs about on par with proprietary solutions with latencies down to 5 ms.

The ongoing development towards autonomous traffic requires detailed context information, including highly accurate position estimates. The 5G standard has therefore extended its feature support for positioning and defined six service levels (SLs) to cater to the various commercial use cases. User positioning at millimeter-waves (mmWaves) is considered particularly promising due to the inherent support for angle measurements and the contiguous broad bands. However, in the literature, a comprehensive classification of the achievable SLs with different positioning algorithms under different parameter measurement errors is missing. Based on ray-tracing data, we find that only the lower SLs can be achieved in an urban scenario, whereas SLs 4 to 6 are hard to meet due to the high availability requirements. For such use cases, hybrid positioning is needed to shorten the tail of the error distribution further: We show that positioning with a single base station (BS) is particularly well-suited because the identified network time synchronization problem is bypassed, thus attaining high accuracy at low overhead. Service availability can be improved further by integrating satellite-based estimates.

With an increasing number of global Internet of Things devices, networks face the challenge of high scalability and support of massive numbers of small data transmissions. With Early Data Transmission for NB-IoT networks, which is called Small Data Transmission for 5G New Radio and Reduced Capability networks, standardization has introduced efficiency optimizations with reduced signaling overhead for better scalability of these networks. Though, previous work demonstrated that current NB-IoT configurations are not well-balanced when it comes to Random Access windows and additional uplink resources. In this work, we evaluated different sets of Random Access parameters derived from public NB-IoT networks and identified new optimal parameters for smart urban networks as well as for private high-density micro cells, based on a detailed analytic Random Access model. The identified optimal Random Access parametrization supports 30 million Random Access preamble transmissions per day when using 15 additional non-anchor carriers and 1% Block Error Rate, which is an improvement of 150% to 223% compared to current public networks. When it comes to private high-density micro cells, up to 1,200 Random Access preambles per second are supported, which is between 23 and 82 times more than with legacy configurations. The results demonstrate the importance of well-configured NB-IoT networks, especially for contention-based channels like Random Access. For further increasing numbers of Internet of Things devices in the future, networks must be adapted for better spectral efficiency and better support of small data transmissions, as shown in this work.

Future vehicular applications like Tele-Operated Driving (ToD) and Communication-Based Train Control (CBTC) pose demanding requirements on mobile communication networks. Despite continuous 5G technology upgrades and expansion strategies, mobile networks cannot provide a full-coverage service guarantee for the required mission-critical Key Performance Indicators (KPIs). However, application and location-specific Quality of Service (QoS) predictions are crucial to reliably meet the highest QoS compliance of emerging future smart city services.Therefore, this paper proposes a digital twin capable of merging connectivity data with arbitrary application domains to derive KPI predictions for mission-critical applications. The potential of the proposed approach is illustrated based on a case study in the urban area of Dortmund, Germany, considering data rate and latency predictions for mobile applications. In this context, a continuous data flow for the multi-dimensional mobile network twin is acquired using a massive, multimodal measurement campaign enabled by road and rail-based vehicles. This ever-growing database is utilized to analyze the KPI requirements of selected vehicular applications.For an example ToD target zone, it is shown that a multi-Mobile Network Operator (MNO) approach increases the KPI fulfillment of direct control ToD from approximately 70% to 90% compared to a single MNO. By further restricting the ToD zone and combining two MNOs, a ToD-ready zone with 100% fulfillment of the KPIs is reached.

Wireless ad-hoc localization can play an enabling role for many safety-critical applications and help to save life. For instance, in challenging applications such as firefighting, first responders are often faced with the decision to enter a structure with limited to no visibility and lacking detailed information on the existing conditions. Relative position uncertainty or loss of contact may lower the operation's prospect of success or, in the worst case, result in human harm. This work aims to provide a novel ad-hoc localization system that allows for omnidirectional awareness of co-responders' locations, even under zero-visibility and non-line-of-sight conditions. To this end, we propose a novel ultra-wideband-based localization method combining multiple phase-difference of arrival capable nodes for accurate direction and distance estimation. A tightly integrated hardware setup enables time difference of arrival direction finding, augmenting the limitations of pure phase-based direction estimation. Additionally, single-sided two-way ranging is utilized for ad-hoc distance estimation. We have evaluated the localization performance for different realistic indoor scenarios under line-of-sight and non-line-of-sight conditions. Experimental results show an average 95th percentile 2D accuracy of 40 cm at a distance of up to 6 m in an unobstructed environment. In more challenging conditions which incorporate obstruction an accuracy of 1.2 m at up to 12 m distance could be achieved.

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or Smart Grids are increasingly dependent on flexible, yet highly reliable communication systems. In this context, Fifth Generation of mobile Communication Networks (5G) promises to support mixed-criticality applications on a single unified physical communication network. This is achieved by a novel approach known as network slicing, that promises to fulfil diverging requirements while providing strict separation between network tenants. We focus in this work on hard performance guarantees by formalizing an analytical method for bounding response times in mixed-criticality 5G network slicing. We reduce pessimism considering models on workload variations.

The 5th generation of mobile communication networks (5G) introduced the concept of network slicing for enabling multiple, diverging service types by providing virtually independent communications within one physical network. While Ultra-Reliable Low Latency Communication (URLLC) aims to provide latency guarantees below 5 ms for mission-critical applications such as Smart Grid as well as Industry 4.0, Enhanced Mobile Broadband (eMBB) focuses mainly on high data rates. Thus, since different Key Performance Indicators (KPIs) such as latency, data rate or time-criticality need to be considered, the allocation of resources between corresponding slices is challenging. This work, therefore, aims to reduce uplink latency for URLLC transmissions by deploying Proactive Grants to minimize the impact of time-consuming scheduling requests and any negative influence on other slices. Resources are allocated proactively by base stations utilizing Machine Learning (ML) models trained on real-world measurements. An experimental evaluation via an Software-Defined Radio (SDR)-based physical testbed demonstrates delay reductions towards the mission-critical threshold while simultaneously increasing spectral efficiency. Compared to Round Robin (RR)-based slicing, latency decreases by 49 %, while maintaining a high throughput of 98 % in the eMBB slice.

The ongoing process of shop floor digitalization makes production processes more transparent and helps technical staff and managers at their day-to-day work in modern factories. The digitalization is enabled by a wide variety of applications which run on different device types and demand support for different network characteristics.

Mission critical applications in domains such as Industry 4.0, autonomous vehicles or smart grids are increasingly dependent on flexible, yet highly reliable communication systems. The Fifth Generation of mobile Communication Networks (5G) promises to support critical communications on a single unified physical communication network through a novel approach known as network slicing. We focus in this work on context-based hard performance guarantees by formalizing an analytical method for bounding response times in critical systems. This approach allows to consider different contexts based on models of degradation of channel quality, and avoids a global highly pessimistic worst-case bound computed for worst possible channel conditions. We demonstrate that the proposed method for computing context-based response times guarantees successfully bounds results obtained in realistic mobility scenarios using a machine-learning based 5G simulation framework.

The growing number of IoT devices will lead to a massive number of users in communication networks. Therefore, this work analyzes the scalability boundaries of NB-IoT networks with different transmission modes, called standard transmissions, Cellular IoT optimization, and Early Data Transmission, using a novel detailed implementation of NB-IoT in the NS-3 LTE simulation framework LENA. The results show that Early Data Transmission clearly outperforms NB-IoT standard transmissions and Cellular IoT optimization by providing up to 4.1 times less latency and 1.6 times longer battery lifetime, while only using one-fourth of the downlink spectrum. Further, a good scalability for up to 864.000 devices per day in a cell with an area of 4.91km2, or 176.000 devices per day and km2for all NB-IoT standard transmission, Cellular IoT optimization, and Early Data Transmission scenarios is given. It is shown that the scalability is limited by downlink spectrum capacity for non-Early Data Transmission scenarios and Random Access windows for all scenarios. Doubling the number of Random Access windows improves the performance in highly scaled scenarios in terms of a larger packet delivery rate and fewer Random Access collisions. Still, the number of Random Access collisions for scenarios over 1.000.000 devices per day is very high, which indicates the necessity of a detailed optimization for the radio resource configuration when using new features like Early Data Transmission for optimal cellular performance. Still, the analysis results show a great positive impact of Early Data Transmission on the overall performance and is highly recommended to be used by default.

Radio channel modeling is one of the most fundamental aspects in the process of designing, optimizing, and simulating wireless communication networks. In this field, long-established approaches such as analytical channel models and ray tracing techniques represent the de-facto standard methodologies. However, as demonstrated by recent results, there remains an untapped potential to innovate this research field by enriching model-based approaches with machine learning techniques. In this paper, we present Deep RAdio channel modeling from GeOinformatioN (DRaGon) as a novel machine learning-enabled method for automatic generation of Radio Environmental Maps (REMs) from geographical data. For achieving accurate path loss prediction results, DRaGon combines determining features extracted from a three-dimensional model of the radio propagation environment with raw images of the receiver area within a deep learning model. In a comprehensive performance evaluation and validation campaign, we compare the accuracy of the proposed approach with real world measurements, ray tracing analyses, and well-known channel models. It is found that the combination of expert knowledge from the communications domain and the data analysis capabilities of deep learning allows to achieve a significantly higher prediction accuracy than the reference methods.

Multiple service types such as Ultra-Reliable Low Latency Communication (uRLLC) and Enhanced Mobile Broadband (eMBB) are envisioned to be incorporated into the next generation mobile communication standard 5G based on a single physical communication network. To unite these services with partly contradicting Quality of Service (QoS) requirements, Network Slicing is considered a key technology. uRLLC slices in particular are highly demanding, requiring extremely high reliability and low latency in the single-digit milliseconds range. Consequentially, the latency impact of radio resource management on the end-to-end latency is optimized in this work by using so-called Configured Grants (CGs), which aim to minimize latency-intensive scheduling requests by pre-allocating radio resources. As predicting future traffic demands and channel conditions are required to use CGs, a data-driven machine learning-based radio resource scheduler prototype is introduced and evaluated in this work based on a specifically developed 5G radio resource simulator. The results show promising latency optimizations and possible trade-offs in uRLLC and eMBB coexistence.

The high popularity of ultra-wideband technology for accurate indoor positioning in industrial and public spaces has led to a large amount of research in recent years. The focus has mostly been on localization accuracy in small-scale setups with a single localization zone. However, solutions designed for line-of-sight environments are not suitable for many large-scale applications. To overcome this lack of research, we propose novel concepts for precise wireless multi-hop clock synchronization and localization zone selection. By integration into a time-difference-of-arrival-based ultra-wideband localization scheme, continuous cross-spatial positioning in large-scale scenarios is enabled. Validation is carried out in an unprecedented testbed with multiple rooms. We could show that positioning accuracy in multi-room scenarios is up to three times higher when exploiting the proposed concepts compared to the initial accuracy achieved with existing approaches. In addition, we provide an open-source implementation of our real-time localization system.

The requirements of vehicular communications grow with increasing level of automated driving and future applications of intelligent transportation systems (ITS). Beside the ever-increasing need for high capacity radio links, reliability and latency constraints challenge the mobile network supply. While for example the millimeter-wave spectrum and THz-bands offer a vast amount of radio resources, their applicability is limited due to delicate radio channel conditions and signal propagation characteristics. Reconfigurable intelligent surfaces (RISs) as part of smart radio environments (SREs) of future ITS infrastructure promise improved radio link qualities by means of purposeful cultivation of passive reflections. With this, obstructed mmWave or THz beams can be guided around obstacles through RIS reflection paths to improve the otherwise limited coverage. In this article, application use cases of RIS-enhanced vehicular communications are proposed. Beside static deployments of RISs at exterior walls of buildings, unmanned aerial vehicles (UAV) could provide reflection capabilities on demand, while future vehicles could - in a visionary approach - consist of meta-material allowing for their opportunistic utilization within an enriched SRE. Results of a case study based on our multi-scale mobility and network simulation model clearly highlight the potential of RIS deployment for hybrid vehicular communication scenarios. Path loss and outage percentages can be reduced considerably.

Interdisciplinary application fields, such as automotive, industrial applications or field robotics show an increasing need for reliable and resilient wireless communication even under high load conditions. These mission-critical applications require dependable service quality characteristics in terms of latency and especially stability. Current deployments often use either wired links that lack the flexibility to accommodate them or wireless technologies that are susceptible to interference. Depending on the application and the surrounding environments, different technologies can meet the associated requirements and have to be tested deliberately to prevent unexpected system failure. To stress-test these infrastructures in a reproducible and application-aware manner, we propose STING, a spatially distributed traffic and interference generation framework. STING is evaluated in a remote control test case of an Unmanned Ground Vehicle that serves as a scout in Search and Rescue missions. A significant impact of interference on the remote control quality of experience is shown in tests with different operators, which result in an 80% increase in completion time in our test scenario with high interference on the radio channel. With this case study, we have proven STING to be a reliable and reproducible way to assess resilience against the interference of wireless machine-type communication use cases. Our concept can find usage for any type of wireless technology, in unlicensed (e.g, Wi-Fi) as well as licensed bands (e.g. 5G).

Private 5G networks are considered key enablers for allowing industrial companies to deploy fully digitized production environments in an ad-hoc manner. Based on the foundation of exclusively assigned frequency spectrum resources, a guaranteed quality of service level can be achieved. However, strict requirements and regulations regarding antenna placement and interference with neighbors impose tough challenges on enterprises without expertise in communication network planning. For closing this gap, we propose an unsupervised learning-based network planning framework capable of rapidly and autonomously finding suitable solutions for antenna placement based on given environments and service quality, while satisfying regulatory restrictions. Results show that our proposed system reliably and rapidly calculates antenna positions and powers for realistic private 5G network scenarios. Temporary network deployments, e.g., Formula 1 tracks inside the given private 5G network, can be planned within minutes based on pre-calculated radio environmental maps.

Cellular-Vehicle-to-Everything (C-V2X) communication is an essential component of future automated traffic systems. As vehicular communication is safety-critical, C-V2X is designed to be reliable and highly scalable. To analyze the C-V2X communication performance in experimental setups, a huge number of communication nodes are necessary, and those large setups become complex and expensive, and creating repeatable results in these huge experimental setups is hard.In research, communication system independent software-based traffic generators are often used to analyze the scalability of a communication system. However, these traffic generators are limited by the restrictions of the underlying communication technology. In this work, a Software-Defined Radio (SDR)-based open-source C-V2X traffic generator is presented. The traffic generator allows repeatable scalability experiments using fewer communication nodes by creating valid C-V2X communication traffic that mimics multiple C-V2X communication nodes. Experimental results using commercial off-the-shelf (COTS) C-V2X modems show that the achieved results correspond to the expected behavior. In addition, these experimental results are compared to the simulation results of our C-V2X simulator. The comparison shows that if the C-V2X traffic generator limits the channel resources, the PRR does almost exactly match the simulation results. The proposed C-V2X traffic generator’s functionality is further confirmed by the very small difference in the Packet Inter-Reception time of only 0.4 % for around 250 vehicles.

In this paper, we present Context-Adaptive PARRoT (CA-PARRoT) as an extension of our previous work Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowledge (PARRoT). Short-term effects, as occurring in urban surroundings, have shown to have a negative impact on the Reinforcement Learning (RL)-based routing process. Therefore, we add a timer-based compensation mechanism to the update process and introduce a hybrid Machine Learning (ML) approach to classify Radio Environment Prototypes (REPs) with a dedicated ML component and enable the protocol for autonomous context adaption. The performance of the novel protocol is evaluated in comprehensive network simulations considering different REPs and is compared to well-known established routing protocols for Mobile Ad-hoc Networks (MANETs). The results show, that CA-PARRoT is capable to compensate the challenges confronted with in different REPs and to improve its Key Performance Indicators (KPIs) up to 23 % compared to PARRoT, and outperform established routing protocols by up to 50 %.

Swarms of collaborating Unmanned Aerial Vehicles (UAVs) that utilize ad-hoc networking technologies for coordinating their actions offer the potential to catalyze emerging research fields such as autonomous exploration of disaster areas, demand-driven network provisioning, and near field packet delivery in Intelligent Transportation Systems (ITSs). As these mobile robotic networks are characterized by high grades of relative mobility, existing routing protocols often fail to adopt their decision making to the implied network topology dynamics. For addressing these challenges, we present Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowledge (PARRoT) as a novel machine learning-enabled routing protocol which exploits mobility control information for integrating knowledge about the future motion of the mobile agents into the routing process. The performance of the proposed routing approach is evaluated using comprehensive network simulation. In comparison to established routing protocols, PARRoT achieves a massively higher robustness and a significantly lower end-to-end latency.

Many industrial players are currently challenged in building distributed CPS and IoT applications with stringent end-to-end QoS requirements. Examples are Vehicle-to-X applications, Advanced Driver-Assistance Systems (ADAS) or functionalities in the Industrial Internet of Things (IIoT). Currently, there is no comprehensive solution allowing to efficiently program, deploy, and operate such distributed applications. This paper will focus on real-time concerns, in building distributed CPS and IoT systems. Thereby, the focus lies, on the one hand, on mechanisms required inside of the IoT (compute) nodes, and, on the other hand, on communication protocols such as TSN and 5G connecting them. In the authors' view, the required building blocks for a first end-to-end technology stack are available. However, their integration into a holistic framework is missing.

For many cyber-physical systems, e.g., IoT systems and autonomous vehicles, offloading workload to auxiliary processing units has become crucial. However, since this approach highly depends on network connectivity and responsiveness, typically only non-critical tasks are offloaded, which have less strict timing requirements than critical tasks. In this work, we provide two protocols allowing to offload critical and non-critical tasks likewise, while providing different service levels for non-critical tasks in the event of an unsuccessful offloading operation, depending on the respective system requirements. We analyze the worst-case timing behavior of the local cyber-physical system and, based on these analyses, we provide a sufficient schedulability test for each of the proposed protocols. In the course of comprehensive experiments, we show that our protocols have reasonable acceptance ratios under the provided schedulability tests. Moreover, we demonstrate that the system behavior under our proposed protocols is strongly dependent on probability of unsuccessful offloading operations, the percentage of critical tasks in the system, and the amount of offloaded workload.

The analysis of the end-to-end behavior of novel mobile communication methods in concrete evaluation scenarios frequently results in a methodological dilemma: Real world measurement campaigns are highly time-consuming and lack of a controllable environment, the derivation of analytical models is often not possible due to the immense system complexity, system-level network simulations imply simplifications that result in significant derivations to the real world observations. In this paper, we present a hybrid simulation approach which brings together model-based mobility simulation, multi-dimensional Radio Environmental Maps (REMs) for efficient maintenance of radio propagation data, and Data-driven Network Simulation (DDNS) for fast and accurate analysis of the end-to-end behavior of mobile networks. For the validation, we analyze an opportunistic vehicular data transfer use-case and compare the proposed method to real world measurements and a corresponding simulation setup in Network Simulator 3 (ns-3). In comparison to the latter, the proposed method is not only able to better mimic the real world behavior, it also achieves a ~300 times higher computational efficiency.

Methods for accurate prediction of radio signal quality parameters are crucial for optimization of mobile networks, and a necessity for future autonomous driving solutions. The power-distance relation of current empirical models struggles with describing the specific local geo-statistics that influence signal quality parameters. The use of empirical models commonly results in an overor under-estimation of the signal quality parameters and require additional calibration studies. In this paper, we present a novel model-aided deep learning approach for path loss prediction, which implicitly extracts radio propagation characteristics from top-view geographical images of the receiver location. In a comprehensive evaluation campaign, we apply the proposed method on an extensive real-world data set consisting of five different scenarios and more than 125.000 individual measurements. It is found that 1) the novel approach reduces the average prediction error by up to 53 % in comparison to ray-tracing techniques, 2) A distance of 250 - 300 meters spanned by the images offer the necessary level of detail, 3) Predictions with a root-mean-squared error of ≈ 6 dB is achieved across inherently different data sources.

Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advancements for IoT-based data analytics, there remains a huge methodological gap for the deployment phase of trained machine learning models. For given resource-constrained platforms such as Microcontroller Units (MCUs), model choice and parametrization are typically performed based on heuristics or analytical models. However, these approaches are only able to provide rough estimates of the required system resources as they do not consider the interplay of hardware, compilerspecific optimizations, and code dependencies. In this paper, we present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS), which applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. LIMITS focuses on high-level tasks such as experiment automation, platform-specific code generation, and sweet spot determination. The solid foundations of validated low-level model implementations are provided by the coupled well-established data analysis framework Waikato Environment for Knowledge Analysis (WEKA). We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification, where we compare different learning models and real world IoT platforms with memory constraints from 16 kB to 4 MB and demonstrate its potential to catalyze the development of machine learning-enabled IoT systems.

In this work, we present Black Spot-aware Contextual Bandit (BS-CB) as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines - supervised, unsupervised, and reinforcement learning - in order to autonomously schedule vehicular sensor data transmissions with respect to the expected resource efficiency. Within a comprehensive real world performance evaluation in the public cellular networks of three Mobile Network Operators (MNOs), it is found that 1) The average uplink data rate is improved by 125%-195% 2) The apparently selfish goal of data rate optimization reduces the amount of occupied cell resources by 84%-89% 3) The average transmission-related power consumption can be reduced by 53%-75% 4) The price to pay is an additional buffering delay due to the opportunistic medium access strategy.

Cellular-Vehicle-to-Everything (C-V2X) communication is an essential component of future automated traffic systems as well as a promising technology for mobile robot applications. In those scenarios, the vehicles typically exchange location data obtained by on-board positioning modules such as satellite receivers or sensor-based SLAM (Simultaneous Localization and Mapping). As such information is vital for cooperative applications, its validation is an important safety feature. In this paper, we propose a method to repurpose available information from C-V2X to estimate the distance between sender and receiver. By analyzing the timing of Cooperative Awareness Messages (CAMs) within the C-V2X resource grid it is possible to derive fairly accurate distance information, which can be used to validate the location data contained within the payload. Such validation is helpful in case of technical failures of positioning modules or intentional transmission of fraudulent location data. Our analysis based on MATLAB simulation shows a positioning error of 30 m for typical traffic and robot scenarios. One key learning is that existing C-V2X signal information can be used to achieve this performance even in the presence of multipath fading. Although the positioning is not yet precise enough for stand-alone use, it is useful for overall safety and reliability measures in cooperative vehicular and robotic applications.

Nowadays always-connected vehicles create opportunities for novel services like vehicles serving as highly mobile sensor platforms in the Internet of Things context. Hereby, cars upload and transfer their sensor data readings via a mobile communication network into the cloud. Data is shared in the cloud via vehicle big data marketplaces. As wireless resources are limited and shared by all users, data transfers need to be conducted efficiently. The ongoing growth of generated data inside vehicles and thereby the increasing data upload demand becomes more and more challenging to Mobile Network Operators.Within the scope of the work, an opportunistic Multi-Radio-Access-Technology (Multi-RAT) uploading approach is proposed in order to conduct those data uploads faster and more efficiently. This approach leverages multiple network interfaces and combines them to one unified communication link. Data uploads can be delayed to exploit good channel conditions and avoid uploads when the channel quality is insufficient. The simulative results and evaluations highlight that the proposed method is well-suited for vehicular data uploads. Even though a trade-off in the age of information is created, the throughput is increased. Besides, better performance in spectral efficiency is achieved in comparison to traditional single-link transmissions.

In the context of vehicle-to-everything (V2X) communications, a mobile subscriber positioning service is beneficial for locating e.g. motor vehicles on the road or unmanned aerial vehicles (UAVs) in the air. Current mobile networks process metrics like the receive power or the propagation delay to determine terminals' positions. With the introduction of millimeter wave (mmWave) communications, the electrically steerable directivity of phased arrays not only procure high gains on the radio link but also offer a direction finding (DF) to the terminals. Several DFs of located base stations could be merged in a cross-bearing manner to indicate the position of mobile devices. In this work, the suitability of DF for positioning of mobile subscribers is discussed based on latest standardization efforts and experimentally evaluated. Our extensive laboratory system consists of three software-defined radio mmWave transceiver platforms, phased array antennas and a tailored rail system allowing for reproducible mobility emulation, positioning and over-the-air testing at 28 GHz. The results show, that considering information about the directivity, a DF can be estimated precisely via the beam pointing direction. The mmWave V2X communications offer a possibility for a precise positioning service for mobile devices as well as to applications in the edge-cloud on the infrastructure side.

Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massive Machine-type Communication (mMTC) caused by vehicle-to-cloud transmissions will confront the cellular network infrastructure with great capacity-related challenges. A cognitive way for achieving relief without introducing additional physical infrastructure is the application of opportunistic data transfer for delay-tolerant applications. Hereby, the clients schedule their data transmissions in a channel-aware manner in order to avoid retransmissions and interference with other cell users. In this paper, we introduce a novel approach for this type of resource-aware data transfer which brings together supervised learning for network quality prediction with reinforcement learning-based decision making. The performance evaluation is carried out using data-driven network simulation and real world experiments in the public cellular networks of multiple Mobile Network Operators (MNOs) in different scenarios. The proposed transmission scheme significantly outperforms state-of-the-art probabilistic approaches in most scenarios and achieves data rate improvements of up to 181% in uplink and up to 270% in downlink transmission direction in comparison to conventional periodic data transfer.

Robots are used more and more often in search and rescue scenarios, for example in post-disaster situations, which are dangerous or lethal for humans, when searching for missing persons after earthquakes. Here, reliable communication is indispensable: on the one hand, unmanned vehicles need to be teleoperated in real-time. On the other hand, high-resolution images, video and sensor-readings producing huge data volume must be transported from the robot back to the operator or rescue forces. This work presents and evaluates SKATES, a new solution enabling interoperable multi-connectivity for reliable communication between a search and rescue (SAR) robot and its remote operator. In this paper, the SKATES concept, its implementation and subsequent validation in a field experiment are presented. SKATES operates protocol agnostic and tunnels any data from the robot reliably to the operator using an improved SOCKS proxy. To increase robustness, a Multi-Radio-AccessTechnology (Multi-RAT) approach incorporating cellular as well as WiFi connectivity options is implemented. This enables link aggregation as well as smooth handover between different wireless technologies in case of outages of one technology. As part of the experimental validation, an unmanned ground vehicle (UGV) has been equipped with a 360° 8K resolution camera, a First Person View (FPV)-camera and the proposed SKATES solution. Being teleoperated, the robot explores a building, while streaming live camera data to the remote operator. The experimental results underline the feasibility and good performance of the approach in a challenging environment: when single communication links break down or do not provide sufficient capacity, the SKATES-enabled Multi-RAT approach enables smooth handovers between different RATs and ensures reliable and real-time communication during the whole experiment.

For many applications relative position information is critical. One particular case is the prevention of compressed air breathing accidents in operations with low visibility. Here, separation of first responders in an unknown and dangerous environment could yield a fatal outcome. The goal of this work is to introduce technology that assists firefighters in situations with no visibility to locate team members. Due to the lack of pre-installed infrastructure, a relative ad-hoc localization scheme without the need for in place infrastructure is required. We propose a three-dimensional ultra-wideband based scheme and show a functional prototype with integrated transceivers combining both, two-way ranging range finding and phase-difference of arrival direction finding. Further, we show a potential architecture for integration in firefighter equipment such as helmet and mask utilizing augmented reality technology. Through multiple experiments we could show, that in the given laboratory setup direction estimation with an accuracy of 20oand position finding with an accuracy of 30cm is possible with the proposed approach.

Ubiquitously deployed Internet of Things (IoT)-based automatic vehicle classification systems will catalyze data-driven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing cyber-physical system. Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost efficiency, and privacy preservation. In this article, we present a novel approach, which exploits radio fingerprints-multidimensional attenuation patterns of wireless signals-for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.

The Smart Grid represents an unprecedented opportunity to move the electric grid into a new era of reliability, availability, and efficiency. It uses two-way communications, digital technologies, advanced sensing and computing infrastructure, and software abilities to provide improved monitoring, protection and optimization of all the grids’ components including generation, transmission, distribution and consumers.

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.

Network data analysis is the fundamental basis for the development of methods to increase service quality in mobile networks. This requires accurate data of the current load in the network. The control channel analysis is a way to monitor the resource allocations and the throughput of all active subscribers in a public mobile radio cell. Previous open-source approaches require either ideal radio conditions or long-term observations in order to obtain reliable data. Otherwise, the revealed information is polluted by spurious assignments with random content. In this paper, we present a new open-source instrument for Fast Analysis of LTE Control channels (FALCON), which combines a novel shortcut-decoding approach with the most reliable techniques known to us to reduce the aforementioned requirements significantly. Long-term field measurements reveal that FALCON reduces errors in average by three orders of magnitude compared to currently the best approach. FALCON allows observations at locations with interference and enables mobile applications with single short-term tracking of the local load situation. It is compatible with numerous software defined radios and can be used on standard computers for a reliable real-time analysis.

The ever increasing need for precise location estimation in robotics is challenging a significant amount of research. Hence, new applications such as wireless localization based aerial robot control or high precision personal safety tracking are developed. However, most of the current developments and research solely focus on the accuracy of the required localization systems. Multi-user scalability, energy efficiency and real-time capabilities are often neglected. This work aims to overcome the technology barrier by providing scalable, high accuracy, real-time localization through energy-efficient, scheduled time-difference of arrival channel access. We could show that simultaneous processing and provisioning of more than a thousand localization results per second with high reliability is possible using the proposed approach. To enable wide-spread adoption, we provide an open source implementation of our system for the robot operating system (ROS). Furthermore, we provide open source access to the raw data created during our evaluation.

Autonomous vehicles, on the ground and in the air, are the next big evolution in human mobility. While autonomous driving in highway scenarios is already possible using only the vehicles sensors, the complex scenarios of big cities with all its different traffic participants is still a vision. Cellular Vehicle-to-Everything (C-V2X) communication is a necessary enabler of this vision and and an emerging field of interest in today's research. However, to the best of our knowledge open source simulators essential for open research do not exist yet. In this work we present our open source C-V2X mode 4 simulator based on the discrete-event network simulator ns-3. To analyze the performance of C-V2X mode 4 using our simulator, we created a worst case scenario and the 3GPP reference Manhattan grid scenario using the microscopic traffic simulator SUMO. We also added the WINNER+ B1 channel model to ns-3, as this is also used by 3GPP. Our results show, that C-V2X is scalable to 250 vehicles within a worst case scenario on a playground of 100 m x 100 m, with respect to the LTE rel. 14 V2X requirements. For the more realistic Manhattan grid scenario, the performance is better, as to be expected. We also analyzed the Packet Inter-Reception time with an outcome of max. 100 ms for more than 99 % of all transmissions. In addition, we investigated the impact of the Resource Reservation Period and the Resource Reselection Probability on the system's Packet Reception Ratio.

While the rollout of the fifth generation of mobile communications is already being discussed, existing networks are still suffering from major coverage gaps even years after their introduction. For the implementation of most IoT applications, however, a mobile network with comprehensive availability is a mandatory prerequisite. Therefore, this paper addresses the problem of coverage gaps and investigates the potential for improvement through multi-operator strategies for LTE and cellular IoT (cIoT) networks in the urban environment of the Smart City Dortmund. Additionally, the potential of data rate improvement through multi-link strategies is evaluated, enabling sufficient data rates of cIoT even in extreme coupling loss conditions. It is shown that in outdoor scenarios both LTE and cIoT can provide 100% coverage, but in indoor and deep indoor scenarios, the Mobile Network Operators (MNO) can not provide full network coverage. When considering multi-operator strategies, the LTE deep indoor coverage can be increased by up to 40%. cIoT can provide full coverage at a Maximum Coupling Loss (MCL) of 164 dB even in deep indoor scenarios using only a single operator, but with very low data rates. Even though cIoT enables sufficient coverage, multi-link communication enabling multi-operator strategies can increase the downlink data rates by a factor of 2.8.

The provision of reliable and efficient end-to-end communication within ground- and air-based mobile mesh networks is a major challenge for routing protocols due to the mobility-related dynamics of the channel properties and the resulting mesh network topology. In this paper, we evaluate the performance of the novel Better Approach To Mobile Adhoc Networking (B.A.T.M.A.N.) V routing protocol for vehicular mesh networks and propose a mobility-predictive extension that explicitly addresses highly dynamic communication networks. In order to enable large-scale simulative analysis, we present an open source simulation model, which is validated by field experiments. Within a comprehensive evaluation campaign in Vehicle-to-Everything (V2X) and Unmanned Aerial Vehicle (UAV) scenarios, it is shown that the predictive B.A.T.M.A.N. V-based approach is significantly better suited for maintaining reliable connectivity within highly mobile mesh networks than established routing protocols.

Cooperating small-scale Unmanned Aerial Vehicles (UAVs) will open up new application fields within next-generation Intelligent Transportation Sytems (ITSs), e.g., airborne near field delivery. In order to allow the exploitation of the potentials of hybrid vehicular scenarios, reliable and efficient bidirectional communication has to be guaranteed in highly dynamic environments. For addressing these novel challenges, we present a lightweight framework for integrated simulation of aerial and ground-based vehicular networks. Mobility and communication are natively brought together using a shared codebase coupling approach, which catalyzes the development of novel context-aware optimization methods that exploit interdependencies between both domains. In a proof-of-concept evaluation, we analyze the exploitation of UAVs as local aerial sensors as well as aerial base stations. In addition, we compare the performance of Long Term Evolution (LTE) and Cellular Vehicle-to-Everything (C-V2X) for connecting the ground- and air-based vehicles.

Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint force of multiple available cellular data connections within vehicular networks. By applying anticipatory communication principles, data transmissions can dynamically utilize the mobile network with the highest estimated network performance in order to achieve improvements in data rate, resource efficiency, and reliability. In this paper, we present the results of a comprehensive real-world measurement campaign in public cellular networks in different scenarios and analyze the performance of online data rate prediction based on multiple machine learning models and data aggregation strategies. It is shown that multi-MNO approaches are able to achieve significant benefits for all considered network quality and end-to-end indicators even in the presence of a single dominant MNO. However, the analysis points out that anticipatory multi-MNO communication requires the consideration of MNO-specific machine learning models since the impact of the different features is highly depending on the configuration of the network infrastructure.

Novel vehicular communication methods are mostly analyzed simulatively or analytically as real world performance tests are highly time-consuming and cost-intense. Moreover, the high number of uncontrollable effects makes it practically impossible to reevaluate different approaches under the exact same conditions. However, as these methods massively simplify the effects of the radio environment and various cross-layer interdependencies, the results of end-to-end indicators (e.g., the resulting data rate) often differ significantly from real world measurements. In this paper, we present a data-driven approach that exploits a combination of multiple machine learning methods for modeling the end-to-end behavior of network performance indicators within vehicular networks. The proposed approach can be exploited for fast and close to reality evaluation and optimization of new methods in a controllable environment as it implicitly considers cross-layer dependencies between measurable features. Within an example case study for opportunistic vehicular data transfer, the proposed approach is validated against real world measurements and a classical system-level network simulation setup. Although the proposed method does only require a fraction of the computation time of the latter, it achieves a significantly better match with the real world evaluations.

Recent technological advances are leading to increasing adoption of Unmanned Aerial Vehicles (UAV) in more and more application areas. The use of UAVs promises great potentials in finding missing persons during maritime search and rescue (SAR) missions. Hereby, communication between ground station and vehicle is essential in order to exchange data. Mandatory requirements are high reliability - especially for telemetry and control data - as well as high data rates as an enabler for high resolution thermal imaging and real-time video payloads. The underlying paper investigates the applicability of Long Term Evolution (LTE) for maritime SAR missions. Therefore, in a first step, a detailed maritime channel model is developed and implemented. The analytic evaluations show significant decreases in communication range in dependency of UAV flight and base station height and in dependency of the current wind speed. To compensate interferences and frequency-dependent propagation effects the authors propose the aggregation of multiple LTE links using the Multipath TCP (MPTCP) protocol. The proposed multi-link concept is evaluated and assessed in a close-to-reality evaluation. Hereby, a hardware-in-the-loop (HIL) experiment is conducted emulating a public mobile network operator in combination with a dedicated SAR-mission-only LTE link. The experiments highlight the applicability of LTE and MPTCP for maritime SAR missions. The heterogeneous multilink concept increases UAV communication range and achieves high reliability and data rates in the whole search area.

Ultra-wideband localization has become a key solution for a variety of industrial applications and recently challenged a significant amount of research. Most of the current research, however, focuses primarily on the analysis of the localization accuracy of one or few mobile users in well-defined system environments. This work aims to analyze the interdependency of various system performance criteria, in particular multi-user scalability, real-time capability, and energy efficiency of time of arrival based wireless localization. We provide an overview by comparing the predominant system topologies and develop analytical models validated by experiments to evaluate the individual tradeoffs. Furthermore, we provide an implementation proposal and in-depth evaluation of emerging time-difference of arrival scheduled channel access to allow for reliable, highly scalable, and energy efficient localization enabling a paradigm shift for many industrial applications. We could show that compared to the most commonly used schemes, the number of users and the energy consumption can be improved by orders of magnitude.

In the past years, connectivity has been introduced in automotive production series, enabling vehicles as highly mobile Internet of Things (IoT) sensors and participants. The Horizon 2020 research project AutoMat addressed this scenario by building a vehicle big data marketplace in order to leverage and exploit crowd-sourced sensor data, a so far unexcavated treasure. As part of this project the Common Vehicle Information Model (CVIM) as harmonized data model has been developed. The CVIM allows a common understanding and generic representation, brand-independent throughout the whole data value and processing chain. The demonstrator consists of CVIM vehicle sensor data, which runs through the different components of the whole AutoMat vehicle big data processing pipeline. Finally, at the example of a traffic measurement service the data of a whole vehicle fleet is aggregated and evaluated.

Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusion, public cellular networks will be confronted with massive increases in Machine-Type Communication (MTC) and will require efficient communication schemes to minimize the interference of Internet of Things (IoT) data traffic with human communication. In this demonstration, we present an Open Source framework for context-aware transmission of vehicular sensor data that exploits knowledge about the characteristics of the transmission channel for leveraging connectivity hotspots, where data transmissions can be performed with a high grade if resource efficiency. At the conference, we will present the measurement application for acquisition and live-visualization of the required network quality indicators and show how the transmission scheme performs in real-world vehicular scenarios based on measurement data obtained from field experiments.

Cellular network technology based device-to-device communication attracts increasing attention for use cases such as the control of autonomous vehicles on the ground and in the air. LTE provides device-to-device communication options, however, the configuration options are manifold (leading to 150+ possible combinations) and therefore the ideal combination of parameters is hard to find. Depending on the use case, either throughput, reliability or latency constraints may be the primary concern of the service provider. In this work we analyze the impact of different configuration settings of unsupervised LTE device-to-device (sidelink) communication on the system performance. Using a simulative approach we vary the length of the PSCCH period and the number of PSCCH subframes and determine the impact of different combinations of those parameters on the resulting latency, reliability and the interarrival times of the received packets. Furthermore we examine the system limitations by a scalability analysis. In this context, we propose a modified HARQ process to mitigate scalability constraints. Our results show that the proposed reduced HARQ retransmission probability can increase the system performance regarding latency and interarrival times as well as the packet transmission reliability for higher channel utilization.

The application of LTE technology has evolved from infrastructure-based deployments in licensed bands to new use cases covering ad hoc, device-to-device communications and unlicensed band operation. Vehicular communication is an emerging field of particular interest for LTE, covering in our understanding both automotive (cars) as well as unmanned aerial vehicles. Existing commercial equipment is designed for infrastructure making it unsuitable for vehicular applications requiring low weight and unlicensed band support (e.g. 5.9 GHz ITS-band). In this work, we present tinyLTE, a system design which provides fully autonomous, multi-purpose and ultra-compact LTE cells by utilizing existing open source eNB and EPC implementations. Due to its small form factor and low weight, the tinyLTE system enables mobile deployment on board of cars and drones as well as smooth integration with existing roadside infrastructure. Additionally, the standalone design allows for systems to be chained in a multi-hop configuration. The paper describes the lean and low-cost design concept and implementation followed by a performance evaluation for single and two-hop configurations at 5.9 GHz. The results from both lab and field experiments validate the feasibility of the tinyLTE approach and demonstrate its potential to even support real-time vehicular applications (e.g. with a lowest average end-to-end latency of around 7 ms in the lab experiment).

Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles such as mobile robotic systems. For 4G/5G-based cellular communication systems, the effective transmission power of uplink data transmissions is of crucial importance for the overall system power consumption. Unfortunately, this information is usually hidden within off-the-shelf modems and mobile handsets and can therefore not be exploited for enabling green communication. Moreover, the dynamic transmission power control behavior of the mobile device is not even explicitly modeled in most of the established simulation frameworks. In this paper, we present a novel machine learning-based approach for forecasting the resulting uplink transmission power used for data transmissions based on the available passive network quality indicators and application-level information. The model is derived from comprehensive field measurements of drive tests performed in a public cellular network and can be parameterized for integrating all measurements a given target platform is able to provide into the prediction process. In a comparison of three different machine learning methods, Random-Forest models thoroughly performed best with a mean average error of 3.166 dB. As the absolute sum of errors converges towards zero and falls below 1 dB after 28 predictions in average, the approach is well-suited for long-term power estimations.

High data rates, low latencies, and a widespread availability are the key properties why current cellular network technologies are used for many different applications. However, the coexistence of different data traffic types in the same 4G/5G-based public mobile network results in a significant growth of interfering data traffic competing for transmission. Particularly in the context of time-critical and highly dynamic Cyber Physical Systems (CPS) and Vehicle-to-Everything (V2X) applications, the compliance with deadlines and therefore the efficient allocation of scarce mobile radio resources is of high importance. Hence, scheduling solutions are required offering a good trade-off between the compliance with deadlines and a spectrum-efficient allocation of resources in mobile networks. In this paper, we present the results of an experimental validation of the Payload-size and Deadline-aware (PayDA) scheduling algorithm using a Software-Defined Radio (SDR)-based eNodeB. The results of the experimental validation prove the high efficiency of the proposed PayDA scheduling algorithm for time-critical applications in both miscellaneous and homogeneous data traffic scenarios.

Nowadays, vehicles are not only merely used as a transportation medium, but also as a highly mobile Internet of Things (IoT) node, collecting data from all types of sources. The delivery of aggregated vehicle sensor data into the cloud for further analysis is very fragile, as vehicles move fast in their environment and channel conditions vary heavily. Next to mastering possible packet loss, communication protocols need to quickly adapt to the frequent data rate changes. Random Linear Network Coding (RLNC) has been proven as an efficient and robust mechanism for reliable data transfer especially on lossy channels. In this paper, the authors extend the Scalable Network Coding (ScalaNC) framework with the novel congestion control Bottleneck, Bandwidth and Round-Trip Time (BBR) in order to quickly adapt to the frequent changes in data rate and allow effective transfer of high amounts of data from vehicles and into the cloud. ScalaNC is validated in a Hardware-in-the-Loop field test consisting of a Long Term Evolution (LTE) base station and channel emulator.

While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resource-efficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%.

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability.

Traffic jams in urban scenarios are often caused by bottlenecks related to the street topology and road infrastructure, e.g traffic lights and merging of lanes. Instead of addressing traffic flow optimization in a static way by extending the road capacity through constructing additional streets, upcoming smart cities will take advantage of the availability of modern communication technologies to dynamically change the mobility behavior of individual vehicles taking these bottlenecks into account. The underlying overall goal is to minimize the total dwell time of the vehicles within the road network. In this paper, a new bottleneck-aware method for dynamic vehicle routing is introduced and compared to existing methods in comprehensive simulations. As a realistic evaluation scenario, the inner city of Dusseldorf is modeled and the mobility behavior of the cars is represented based on real-world traffic flow data. The simulation results show, that the consideration of bottlenecks in a routing method decreased the average travel time by around 23%. The newly created routing method reduces the average travel time further by around 10%. The simulations also show, that the implementation of dynamic lanes in inner cities, and so the manipulation of the capacity of the bottleneck, most of the time only shift traffic congestion to following bottlenecks without reducing the travel times.

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects.This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses.However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications.Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system.In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application.The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests.We describe the hardware and software components of the nodes in addition to the overall structure of the testbed.Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse.

To receive the highest possible data rate or/and the most reliable connection, the User Equipment (UE) may want to choose between different networks. However, current LTE and LTE-Advanced mobile networks do not supply the UE with an explicit indicator about the currently achievable data rate. For this reason, the mobile device will only see what it obtains from the network once it actively sends data. A passive estimation in advance is therefore not doable without further effort. Although the device can identify its current radio conditions based on the received signal strength and quality, it has no information about the cell's traffic load caused by other users. To close this gap we present an Enhanced Client-based Control-Channel Analysis for Connectivity Estimation (E-C3ACE), which uncovers the cell load broken down by each single user. Based on this information and in conjunction with existing indicators like Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), a neural network is trained to perform a data rate prediction for the current LTE link. Compared to an earlier work, our approach reduces the average prediction error below one third. Applied in public networks, the predicted data rate differs by less than 1.5 Mbit/s in 93% of cases.

Ultra-wideband wireless positioning technologies based on IEEE 802.15.4a have gained attention for various use cases requiring highly precise localization. Multi-rotor unmanned aerial vehicle (UAV) systems provide several sensors for stabilization and navigation. However, absolute indoor positioning poses a problem for autonomous robotic systems. The specific challenge addressed in this paper is enabling novel applications with autonomous UAV systems through tight integration with scalable and precise receiver-side time-difference of arrival (TDOA) based ultra-wideband (UWB) indoor localization. For the in-depth validation of the proposed approach, several experiments are performed. One proves the repeatability of the proposed method by following a predefined trajectory ten times achieving close to optical motion-capture based control performance, with a 3rd quartile of the alignment error lower than 10 cm. Another experiment addresses the simultaneous flight of three UAVs in close proximity and delivers an analysis of the real-time capabilities which in turn proves the multi-user scalability. The last experiment demonstrates a user-interactive application of the proposed approach in a logistics environment. A video along with the raw samples, reference data and processed positions of the aforementioned experiments is provided alongside this work.

Even though connectivity services have been introduced in many of the most recent car models, access to vehicle data is currently limited due to its proprietary nature. The European project AutoMat has therefore developed an open Marketplace providing a single point of access for brand-independent vehicle data. Thereby, vehicle sensor data can be leveraged for the design and implementation of entirely new services even beyond traffic-related applications (such as hyper-local traffic forecasts). This paper presents the architecture for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative vehicle data services. Therefore, the novel Common Vehicle Information Model (CVIM) is defined as an open and harmonized data model, allowing the aggregation of brand-independent and generic data sets. Within this work the realization of a prototype CVIM and Marketplace implementation is presented. The two use-cases of local weather prediction and road quality measurements are introduced to show the applicability of the AutoMat concept and prototype to non-automotive applications.

The challenges related to the large-scale deployment of the Internet of Things to enable a wide range of application use cases have received significant attention. A number of communication technologies have been and are still developed ranging from evolved existing technologies such as 4G, WiFi or WPAN technologies to the much hyped 5G networks. Against the background of the on-going discussion on the most suitable technologies the Bluetooth Special Interest Group has proposed the next generation Bluetooth 5 specification and claims to be ready for the Internet of Things as well: mainly by increasing the maximum communication range and adapting the corresponding data rates. In this context, this paper evaluates the suitability of Bluetooth 5 for realistic application activity levels and interference scenarios, based on analytical models and validated by frequency hopping algorithm simulations. The performance results demonstrate that Bluetooth 5 is a valid technology candidate for implementation of Internet of Things applications, provided that activity level, expected coverage areas and the interference situation within the ISM band are carefully considered in the deployment process.

The vision of Smart City is to enable new use cases being overall connected. Considering a large number of Internet of Things sensors and challenging channel characteristics, new technologies evolve facing these new challenges. A promising solution to meet such requirements are Low Power Wide Area Networks. In this paper, a LoRa availability analysis is carried out to analyze performance of a Low Power Wide Area Networks representative in the 433 MHz and 868 MHz ISM bands considering a Smart City Internet of Things scenario, resulting in signal ranges up to 5.8 km. Additional reliability measurements show that indoor and outdoor LoRa nodes are able to achieve a Packet Delivery Rate of over 99% within a range of 4.8 km, depending on their installation side. The results of these measurements are compared with established empirical channel models. Due to the insufficient prediction accuracy of the established models in the examined Smart City area of Dortmund, Germany, two new path loss models for urban areas are presented for both the 868 MHz and 433 MHz frequency bands.

Boosting data rates in LTE mobile networks is one of the key features of LTE-Advanced. This improved user experience is achieved by Carrier Aggregation (CA), in which the available spectrum of an operator is bundled out of several frequency bands. Accordingly, the user equipment has to supply multiple reception chains and therefore consumes considerably more power during a transmission. On the other hand, transmissions terminate faster, which enables a quick switchover into energy-saving mode. In order to examine these opposed facts, empirical analyses of existing devices are first carried out. Subsequently, we present a new CA enhancement of an existing context- aware power consumption model which incorporates the development density of the environment and the mobile device mobility. Based on the extended model we perform a detailed power consumption analysis and show that CA leads to power savings of 31% if the data rate doubled for large file transmissions. In addition, we show that CA can lead to power savings even from a data rate increase of 25%, regardless of mobility and urban development density. Besides, the measurement results show that CA operated in the same band leads to a lower power consumption than inter-band CA.

Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and proactive jam avoidance by intelligent traffic control mechanisms. In addition, the monitoring and classification of vehicles can be used in the field of smart parking systems. The required data is measured using networks with a wide range of sensors. Nevertheless, in the context of smart cities no existing solution for traffic flow detection and vehicle classification is able to guarantee high classification accuracy, low deployment and maintenance costs, low power consumption and a weather-independent operation while respecting privacy. In this paper, we propose a radiobased approach for traffic flow detection and vehicle classification using signal attenuation measurements and machine learning algorithms. The results of comprehensive measurements in the field prove its high classification success rate of about 99%.

Although connectivity services have been introduced already today in many of the most recent car models, the potential of vehicles serving as highly mobile sensor platform in the Internet of Things (IoT) has not been sufficiently exploited yet. The European AutoMat project has therefore defined an open Common Vehicle Information Model (CVIM) in combination with a cross-industry, cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged for the design of entirely new services even beyond traffic-related applications (such as localized weather forecasts). This paper focuses on the prediction of the achievable data rate making use of an analytical model based on empirical measurements. For an in-depth analysis, the CVIM has been integrated in a vehicle traffic simulator to produce CVIM-complaint data streams as a result of the individual behavior of each vehicle (speed, brake activity, steering activity, etc.). In a next step, a simulation of vehicle traffic in a realistically modeled, large-area street network has been used in combination with a cellular Long Term Evolution (LTE) network to determine the cumulated amount of data produced within each network cell. As a result, a new car-to-cloud communication traffic model has been derived, which quantifies the data rate of aggregated car-to-cloud data producible by vehicles depending on the current traffic situations (free flow and traffic jam). The results provide a reference for network planning and resource scheduling for car-to-cloud type services in the context of smart cities.

Efficient routing is one of the key challenges for next generation vehicular networks for providing fast and reliable communication in a smart city context. Various routing protocols have been proposed for determining optimal routing paths in highly dynamic topologies. However, it is the dilemma of those kinds of networks that good paths are used intensively, resulting in congestion and path quality degradation. In this paper, we adopt ideas from multipath routing and propose a simple decentral scheme for Mobile Ad-hoc Network (MANET) routing, which performs passive load balancing without requiring additional communication effort. It can easily be applied to existing routing protocols to achieve load balancing without changing the routing process itself. In comprehensive simulation studies, we apply the proposed load balancing technique to multiple example protocols and evaluate its effects on the network performance. The results show that all considered protocols can achieve significantly higher reliability and improved Packet Delivery Ratio (PDR) values by applying the proposed load balancing scheme.

Ultra-wideband wireless positioning technologies based on IEEE 802.15.4a have gained attention for various use-cases requiring highly precise localization. In this paper the orientation dependent characteristics of commonly used ultra-wideband modules are experimentally determined and analyzed. The specific challenge addressed in this paper is the prediction of orientation induced ranging errors through channel response analysis. For the in-depth validation of the proposed methodology, two experiments are performed: the first one is conducted indoors to demonstrate the system behavior in dense multipath environments, whereas the second experiment is executed in an outdoor environment to allow for detailed analysis with as few multipath components as possible. In a second step, a deep learning neural network is applied to the channel response data, showing that the orientation induced ranging error estimate can be improved significantly using the proposed method.

The aggregation of multiple carriers and even heterogeneous links is a well established method to boost data rates in wireless networks. At the same time, Network Coding has been proven as an efficient and robust mechanism to distribute content in mesh networks or cloud storages. In this paper, we introduce with ScalaNC a new method of heterogeneous link aggregation, which requires no changes in the network infrastructure and incorporates Network Coding to allow for a scalable trade-off between speed, latency and security in terms of confidentiality (information distribution) of the aggregated end-to-end connection. The proposed method is particularly useful in emergency response scenarios, in which first responders need to access cloud storages to retrieve data in harsh communication environments. Using the scalable parameterization the users can increase real-time capability, speed or security depending on the actual needs. ScalaNC is validated with an experimental setup, in which the increase in real-time capability, goodput and security with high packet error rates was demonstrated: while the goodput of an aggregated link operated with Multipath TCP is reduced to around 50 % with occurring packet errors, the ScalaNC-enabled aggregated link maintains stable goodput.

Future energy systems depend on a corresponding Information and Communication Technology (ICT) overlay, allowing timely transmission of critical grid information in particular in case of failures or other unanticipated events. Therefore, to maintain grid stability, Smart Grid communication networks are required to be highly reliable and real-time capable. Within this paper, we propose and evaluate techniques for improved fault tolerance with regard to link failures within the ICT infrastructure, utilizing the concepts of Software-Defined Networking (SDN). centralized and decentralized approaches for both failure detection and traffic recovery are compared. While decentralized approaches, employing Bidirectional Forwarding Detection and OpenFlow's fast failover groups, allow for shorter traffic disruptions by saving the delay of controller-switch communication, they result in sub-optimal configurations and possibly overloaded links. Vice versa, controller-driven approaches, using a custom heartbeat detection mechanism, may offer better alternative configurations due to fast re-calculation of routes, yet incur higher delays. Combining the advantages of both approaches, a hybrid concept is proposed, which enables nearly instant local recovery, succeeded by immediate optimal route updates, issued by the SDN controller. Thus, failover times are reduced to 4.5 ms mean delay, fulfilling IEC 61850 Smart Grid requirements, while providing optimal routes almost continuously and maintaining defined Quality-of-Service (QoS) levels.

Wireless positioning based on IEEE 802.15.4a has recently gained attention for precise localization. However, the multi-user scalability of those, mostly two-way ranging based, approaches is not considered. Due to the exchange of multiple frames per ranging, two-way ranging has significant downsides in terms of scalability. This paper proposes and validates a novel multi-user time-difference of arrival based localization approach using wireless clock synchronization to overcome the limitations of two-way ranging based positioning. The clock characteristics of the individual nodes are experimentally analyzed and the requirements and constraints for wireless clock synchronization are elaborated based on the experiments. The results show, that a clock synchronization rate of around 1 Hz is sufficient to achieve accuracies in the decimeter range. Experiments analyzing the multi-user capabilities of the system are conducted, highlighting the specific properties of multi-user access in the proposed time-difference of arrival based localization. The experiments show, that the system is capable of scaling gracefully under heavy load. However, clock synchronization suffers as it is degrading equally. Therefore, a novel approach is proposed and evaluated to prioritize the wireless clock synchronization to ensure load independent positioning accuracy. It is shown, that the proposed approach is capable of ensuring a synchronization receive ratio of around 90% independent of the system load.

IEEE 802.15.4a Ultra-wideband based wireless positioning has recently gained attention for precise localization. However, the multi-user scalability of those, mostly two-way ranging based, approaches is not considered. Due to the exchange of multiple frames per ranging, two-way ranging has significant downsides in terms of scalability. This paper proposes and validates a novel approach for a multi-user time-difference of arrival based localization system using wireless clock synchronization. The system accuracy is assessed using a complex experiment, covering robotic movement and an optical reference system for comparable results. It is shown, that the accuracies achievable by time-difference of arrival positioning with wireless clock synchronization are comparable to similar two-way ranging based approaches. All raw samples, reference data and processed positions are provided alongside this work.

Advanced Cyber-Physical Systems aim for the balancing of restricted local resources of deeply embedded systems with cloud-based resources depending on the availability of network connectivity: in case of excellent connectivity, the off-loading of large amounts of data can be more efficient than the local processing on a resource- constraint platform, while this latter solution is preferred in case of limited connectivity. This paper proposes a Client-Based Control Channel Analysis for Connectivity Estimation (C3ACE), a new passive probing mechanism to enable the client- side to estimate the connection quality of 4G networks in range. The results show that by observing and analyzing the control traffic in real-time, the number of active user equipment in a cell can be determined with surprising accuracy (with errors well below 10^-6). The specific challenge addressed in this paper lies in a dedicated filtering and validation of the DCI (Downlink Control Information). In a subsequent step the data rates to be expected can be estimated in order to enable decision about the choice of network and the timing of the data offloading to the cloud. The proposed methods have been implemented and validated leveraging the SDR OpenAirInterface, a real- life LTE network and a distributed load generator producing a scalable network traffic by a number of LTE User Equipment.

This contribution illustrates the benefit of incorporating real-time environmental data into highway traffic information systems and describes the modelling and integration of weather conditions into a complex microscopic traffic simulation. Using stationary measured weather data as an example, the achieved results show the potential extended Floating Car Data (xFCD) - submitted to the traffic information system by cellular communication - can have for traffic simulation. Therefore, an estimation about the expected communication network load is given, when xFCD equipped vehicles have become prevalent.

The load-aware operation of network infrastructure is a well established green networking concept to improve the energy-efficiency of wireless communication networks. But while it has been shown that switching-off base stations in times of low traffic leads to significant savings of energy, the interdependency of such infrastructure-oriented measures on the power consumption of user equipments has not been addressed in detail so far. This paper therefore quantizes the negative impact of switching-off strategies for base stations on the battery lifetime of Long Term Evolution (LTE) devices. Therefore, the Context- Aware Power Consumption Model for LTE devices (CoPoMo) that enables an accurate quantification of the energy efficiency of devices under different network deployments, is leveraged. In order to enable a trade-off between energy savings in the infrastructure and battery lifetimes of devices on the user side, we propose a Battery-Lifetime-Aware switching-off Strategy (BaLAnce) for base stations. The results of a simulation study with a reference LTE network scenario show, that a significantly better energy trade-off can be achieved by BaLAnce.

Many vehicular applications can be enabled of cellular communication networks like Long-Term Evolution (LTE). To guarantee a resource-efficient communication, the evaluation of the channel quality in the device is an important research topic in order to consider the channel quality for data transmission decisions. In this paper, the correlation between downlink channel quality indicators that are evaluated in the device and the uplink system performance is analyzed. For this purpose, theoretical analyses and field measurements in a dedicated LTE network as well as in a public LTE development are provided. The results show that the Reference Signal Received Power (RSRP) is suitable indicator for very good uplink connectivity situations and that the connectivity at the cell edge can be identified by the Reference Signal Received Quality (RSRQ).

Precise traffic prediction and automated driving require accurate and reliable positioning information to be communicated to neighbouring vehicles as well as centralized traffic management centres. This paper proposes and evaluates the so-called Local Interference Compensation (LOCATe) method, which is a cloud-aided solution to provide lane-specific positioning information of vehicles. With LOCATe, location errors of satellite-based positioning systems caused by shadowing and multi-path fading of the environment are predicted, quantified and compensated by identifying those impacts with ray-tracing techniques applied to a 3D model of the environment. The paper introduces significant improvements of the original LOCATe approach which lead to both an enhanced accuracy and a reduction of the computation time to reach nearly real-time capability. Based on raw data gained with an open Software-Defined Radio implementation in a real-life environment, the efficiency of different LOCATe variants is evaluated and discussed. The results show, that the so-called real-time LOCATe provides significant improvements to the accuracy with only moderate additional CPU complexity/time compared to an ordinary GPS positioning estimation.

Ultra-wideband wireless positioning technologies based on IEEE 802.15.4a have gained attention for various use cases requiring highly precise localization. In this paper an ultra-wideband based approach is proposed and validated for a vehicular use case requiring highly precise local positioning in a dedicated parking lot. For efficient power transfer in wireless charging of electric vehicles scenarios, an accurate alignment of the coils of vehicle and ground is essential. The specific challenge addressed in this paper is that the proposed approach achieves the required lateral accuracy with only two ground- based anchor nodes in combination with one vehicle-based node. For the in-depth validation of the proposed system concept, two experiments are performed: the first one is conducted with an electrical vehicle representing a real park-to-charge-case, whereas the second experiment is executed in a controlled environment to allow for further alignment error analysis. The experiments show, that an error lower than 10cm can be achieved.

Low-altitude unmanned aerial vehicles (UAVs) combined with WLAN mesh networks (WMNs) have facilitated the emergence of airborne network-assisted applications. In disaster relief, they are key solutions for (1) on-demand ubiquitous network access and (2) efficient exploration of sized areas. Nevertheless, these solutions still face major security challenges as WMNs are prone to routing attacks. Consequently, the network can be sabotaged, and the attacker might manipulate payload data or even hijack the UAVs. Contemporary security standards, such as the IEEE 802.11i and the security mechanisms of the IEEE 802.11s mesh standard, are vulnerable to routing attacks as we experimentally showed in previous works. Therefore, a secure routing protocol is indispensable for making feasible the deployment of UAV-WMN. As far as we know, none of the existing research approaches have gained acceptance in practice due to their high overhead or strong assumptions. Here, we present the position-aware, secure, and efficient mesh routing approach (PASER). Our proposal prevents more attacks than the IEEE 802.11sΓi security mechanisms and the well-known, secure routing protocol ARAN, without making restrictive assumptions. In realistic UAV-WMN scenarios, PASER achieves similar performance results as the well-established, nonsecure routing protocol HWMP combined with the IEEE 802.11s security mechanisms.

Commonly used unmanned aerial vehicle (UAV) platforms rely on the use of global navigation satellite system (GNSS) receivers for navigation. To enable the autonomous navigation of cooperative UAVs in GNSS-denied environments, the use of an ultra-wideband (UWB) positioning system is proposed. This paper discusses the design and evaluation of a practical and cooperative UWB positioning system using newly available integrated radio frequency hardware and antennas. Constellation-aware parameters, as well as other effects like antenna characteristics, are taken into consideration. A non-line-of-sight rejection is implemented based on the ratio of the first path compared to the power of the cumulated channel impulse response. An experiment covering a range of positions and orientations is conducted to gain a broad, representative set of results to assess the system accuracy in real-life usage. In a first experiment the system performance achieves a root-mean-square error of under 10 cm in the horizontal plane and under 20 cm in the three-dimensional space with a probability of 95 %. A GNSS emulation system is implemented to evaluate the real-time in-flight use of the UWB positioning system on an experimental UAV carrier. A proof of concept is given that the GNSS emulation may be used with commercially available UAV platforms to augment those systems with indoor navigation capabilities.

The prediction of both, vehicular traffic and communication connectivity are important research topics. In this paper, we propose the usage of innovative machine learning approaches for these objectives. For this purpose, Poisson Dependency Networks (PDNs) are introduced to enhance the prediction quality of vehicular traffic flows. The machine learning model is fitted based on empirical vehicular traffic data. The results show that PDNs enable a significantly better short-term prediction in comparison to a prediction based on the physics of traffic. To combine vehicular traffic with cellular communication networks, a correlation between connectivity indicators and vehicular traffic flow is shown based on measurement results. This relationship is leveraged by means of Poisson regression trees in both directions, and hence, enabling the prediction of both types of network utilization.

A Quality of Experience (QoE) parameter of increasing importance is the time that a battery powered communication device (e.g. smartphone) can be operated before it needs to be recharged. However, due to the fact that battery capacity is not evolving as fast as the power requirement, the battery lifetime of modern user equipment is stagnating or even decreasing from one device generation to another. In parallel, a major challenge for the design of next generation wireless systems such as LTE-Advanced (LTE-A) is that the required high portion of spectrum is not available in a consecutive portion. For this reason, a procedure called interband non-continuous Carrier Aggregation (CA) will be introduced in LTE-A which allows for the combination of multiple spectrum pieces from different frequency bands. This procedure however requires the parallel operation of multiple power amplifiers that are characterized by a high energy demand. In this paper, we quantify the impact of CA on the power consumption of LTE-A enabled communication by means of a Markovian based power consumption model that incorporates system parameters as well as context parameters. The results show that the suitability of CA does from a battery lifetime perspective strongly depend upon the actual device characteristics as well as the resource availability is the various frequency bands. Furthermore, the application of the sophisticated Kinetic Battery Model (KiBaM) shows that the charge recovery effect during idle periods does significantly affect the battery lifetime.

Due to continuously growing communication traffic of emerging mobile phone applications, resource-efficient communication is a key to affordable network services with high Quality of Experience. While some communication traffic requires immediate resource allocations (such as voice services), an increasing number of mobile phone applications produce a lot of background communication traffic (e.g. social network apps, crowd sensing services). In this paper we discuss the predictive Channel-Aware Transmission (pCAT) scheme, which leverages the fact, that favorable channel conditions require much less spectrum resource allocation than bad channel conditions. By leveraging the knowledge of user trajectories and recurring spots with favorable channel conditions, the so-called LTE connectivity hot spots, background traffic transmissions can be scheduled by the client according to expected channel quality and application data priority. Thereby, the spectrum consumption of background traffic of applications can be reduced significantly. At the same time, the efficient usage of spectrum resources has also an impact on the battery lifetime of the mobile devices. By introducing the Context-Aware Power Consumption Model (CoPoMo) for LTE communications, we highlight, how decisions about the spectrum resource allocation by the network will impact the battery lifetime. One case study will show, that by simply changing the resource allocation scheme and without the need for spending more spectrum resources, the power consumption can be reduced by more than 70% using the Energy-Efficient Scheduling (EES) introduced in this paper.

The current development of the power grid towards a Smart Grid advances the complexity of the system, involving active control, new software components and large amounts of data. This, in turn, requires new approaches for the ICT infrastructure to guarantee real-time capability and reliability. In this work, we present our design of a novel infrastructure for smart substations in the transmission grid, applying the concept of virtualization to substation devices. Since virtualization has already been successfully applied for fault-tolerant and dependable computer systems, it promises to be a valuable concept for substation automation. However, virtualization poses additional challenges to the real-time capability of the substation infrastructure in terms of additional traffic and resource allocation. For analysing the impact on substation communication, we apply simulations and the analytical technique Network Calculus to provide guarantees on the performance of the proposed communication infrastructure. In addition, the performance of the execution platform is studied empirically, measuring occurring delays in a test-bed set-up. Finally, we combine the results from both evaluations to derive an end-to-end delay bound for a power grid related example. Our results show that, other than Fast Ethernet, Gigabit Ethernet networks can guarantee the secure operation of virtualized, fault-tolerant substation infrastructures.

Swarms of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are used for cooperative sensing, clarification and communication relaying in disaster situations and, thereby, allow to reduce jeopardizing the health of rescue personnel. Having meshed interconnection, a swarm maintains a communication network dedicated for applications with context-dependent performance requirements that require reliable traffic classification and conditioning. Nevertheless, these systems are vulnerable because UAVs/UGVs may act incorrectly resulting in an erroneous network behavior which may affect the performance of mission-critical flows. This is a major problem if a couple of untrusted UAVs and UGVs from different rescue organizations are connected. In this paper, the reliability of traffic conditioning within untrusted swarms is enhanced by the Distributed Flow Permission Inspection (DiFPIN) scheme. DiFPIN is used to verify the flow classification to circumvent the usage of erroneous entities on the transmission path. Thereby, the negative impairment of mission-critical flows can be reduced.

A key challenge for civil usage of UAVs is reliable and continuous communication between an operator and the UAVs. In previous works we have developed and investigated communication-aware mobility strategies which consider operational goals but also communication performance. In this work we present an experimental performance evaluation of our Role-Based Connectivity Management using a multi-UAV testbed. Results show that our approach allows the dynamic use of Relay-UAVs to extend the operational range and enables reliable communication.

This paper describes a solution for the problem of wrong way driving on motorways by proposing a new Passive Vehicle Detection (PVD) technology, which is able to detect the driving direction based on roadside radio sensors. The new sensor technology is combined with local and wide-area warning functionality and generates real-time telematics information to increase the safety of all road users. The major benefit of the proposed system is that the vehicle itself must not be equipped with additional hardware. Based on simulation results, we developed a prototype of the Wireless Detection and Warning System (WDWS) to perform experiments for validation. Afterwards, the system was deployed in an extensive field trial at a German motorway in Bochum. The paper describes the architecture, the algorithm design and the results of the performance evaluation. Another benefit is the enormous time gain by a factor of 33 in the generation and transmission of an automated warning to endangered road users.

Extended Floating Car Data (xFCD) is the fundamental for many modern traffic forecast systems. In this paper, an xFCD source model with different transmission rules for making the data efficiently available at the forecast server by means of Long Term Evolution (LTE) enabled Machine-Type Communication (MTC) is proposed. Thereby, the delay requirements of the sensor data and the channel conditions of the communication link are taken into account. The results show that the new source model leads to a decreased utilization of the LTE system and therefore to a low service degradation of other human cellular users. Furthermore, the inserted delay of the data transmission is tolerable for the considered application.

The pervasive use of smartphones in daily life and the advanced expansion of cellular networks (LTE) has strongly increased the amount of mobile data traffic and has established the trend of always on connectivity. To cope with the huge amount of data, WiFi offload has become an indispensable solution. Keeping in mind the resource constraints of smartphones, energyaware applications are of high importance. Vertical handover decision algorithms (VHDA) are indispensable for the balance and coordination of heterogeneous networks capacity and the handling of the increased amount of data traffic. In this paper we present an energy metric for vertical handover decision algorithms (VHDA) derived from measurements. Furthermore, we propose a client-based lightweight handover solution with an integrated energy-aware vertical handover decision algorithm based on Fuzzy logic. We analyze the handover delay and the energy consumption in a laboratory as well as in a real environment and compare our results with a RSS based solution. We show that our client-based energy-aware handover solution (EC) fulfills the requirement of small handover delays (≤ 200ms) and outperforms RSS based solutions in terms of energy efficiency (E/Bit).

In Long Term Evolution (LTE), Voice over Internet Protocol (VoIP) is used for the delivery of the voice data. One of the major challenges for VoIP over LTE is to guarantee the Quality of Experience (QoE) of the VoIP calls. This paper uses an LTE base station emulator and a radio channel emulator to evaluate the impacts of different User Equipment (UE) velocities and Radio Link Control (RLC) parameter values on the QoE of the calls in the vehicular environment. Our study shows that LTE can provide acceptable VoIP quality in the high-speed scenarios (e.g., high-speed train). This paper provides the guidelines for the telecommunications operators to select the appropriate parameter values when deploying their VoIP services in LTE.

System level analytical models and simulations usually use analytical propagation models for estimating received signal power. These propagation models are typically based on statistical properties. Consequently, they are very well suited for general assessment of communication technologies and protocols. However, their universality lets them produce unrealistic results in situations where the concrete assessment of network situations is needed. This type of assessment is particularly useful for network self-healing mechanisms, e.g relay positioning. For this purpose, ray tracing based approaches provide detailed assessment of real network configurations. Ray tracing can thus be used for replacing the path loss estimating part in analytical system level Long Term Evolution (LTE) models or simulations. In this work, a novel combination of ray tracing and an existing analytical interference-aware downlink model was made possible through extension of the model. It is shown at the example of a realistic scenario that system level interference control can be used to enhance the overall user satisfaction of the LTE system. This is done through prevention of a full interference situation by throttling non-premium users.

Wireless Mesh Networks (WMNs) have been a major research focus in the recent years leading to a profusion of protocol proposals. While most existing implementations address routing aspects, none of the proposals addressing security aspects have gained acceptance in practice, due to their high overhead or strong assumptions. To cope with security issues in current WMN deployments, well-known non-secure routing protocols such as HWMP, BATMAN or OLSR could be combined with the security frameworks of the IEEE802.11s or the IEEE802.11i standards. In this paper, we analyze the impact of both security frameworks on the performance of WMNs in simulation and in a real testbed. Besides, we experimentally show that both frameworks do not mitigate the blackhole and wormhole attacks. In addition, we demonstrate that an efficient secure routing protocol combined with a dynamic key management scheme are inevitable to establish reliable WMNs.

For simulating cyber-physical energy systems, distinct simulation domains need to be integrated for a comprehensive analysis of the interdependent subsystems. In particular, continuous time-based power system simulation and discrete event-based simulation of information and communication technology (ICT) networks are critical for investigating future smart grids. This paper presents the novel cosimulation environment INtegrated coSimulation of Power and ICT systems for Real-time Evaluation (INSPIRE) of power and ICT systems using the high-level architecture (HLA) (IEEE Std. 1516-2010). By applying the HLA standard, the environment is extendable to other simulation domains and a variety of functionalities and models, like thermal load models and additional third-party tools. Moreover, the HLA enables computational performance by distributed execution of each simulator on its own hardware and, thus, the applicability to realistic large-scale scenarios. INSPIRE has been designed under consideration of industrial standards [IEC 61850,object linking and embedding (OLE) for process control (OPC), and the common information model (CIM) (IEC 61968/61970)] in order to enhance interoperability, and to develop and validate solutions close to practice. The simulation results underline the interdependencies of power and ICT systems, and the necessity of an integrated analysis.

In this paper, the authors present a study of a network fingerprinting classification using monotone multilayer perceptron neuronal networks. It is part of an overall performance engineering approach. The classification is used to increase the performance of an active queue management on the quality of service for a next generation public safety communication system based on an IP overlay network. This network combines heterogeneous communication networks and technologies to increase the overall systems performance. Public safety users have higher requirements regarding coverage, data rates and quality of service than standard commercial ones. Main challenge for this study is the optimization of the overall system for voice group communication, which is still the most important communication within public safety scenarios. This paper shows that with the given parametrization, an ensemble of multi-layer perceptrons gives a satisfactory classification probability, if a setup of three technologies (EDGE, UMTS and LTE) is assumed to be in usage as communication technologies. This setup is practicable enough to have a chance to be implemented in a future system.

Driven by the increasing application of Smart Grid technologies in today's power systems, communication networks are becoming more and more important for exchanging monitoring, control and protection information on local and wide area level. For communication the IEC 61850 standard is a candidate for the Smart Grid and has been introduced for Substation Automation Systems (SAS) some years ago. IEC 61850 provides interoperability among various manufactures and enables systemwide communication between intelligent components of future power systems. However, as IEC 61850 addresses Ethernet (ISO/IEC 8802-3 family) as network technology, especially high performance aspects of Ethernet have become increasingly important for time-critical communication within substation automation systems. In this paper we introduce the generic architecture of IEC 61850 and present our modelling approach for evaluating high performance and real-time capability of communication technologies for future smart grid application. First, we give a short overview of the IEC 61850 protocol and present communication flows in substation automation systems according to the standard. Here we focus on substation automation at bay level, located inside an exemplary substation node taken from the IEEE 39-bus power system network. Afterwards we demonstrate our modeling approach for communication networks based on IEC 61850. For performance evaluation we developed a simulation model along with an analytical approach on basis of Network Calculus, enabling to identify worst case boundaries for intra-substation communication. Finally results for simulative and analytical modelling are provided and cross validated for two bay level scenarios, showing the applicability of Network Calculus for real-time constrained smart grid communication.

One important advantage of the Orthogonal Frequency Division Multiple Access (OFDMA) based cellular communication system Long Term Evolution (LTE) is its robustness against different kinds of impairments as they may occur on the mobile radio communication channel. This includes an increased toughness not only in harsh multi path scenarios, but also for the case of extremely high user velocities. In this paper, the applicability of the LTE system for two example applications (LTE based backhaul for high speed trains and live transmission of flight recorder data) is investigated by means of extensive throughput measurements (TCP and UDP) incorporating a radio channel emulator. The measurement results show that LTE in general is capable to provide reliable communication links for both considered scenarios. However, extreme velocities as they occur in a “flight data transmission at cruising speed” scenario rely on a specialized cross-layer parameterization.

In this work we examine the influence of harsh communication conditions on cooperative consensus finding in multi-agent systems. We investigate the impact of network topology on the convergence behavior of cooperative consensus finding. Analysis of the effect of packet error rates on convergence speed and convergence error are carried out in simulations. We propose a model to determine the best number of communication links an agent should maintain in order to find the best tradeoff between spatial coverage and convergence speed. We use these results in our communication-aware mobility algorithms for unmanned aerial systems to actively influence the vehicles' network topology. Experimental implementations show that already small amounts of packet errors disable the agents to find the correct average consensus using well-known consensus algorithms. Consequently, we propose a novel consensus protocol which reliably converges to the average value of the initial agent states. Our results have been obtained using simulations and a hardware testbed.

The interaction between Machine-to-Machine (M2M) Communications, also known as Machine-Type Communications (MTC), and Human-to-Human (H2H) Communications is a recent topic in the context of cellular networks like Long Term Evolution (LTE). In this paper, we evaluate the performance of Channel-Aware MTC (CAT) based on LTE resource allocation measurements of a real LTE deployment. By means of a novel measurement setup, which uses a real-time spectrum analyzer, the LTE resource utilization in terms of occupied Resource Blocks (RBs) of the uplink is quantified for different channel conditions. Furthermore, the resource distribution in time and frequency domain, which is determined by the scheduler, is analyzed in this paper allowing for the calculation of average data rates and the spectral efficiency. The measurement results are used to quantize the potential gain of CAT. It is shown that by means of the scheme, the influence of MTC on other LTE users can be significantly reduced. In addition, the fraction of available RBs can be increased by 47%.

The communication aspects of Swarm-based Unmanned Aerial Systems (UAS) for surveillance and monitoring tasks have received increased attention in recent research. In this paper, we address the specific challenges of unpredictable incidents as forest fires, accidents in chemical or nuclear plants which cause potentially dangerous plumes. Therefore, we focus on the detection of chemical plume borderlines while maintaining the connectivity within the UAV team. Leveraging previously developed spatial exploration algorithms like Cooperative Repelling Walk we introduce two novel strategies to optimize the trade-off between detection-efficiency and communication constraints: the Aerosol-detecting Cooperative Repelling Walk (ADCRW) and the Distributed Dispersion Detection (DDD) Algorithm. Both algorithms are evaluated with the help of a multi-scale simulation model which includes a newly incorporated Briggs plume model. Considering the mentioned scenario the results of the performance evaluation demonstrate that the detection-efficiency of our novel algorithms is significantly improved, while at the same time connectivity goals are still met: with the new algorithms the time to detect the borderline of a plume can be reduced by 35%, while the connectivity within the swarm is about 100%.

LTE heterogeneous networks (HetNets) are deployed in areas of high user density in order to increase the capacity in the cellular mobile network. However, they are raising two problems: fixed cell sizes and interference. In our work, we propose a technique, which is maximizing the throughput of the users while considering fairness, dynamically adapting cell sizes and reducing interferences. Moreover, we included the concept of cell selection offsets (CSOs) for the adaptation of the cell sizes and we applied protected subframes, a time domain intercell interference coordination technique in LTE advanced. In our results, we consider the system throughput and the 5-percentile UE throughput and show, that our technique outperforms the best static HetNet configurations achieving a gain up to 16.5% in terms of the fairness based system throughput and a gain up to 37.0% in the 5-percentile UE throughput.

Machine-Type Communication (MTC) poses an ongoing research topic in the development of cellular communication systems. In this context, the efficient collection of extended Floating Car Data (xFCD) via Long Term Evolution (LTE) is a major challenge. In this paper, we present cluster-based xFCD collection schemes in order to form clusters with a long lifetime. As a result, the proposed clustering algorithms reduce the occurring cellular communication traffic. For the performance evaluation of the presented algorithm, a novel system model is used. By means of the system model, the user mobility can be modeled realistically and a precise quantification of the utilization of the LTE network for xFCD transmission is possible. The results show that the LTE network utilization can be significantly reduced by the proposed clustering algorithms.

This paper presents an early warning system based on IEEE 802.15.4 to reduce the risk of collisions between pedestrians and cars. Here, it is of great interest how far the car is situated from the person, but also from which direction the traffic participants approximate. Therefore, we evaluate the performance of two different directional antenna types for a robust, compact and low-cost direction-of-arrival estimation. The first analyzed antenna is a rotating directional Yagi PCB antenna, mounted on a stepper motor reflecting a reference antenna type due to its clearly defined radiation pattern, whereas the second one is a dielectric embedded ESPAR (DE-ESPAR) antenna array using reactively loaded parasitic elements. As interference and noise in the link measurements caused by multipath propagation effects can harm the achievable accuracy, we will demonstrate the feasibility of our approach by carrying out a highly detailed ray tracing analysis within challenging urban scenarios. Furthermore, we show that our approach is even suitable for scenarios in which a line of sight cannot always be guaranteed and in which the nodes do not lie within the same azimuth plane. Finally, we will conclude this work with experimental results.

In this paper, an adaptive and self-organizing spectrum allocation policy for ad hoc femtocells in two-tier networks based on usage of random frequency hopping is presented. As femto- and macrocells normally share the same frequency resources, the proposed approach is able to minimize InterCell Interference (ICI) in an adaptive manner. Each femto- and macrocell user chooses random carrier frequencies for transmission corresponding to a given probability density function (pdf). If the level of interference reaches a certain limit, which is typically the case for femtocell edge users, the probability density function of the carrier frequencies is changed in a way that it is quasi-orthogonal to the one of the macrocell users nearby. The changeover point between different probability density functions is realized in a self-organizing manner regarding the detected level of interference. Hence, the approach is able to adjust the trade-off between spectral efficiency and introduced interference during operation. We present analytical models for the Signal-to-Interference-and-Noise-Ratio (SINR), the Bit Error Ratio (BER) and the required transmission power of Orthogonal Frequency Division Multiple Access (OFDMA) femtocells applying adaptive random frequency hopping. The mobile radio channel is characterized by non-frequency selective Rayleigh fading and all results are verified by simulations implemented in MATLAB. In order to evaluate the improvements, the results are compared to systems using centralized orthogonal planning, systems using neither centralized nor distributed planning, and systems using static random frequency hopping. The comparison shows that adaptive random frequency hopping is capable of reducing the BER by a factor of 15 and the transmission power of femtocells by 17 dBm compared to systems where the macrocell user transmits in the same subbands as the femtocell user.

The average operational time of today's smart phones with one filling of the accumulator is one of the most important performance parameters for the customers of new devices. Nevertheless, this value is remaining constant or even decreasing in the last few years due to the continuously increasing complexity. On the other hand, for ensuring the fulfillment of the high user-requirements even under worst case conditions, novel cellular systems such as Long Term Evolution (LTE) are usually over-dimensioned in terms of the maximum available capacity. In this paper, a novel approach is presented which allows for trading in unused cell capacity for battery lifetime extension of the mobile devices. The basic idea in this context is to increase the robustness of the submitted uplink (UL) signal by decreasing the order of the modulation and coding scheme used. If the provided data rate is kept constant, this comes along with an increasing number of allocated Resource Blocks (RB) and therefore a decreased overall cell capacity. Nevertheless, the more robust signal allows for reducing the uplink transmit power while the Quality of Service (QoS) parameters are still met. The results for exemplary Voice over IP (VoIP) traffic show that for users originally submitting at the maximum available transmit power, the consumed energy of the LTE chip-set can be reduced by up to 27.5 % applying this novel approach.

In this paper, we present a channel sensitive transmission scheme which reduces the negative impact of Vehicle-to-Infrastructure (V2I) traffic on the Quality of Services (QoS) of Human to Human (H2H) communication in cellular networks. The performance evaluation of Long Term Evolution (LTE) for different traffic characteristics is based on an introduced Markovian model. This describes the utilization of the shared LTE Resource Blocks (RBs). The model is parameterized by laboratory measurements and ray tracing simulations. We present blocking probabilities for an LTE network with heterogeneous V2I and H2H traffic and propose different transmission strategies for V2I data with the goal to minimize the impact on human users. The results show that the number of V2I devices can be doubled by using channel sensitive transmission schemes ensuring equal QoS for H2H communication compared to periodically data transmission.

In this paper, an energy efficient approach for the transmission of Floating Car Data (FCD) via Long Term Evolution (LTE) is presented. Applying channel sensitive transmission, FCD is collected by mobile devices in cars and transmitted preferably if the channel conditions are good. The results show that this approach reduces the negative impact on the utilization of the LTE air interface as well as on the energy consumption of the mobile devices. For the performance evaluation of the LTE system, a close to reality parametrized Markovian model is used. The parameters of the model are determined by laboratory measurements and ray tracing simulations. In the laboratory, also the energy consumption of the used LTE USB stick is measured. Hence, we evaluated that the channel sensitive transmission allows for an energy saving of up to 54 % for the transmission of the FCD.

The energy that has to be spent for the successful submission of one Bit is an important figure of merit for the performance analysis and optimization of modern wireless communication systems. The many factors which are influencing this performance parameter range from the efficiency of the User Equipment's (UE) power amplifier to the average path loss, the Transmit Power Control (TPC) parametrization and the fading characteristics of the radio channel. Although many of the relationships can be analytically modeled, the aim of this paper is to present reliable measurements based on commercially available Long Term Evolution (LTE) hardware. Therefore, extensive User Datagram Protocol (UDP) data rate measurements have been performed in a mobile communications laboratory for different radio channel conditions. The impact of the mobile fading channel was emulated by a sophisticated radio channel emulator. Beside this, the on average consumed power of the LTE UE was measured during data transmission. From the results of these measurements, quantitative figures on the energy efficiency are presented for different LTE frequency bands and different radio channels. The results show that major energy savings are possible if the 800 MHz frequency band, which becomes available as part of the digital dividend, can be used for user with bad channel conditions. Considering energy efficiency as a Quality of Service (QoS) parameter of increasing importance the results presented in this paper allow for a context sensitive optimization in a way that the Modulation and Coding Scheme (MCS) switching points as well as the frequency band are chosen with respect to the UE's condition.

Vertical handoff (VHO) is the key feature to increase the seamless services availability (i.e., cloud services) in a heterogeneous network environment that especially plays an important role in emergency use cases for reliable information retrieval. For precise handoff decisions, latest proposed vertical handoff decision algorithms (VHDA) are, however, too complex and do not consider the restricted access to decision metrics of mobile phones with limited system resources. For addressing this issue and to benefit earlier from seamless service provisioning, we present feasible client based VHDA solutions ensuring context-aware QoS (i.e., high throughput, low power consumption) for specific use cases. Unlike related approaches, our solutions run completely locally on mobile phones without any required modifications on network side. In terms of performance analysis, we design a comprehensive simulation model for comparing the suggested VHDA solutions with different complexity levels. Moreover, we analyze the impact of an energy model, which is derived from experimental measurements, on handoff decision quality and the improvement of battery usage. Simulation results illustrate that VHDAs with energy model enhance the QoS of handoff decisions in terms of high throughput and low power consumption compared to a less approach.

The full coverage of continuous voice cloud services is still an open issue in an overlapping network, particularly with regard to heterogeneously public communication network infrastructures. However, hardly anyone of today's service providers offers seamless voice call continuity across these networks due to the high technical integration effort and economic cost. Given this backdrop, we present a feasible Virtual Room based Vertical Handoff (VOOH) solution. In contrast to its counterparts, VOOH is a network operator independent solution that only uses the today's existing wireless access technologies for connectivity purposes. The advantage of VOOH is that, e.g., mobile service providers can immediately provide advantage of seamless voice cloud service without any modifications in the underlying network layers. Besides illustrating the system architecture, we elaborate the operational details of the proposed solution and also analyze its performance based on real implementation over a public network infrastructure. The experimental results demonstrate that the handoff performance of the VOOH solution is not only competitive, but also less expensive compared to similar approaches. Furthermore, a Markov Chain model has been developed to show the effect of an enhanced VOOH solution on the handoff performance as QoS for different traffic loads.

Seamless service provisioning strongly depends on Vertical HandOff (VHO) as key technology that is especially indispensable for the successful management of emergency operations. Latest mobility solutions, including vertical handoff decision algorithms and executions, are related to high technical and economic costs preventing the immediate introduction of VHO technologies. In order to benefit at an earlier stage from seamless emergency services, we present a holistic cost-effective Client based Secure Handoff for Mobile Units (CSH-MU) solution combined with an energy-efficient Vertical Handoff Decision Algorithm (VHDA) that considers the limited system resources of mobile phones (e.g., restricted access to decision metrics, battery life). For performance evaluation and as proof of concept, we develop a comprehensive simulation model and a real experimental test bed to analyze the gain of our energy-efficient decision algorithm for TCP and UDP based applications. For this purpose, the developed energy model is derived from real measurements based on a mobile phone. Confirmed by the results, using the proposed VHDA with energy model enhances the performance in terms of higher throughput and lower power consumption.

In emergency operations, upcoming rescue support systems, like first responders' helmet cameras, raise the need for reliable networks with high data rates and low latency. Typically, in such environments, existing infrastructure networks are often congested or, even worse, destroyed and are therefore useless for rescue personnel. Thereby, rescue organizations have recently offered increased attention to wireless mesh network as a high-performance and low-cost solution for an ad hoc incident network. In this paper, we evaluate the performance of a novel process-oriented way for wireless mesh relay deployment in emergency scenarios. Our approach tackles the problem of the management and the involving of rescue personnel in the placement of mesh nodes. For that end, we propose to integrate wireless relays in fire hose couplings so that these relays cover the whole area of interest in a process oriented way. The packet delivery ratio and round trip time of our solution approach is compared with the performance of the Steiner tree-based theoretical approach for optimized positioned wireless relays. We investigate three scenarios with a rising number of network nodes and senders. The results show that our solution approach achieves comparable performance to its theoretical counterpart, while being much more comfortable for network deployment and post-mission node collection.

This paper analyzes self-organization of ad hoc femtocells within a macrocell infrastructure by using random frequency hopping. Femtocell users select their frequency subbands randomly, so that corresponding femtocells are capable of integrating themselves into macrocells without any exchange of information. No sensing equipment is required in femtocell devices and no time is consumed for self-organization. This makes random frequency hopping highly attractive as a self-organization policy in outage situations, i.e. where macrocell base stations fail and immediate coverage has to be provided by femtocells. We present an exact analytical model for the Bit Error Rate (BER) of femtocell users and access points using random frequency hopping, based on Signal to Interference and Noise Ratio (SINR) and Laplace transform techniques. Moreover, for performance evaluation, the model is applied to frequency-nonselective and frequency-selective channels and is verified by simulations implemented in MATLAB. The results of random frequency hopping are compared to classical resource planning with orthogonal patterns and to systems without resource planning. It is shown that random frequency hopping introduces a reduction of the BER up to a factor of 7 compared to systems where the interferer is transmitting in the same subband as the femtocell user.

In emergency and rescue operations, wireless mesh networks are attracting increased attention as a high-performance and low-cost solution for ubiquitous network access. In this paper, we evaluate the novel secure mesh route discovery protocol PASER, which has been designed to address the mesh network security in such critical environments. The protocol aims to set up reliable ad-hoc routes between network nodes and to combat unauthorized nodes of manipulating the route look-up process. Especially, its lightweight symmetric authentication scheme is noteworthy. The protocol is investigated together with the well-established routing protocols AODV, DYMO, BATMAN and OLSR under various scenario conditions and different attacks. In contrast to its counterparts, the results show that PASER is able to secure the network without noticeable computational overhead. On top of that, the paper reveals that PASER outperforms its counterparts in many cases in terms of packet delivery ratio and maximum end-to-end delay.

Today's heterogeneous networks are still in growing progress with the current introduction of Long Term Evolution. Despite the given infrastructure, hardly any service provider offers seamless voice call services due to the high integration cost. For addressing this issue, we introduce a feasible Virtual Room based Vertical Handoff (VOOH) cloud solution that only applies latest existing wireless access technologies (UMTS, LTE, WiFi) for connectivity purposes. Therefore, VOOH does not require any changes in the underlying network layers that result in low integration effort. Moreover, this solution uses the circuit-switched instead of packet-switched channel of UMTS to guarantee QoS for calls while handing over seamlessly from VoIP (via LTE or WiFi) to UMTS. Besides introducing the VOOH concept and its communication procedure, we present an analytical three-dimensional Markov model for performance evaluation that is validated by our developed event-discrete simulation model. Based on this model, a scalable and cost-effective design of VOOH as cloud service for specific use case examples and the obtained findings are discussed. The results presented in this paper illustrate that an enhanced VOOH (eVOOH) solution improves the VOOH performance up to 56% and thus allows to further minimize economic costs.

The operational time of modern smart-phones with one filling of the accumulator is one of the most important performance parameter for the customers of new devices. This is the reason why extensive research has been performed in the fields of battery design and system optimization with regard on energy consumption. Due to the fact that the radio part of a smart-phone is one of the major energy consumers, a special focus has to be set on different approaches and strategies how to reduce the energy that has to be spent for the transmission of a certain amount of data. However, before new energy saving protocols can be efficiently developed one needs to have detailed knowledge on the different factors which impact the energy efficiency. In this paper, we present a measurement based energy model for IEEE 802.16e conform Mobile WiMAX devices. For this purpose, extensive measurements of the energy consumption of a Mobile WiMAX USB Stick have been performed for different system parameterizations, different data sizes and different application data rates. From the results of the measurements analytic models have been derived which allow for the calculation of the energy that has to be spent per successfully submitted bit. These analytic formulas can now be integrated in system level simulators for the evaluation of the energy efficiency of newly designed protocols.

The travel time estimation of vehicles is a major challenge in the area of dynamic traffic prognosis. Our approach is to increase the number of considered sensor objects in the road network. For this purpose Floating Car Data (FCD) including travel time information of vehicles is transmitted to a server via Long Term Evolution (LTE). In this paper, the benefit of FCD on the accuracy of travel time estimation, depending on the FCD penetration rate is analyzed by an enhanced Nagel-Schreckenberg cellular automaton model. Furthermore, the negative impact of the FCD transmission on the air interface of the cellular communication system is evaluated for various penetration rates and different transmission strategies, including a channel sensitive transmission. Therefore, a close to reality parameterized Markovian model is used. The results show that a penetration rate of a few percent is sufficient for a realistic travel time estimation. The respective influence on the LTE network is tolerable, especially for channel sensitive transmission.

Heterogeneous networks with different wireless technologies increase the availability of Internet services (i.e., cloud services). However, latest mobile phones with at least two interfaces only access these services either via WiFi or UMTS/HSPA. From user's view, an optimal use of heterogeneous networks with today's mobile phones includes seamless data transmission using vertical handoff (VHO) solutions. Caused by high technical and economic costs there exist no usable solutions for readily seamless data transfers. To benefit earlier from seamless service provisioning, we present a feasible client based handoff management solution for mobile phones (CSH-MU) with embedded vertical handoff decision algorithm (VHDA) that considers mobile device's power consumption for WiFi and UMTS communication. As proof of concept and for performance evaluation, a prototype is tested on different mobile phones in a real test bed for TCP and UDP based applications. The results show that the proposed solution is not only a feasible, but also a readily usable solution for mobile phones as well as outperforms network based solutions in terms of handoff delay. A further advantage is the costeffective and immediate integration in current mobile phones without requiring any changes in the used wireless network technologies (e.g., UMTS, WiFi).

Compensating temporary overload or site outage in cellular mobile networks is still an unsolved problem in order to avoid situations where services are unavailable. For this objective, we propose to use a swarm of Unmanned Aerial Vehicles (UAVs) equipped with cellular technology to temporarily offload traffic into neighbouring cells in LTE/4G networks. We discuss relay placement, amount of relays and relay transmit power for overload and outage compensation and provide an analytical model for evaluating system performance in the downlink. We assume that the spatial separation between the aerial service provider, users, and offload eNodeB is beneficial for temporarily increasing spectral efficiency. Our results give evidence, that aerial network provisioning can be used for optimizing mobile networks in overload and outage scenarios.

Emergency and rescue organizations are giving Wireless Mesh Networks (WMNs) increased attention as the key technology to realize network connectivity anywhere, anytime with simplicity and low cost. The WMNs capability for self-organization significantly reduces the complexity of network deployment and maintenance, and thus requires minimal upfront investment. On the other hand, this characteristic makes WMNs prone to a new type of attacks, namely wormhole. While geographical leash-based secure routing such as PASER provides protection against wormhole in outdoor scenarios, in indoor scenarios, this method is ineffective since the geographical position of nodes is not always known (no GPS). For this end, we propose in this paper an efficient security extension (VLX) to secure WMNs routing against wormhole in indoor scenarios using a novel virtual localization technology. We extend the secure routing protocol PASER with our novel approach and evaluate it in different scenarios. The results show that VLX has negligible to no side effect in outdoor scenarios. It provides nearly the same level of security indoors as geographical leashes outdoors. It grants legitimate indoor nodes access to the network despite the lack of their geographical information. It has a light overhead and it is not sensitive to low outdoor to indoor node ratios.

Ad-hoc aerial sensor networks leveraging MUAVs (Micro Unmanned Aerial Vehicles) are ideally suited to cost-efficiently explore unknown or hostile environments for example in case of incidents producing harmful gases or radiation. In this manuscript we present results on the investigations of communication-aware steering algorithms for cooperative MUAV swarms. The mission objective is to achieve a maximum spatial exploration efficiency with the simultaneous ability to self-optimize the communication links by exploiting controlled mobility. While our previous work has mainly considered the performance of the Air-to-Air mesh network, in this paper we focus on the Air-to-Ground-link connectivity control. To achieve appropriate communication links to a central sensor data sink even while exploring larger search areas, an agent-based role management strategy is used to provide suitable multi-hop connectivity. The novel algorithms are investigated for static as well as dynamically changing environments. Key results include a detailed realistic aerial channel characterization and network dimensioning analysis considering numbers of MUAVs and density of ground stations vs. exploration speed and sensor data latency.

The constraints of current monitoring systems, such as stationary data loggers, wired sensor arrays, manned reconnaissance aircraft or remote sensing by means of satellites, have prompted extensive research in wireless sensor networks. Consequently, the use of Unmanned Aerial Vehicles (UAV) for aerial exploration enables the introduction of aerial sensor networks. In this paper we focus on the development of communication aware steering strategies for UAV swarms to achieve quick and comprehensive Spatial Exploration Ratio while maintaining connectivity within the swarm. We develop, enhance and compare two new random based steering strategies, namely Smart Cube and Cooperative RepellingWalk. Our results show that the proposed algorithms simultaneously attain an efficient equilibrium for the mesh connectivity, sensor perception and distribution in the aerial network. Considering autonomous communication awareness enhances the exploration efficiency in terms of swarm coherence and subsequently reliability.

In large scale disaster management operations with hundreds and thousands of victims, fast access to distributed heterogeneous information of different organizations is required for efficient and reliable dispensation of rescue operations. The development of such emergency systems poses a big challenge, if requirements such as performance, security and reliability have to be fulfilled simultaneously. In this paper, we propose a novel Role integrated Certificate-based Single Sign-On (RC-SSO) solution for fast mobile access between first responders at the incident scene and their distributed organizations. Beside the illustration of operational details of the RC-SSO solution, we validate our concept by implementing an experimental prototype as proof-of-concept for a limited number of users. Furthermore, we design a simulation model to determine the performance boundary of our solution under high user density. In contrast to other related emergency system solutions, our approach does not employ a so-called Identity Provider (IDP) for authentication and authorization process and thus reduces additional communication cost as well. A comparison of our proposed solution to an IDP based classical single sign-on counterparts i.e. Security Assertion Markup Language (SAML) shows that our RC-SSO outperforms these by up to 80%. In addition RC-SSO ensures high data security level with negligible overhead compared to the standard security protocol SSL/TLS.

For the reliable control of Micro Unmanned Aerial Vehicles (MUAV) using IP based networks it is of high importance to assure certain quality of service warranties for the transmission of control information. The mobile WiMAX standard IEEE 802.16e defines five QoS Classes which represent different requirements concerning data rate and delay. The scheduling scheme which is used to fulfill these requirements can be independently chosen by the network operator. In this paper we present a novel QoS aware scheduling scheme which can be parameterized in a very flexible and simple way and therefore enables the network operator to set up the service guarantees very accurate depending on the specific requirements. This is of special importance in the case that WiMAX is used for untypical applications such as for example the control of UAVs and therewith the requirements do not exactly match the predefined QoS classes.

Fast and secure availability and retrieval of mission critical information for mobile emergency units over a public communication infrastructure is a key factor that determines the mission success during emergency situations. However, the uninterrupted and secure access to public authority databases by the emergency managers, while they are moving towards the scene of incidence in a highly heterogeneous network, is a challenging proposition with imposition of high technical and economical costs. To cater to such a high priority and specialized service application, there is a need to provide an independent IP mobility service framework that would meet the stringent communication requirements of the mobile emergency managers, without requiring any changes/modifications to the existing public communication network infrastructure. With this motivation, we present a novel secure mobility management solution framework called enhanced Client based Secure Handoff for Mobile Units (eCSH-MU), which extends our previously proposed CSH-MU solution. The eCSH-MU solution is a deployable solution, in which the entire mobility functions are managed by the client side, and hence is independent of the underlying network architecture. This paper will provide the operational and functional concept of the eCSH-MU solution and analyze its performance based on a real prototype implementation.

The performance evaluation of mobile communication systems for time varying environments poses a major challenge. To address this issue, in this paper we propose an approach which extends a common laboratory environment by a fading channel emulator. Hereby, we analyze OFDM based links under complex and realistic radio channel conditions including upper layer protocols. By means of this setup, the influence of velocity on the data rate and Packet Error Rate (PER) of a Mobile WiMAX system is investigated for various Signal to Noise Ratios (SNR) assuming vehicular and pedestrian channel models defined by the ITU. As a result, we analyzed the performance of Mobile WiMAX for Adaptive Modulation and Coding (AMC) dependent on the QoS target, the channel model, the user velocity and the SNR. Hereby, we assumed two partially contrary QoS targets, high data rate and target PER.

For efficient emergency process management in large scale disaster situations, fast and secure access to sensitive information of heterogeneous organizations is an indispensable goal. For this purpose, we propose a novel Multi-tier communication Security model for Emergency and rescue operations (MuSE) that addresses an acceptable trade-off between performance and security of information exchange in those environments. Based on in-depth user requirements analysis, MuSE deals with the communication system upon three tiers: federation, incident network and mobile client. At the federation tier, MuSE specifies an efficient Role- and Certificate-based Single Sign On solution (RC-SSO) for interorganization communication. In contrast to its counterparts such as SAML, RC-SSO does not depend on an identity provider and reduces the SSO steps to a minimum. At the incident network tier, MuSE prescribes a Position Aware Secure and Efficient Route discovery protocol (PASER). It aims to secure the network based on lightweight cryptography. PASER deals with the network in a hierarchical way and supports nodes positions' exchange, providing both satisfying level of security as well as an advanced network management. At the mobile client tier, MuSE restricts the network access to the rescue fighters' clients based on the lightweight standard EAP-PSK and a novel TETRA-based Dynamic key Distribution method (TEDDi).

Industrial applications of IEEE 802.15.4 networks require autonomous network reconfiguration and high mobility support. Therefore, dynamic meshing in case of node failures or changing environmental influences is needed as well as handover strategies when nodes change network domains. Recently a growing number of low rated, low power sensor nodes are deployed within Wireless Sensor Networks (WSN) to support e.g. data acquisition and transmission. Therefore, this work focuses on a mobility management to combine micro and macro mobility aspects in form of mobile ad hoc network (MANET) protocols and Mobile Internet Protocol Version 6 (MIPv6) without changing the original MIPv6 stack. In addition to that the application of 6LoWPAN is addressed to make efficient data transmission feasible within embedded devices by IPv6 header compression which increases resulting payloads significantly. Finally, our approach is validated within a real world industrial simulation environment by combining the protocol simulator OMNeT++ with a ray tracing tool.

In this paper we propose distributed algorithms that solve the coverage problem in 3D space. In particular, we use a swarm of unmanned aerial vehicles (UAVs) in order to explore a possibly unknown area. Actual use cases include the automated acquisition of sensory data in operations that try to gain extensive knowledge about an area. First, we try to efficiently cover a predefined 3D space with multiple UAVs. Second, we target to maintain intra-swarm connectivity during the whole operation. Consequently, we develop and enhance metrics for benchmarking the resulting system with respect to coverage and communication capabilities. The algorithms were optimized towards getting a quick overview that is iteratively refined by getting more accurate data. This principle is similar to mip-mapping in computer graphics.

Vertical handoff (VHO) is the key factor for seamless voice calls in today's heterogeneous networks, but due to the high technical and economic cost of such VHO technologies, barely any service provider currently offers those voice services. To address this issue and to offer full area-covering for calls, we present a feasible and cost-efficient Virtual Room based Vertical Handoff (VOOH) solution. Moreover, our approach simplifies the handling of telephone numbers, protects the privacy of called parties, and requires no changes on existing wireless access networks. Besides, VOOH does not violate contract agreements of most commercial network providers. Instead of the packet switched channel, VOOH uses the circuit switched channel of UMTS for calls in case of handoff from WLAN to UMTS. To investigate the impact of Direct Call Function on VOOH performance, we design an enhanced VOOH (eVOOH) solution. Based on a developed and comprehensive Markov model, the system performance of both solutions (i.e., VOOH and eVOOH) is compared under different traffic loads. The results illustrate that eVOOH performs up to 42.46% better than VOOH.

The success and efficiency of managing an emergency situation (such as rescue operation, fire fighting etc.) depends on enabling a system that would ensure the timely availability of high-quality and latest information to the emergency Mobile Units (MU). The system must ensure the provisioning of relevant information in a secure manner irrespective of the location and/or mobility of the MU. The enabling of secure and ubiquitous communication services, while the MUs are on the move, becomes more of a challenge in heterogeneous network environments due to the fact that the present public cellular network infrastructure does not support IP based mobility management functions (MIPv6, mSCTP etc.) and moreover, the interoperability between different access technologies is not well defined. In view of these restrictions, we present in this paper a novel client-based secure handoff management solution that is self-sufficient, does not require and/or rely on any changes on part of the underlying network infrastructure and enables the use of standard web browsers as well. We will present the operational details of our proposed approach and also analyze its performance based on the real implementation over the public network infrastructure. The performance will also be analyzed with respect to the influence of security mechanism such as authentication and encryption on the performance of the vertical handover process and compare the results with the standard mobility solutions such as MIPv6/NEMO and mSCTP based on experimental test beds.

Miniaturized unmanned flying robots, also referred to as MUAVs (Micro Unmanned Aerial Vehicles) open up completely new fields of innovative applications in the areas of civil security, cellular networks, surveying, entertainment and media. The AVIGLE project is based on the vision of a novel, widely applicable avionic service platform which supports multiple high-tech services by using open interfaces. Recent developments in the area of lithium polymer batteries and carbon fiber-reinforced plastic materials let MUAVs become an aerial platform, that can be equipped with a variety of sensors such as image or time of flight (ToF) cameras. Furthermore, it is also possible to mount communication technologies on the platform in order to let the MUAVs work as communication hot spots or relais at places where no cellular networks are available. In this paper we present a system of systems concept of an Unmanned Aerial System working as a service platform for different Concepts of Operations (ConOps).

IEEE 802.15.4a Wireless Sensor Networks (WSN) and IEEE 802.11b Wi-Fi networks are operating in the 2.4 GHz band. In order to examine co-channel interference scenarios and the impact on key performance indicators, this paper combines an analytical model, measurements and simulations. It is a pilot study which reflects the interference caused by the new IEEE 802.15.4a-CSS (Chirp Spread Spectrum). An analytical model for adaptive bitrate adjustment and throughput optimization reflects the performance impact and the system behaviour of IEEE 802.11b. Measurements are used for model validation before simulation results show the system behaviour for changing node constellations, radio conditions and system setups. The validated simulation model is then applied to a use case scenario for indoor localization, which shows the impact on the performance of a realistic co-existence scenario.

In dynamic networks such as highly mobile swarms of micro unmanned aerial vehicles (MUAV) the round trip time (RTT) is a crucial key figure for the reliability and viability of the system. For this purpose the deployed protocols and underlying network architecture have to be designed in awareness of the available and allocatable throughput. In this paper we are investigating a network and protocol design of a novel system approach for aerial remote sensing by means of MUAV swarms. Since effectiveness and performance ability of the navigation, control and guidance algorithms depend not only on sense and avoid or collision avoidance capabilities of each MUAV, we are particularly analyzing the delays and RTT between each node in the considered agent-based avionic mesh network. As the proposed protocol design has got a significant impact on the processing delay of embedded systems, a major focus is given to the RTT analysis of a multi-hop WLAN mesh network. Next to the self-interference caused in the experimental testbed, external co-channel and common channel interference are considered to emulate a realistic environment.

The present day challenge during emergency response is the lack of availability of latest and detailed information to the emergency personnel about the incident scene. The success of the fire mission planning depends critically on the ubiquitous availability of high-quality information (e.g., building plans, aerial photos, hazardous material databases, medical patient data) while the fire fighters are on the move. The information in question is usually distributed over several public authorities offices and, as of today, is requested of them via the traditional time-consuming postal services. Thus to ensure a secure and reliable access to such distributed databases while on the move, we present a comprehensive mobile framework that enables the interoperability of diverse information providers by using a newly proposed emergency Role based Authentication/Authorization Protocol (eRAAP). Moreover, we extend and integrate some important services in the proposed framework; such as the RFID fOr emergencY (ROY) feature that will enhance the system usability for fire brigades during rescue missions. For enabling seamless mobility support to such a system, the Network Mobility (NEMO) protocol is integrated in our framework. Besides providing functional details of the proposed framework, we will also provide performance analysis in terms of the response time and handover process based on our experimental testbed.

The European satellite navigation system Galileo is ready for service in the year 2013. New services are needed to be investigated to enlarge the capabilities of the system. In this paper we propose a Galileo Search and Rescue Short Message Service (Galileo SAR-SMS) to enable sparse text communication between endangered persons and rescue forces to optimize the process of retrieval. Galileo SAR is an addition to the well established humanitarian satellite-based SAR System Cospas-Sarsat (CS) and will be fully compliant. In succession we developed a concept for a Short Message Service which is adaptable to the existing system by exploiting the improved channel characteristics of the Galileo system. Primary, the integrated feedback channel of Galileo enables a two way communication. This paper presents a description of the service flow, message design and channel properties of Galileo SAR-SMS. The performance of the developed protocol architecture is shown to demonstrate the feasibility of the new service.

Multimedia group communication services in mobile networks as a replacement for traditional trunked radio communication have attracted a lot of attention recently. This paper presents results from a newly developed simulation model to study the transfer of multimedia streams in group communication systems within different setups. As the real time ability of such a system has been identified as the key success feature, the focus of the presented studies is placed on the variation of the users' upload data rate, the resulting overlay network structure and it's impact on the overall transfer delay.

IETF has proposed Fast Mobile IPv6 (FMIPv6) protocol, a seamless handover protocol, that reduces packet loss during the handover process by tunneling packets from the MN's previous IP subnet to the new IP subnet where they will get buffered. These buffered packets will get forwarded to the MN once it becomes IP capable in the new subnet. Although packet tunneling and buffering is an effective strategy to reduce packet loss during the handover process but it will not only incur a high tunneling load on the link between the previous and new subnets, especially for CBR traffic, but will also account towards increased processing load in the access routers due to successive tunneling and de-tunneling of packets. This tunneling load is also dependent on the timing of the FMIPv6 handover decision which in turn is directly dependent on the location and speed of the MN. In this paper we propose a mechanism called Proactive Bindings for FMIPv6 (PB-FMIPv6) which can remedy the above limitations. Simulation results have shown that our mechanism not only reduces the tunneling load during the handover process but it also decouples the handover decision from the location and/or speed of the MN.

Despite recent advancements in cellular mobile network architectures, full coverage for seamless handoffs is still an open issue. Moreover, cost efficient calls for the end users in urban regions with very high coverage can be improved. In many areas overlapping networks like WLAN, GSM and 3G provide alternative wireless links. Ideally, the user should be connected to the most attractive network any time according to predefined priorities using an automatic handover. In this paper, we present a framework for vertical handoff with dual- mode (cellular/WLAN) devices by using the conference bridging functionality of a PBX gateway. The communication between client and server will be realized via an overlay signaling channel. Based on the work done in we extend and integrate some important services in the proposed framework: The ICAS (Individual Communication Address Space) feature allows users to be reachable by an alias name without revealing his real phone number. Our proposed Presence Enabled Phonebook shows the system presence and status information of the users' contacts. Beyond the illustration of the framework, the quality and reliability of voice and overlay channel were analyzed in terms of a vertical handoff.

To manage the mobility of mobile networks, IETF has proposed MIPv6 based network mobility (NEMO) Basic Support protocol. However the NEMO protocol has severe performance limitations and does not specify the route optimization method for mobile networks and does not take into account the operational and functional complexities involving nested mobile networks. The Next Generation Network (NGN) however warrants and demands optimized protocol operation capabilities in terms of supporting nested mobile networks that would meet the stringent quality of service requirements imposed by NGN. This paper presents NERON: NEst Route Optimization for NEMO, an efficient and scalable approach that aims at enabling nodes behind nested mobile networks to use optimized communication paths with zero tunneling overhead and end-to-end delay, irrespective of the depth of the nest, with minimum changes to the base MIPv6 protocol and without introducing any new network entities. The performance results are validated using an accurate simulation model.

Seamless IP mobility protocols like Fast Mobile IPv6 (FMIPv6) requires the knowledge of candidate access routers, to which the mobile node will hand over its connection to, well in advance while the mobile node is still connected to its current AR. The candidate access router discovery (CARD) protocol is one such protocol that provides the identity and capabilities information of the candidate access routers to the mobile node prior to the initiation of handover. The CARD protocol, however, specifies a very generic mechanism and it does not take into account the stringent requirements of fast moving mobile nodes and real time communication sessions. In this paper we present an enhanced mechanism that can enable a fast moving mobile node to discover the identity and capabilities of the candidate access routers which may be adjacent geographically but not topologically to its current access router.

Indoor localization is an emerging subject in the field of wireless sensor networks (WSNs) and even more in wireless mesh networks (WMNs). Off the shelf solutions are still missing, even though the new IEEE 802.15.4a/ZigBee standard enhances the precision of ranging. This paper describes a hybrid localization approach combining time of arrival trilateration (TOA) as being proposed in the IEEE 802.15.4a and standard meshing aspects of the IEEE802.15/TG5. The evaluation is done in a multi-scalar simulation environment which models mobility, radio channel properties, protocol interaction and their cross effects simultaneously. This enables the use of realistic scenarios in which the performance of the localization method can be compared to existing approaches on a fair and reliable basis. The hybrid approach shows a significant precision and reliability gain while the installation effort and protocol overhead is kept at a very reasonable level.

The European satellite navigation system Galileo is a highly promising technology with communication capabilities providing an enhanced search-and-rescue (SAR) service in combination with the current Cospas-Sarsat system. Our development focuses on a high reliable hybrid communication infrastructure for wide area fire fighter missions. The combination of a terrestrial network infrastructure and the enhanced Galileo SAR service supports communication out of coverage in distress situations. The improved Galileo SAR service is compared to the actual Cospas-Sarsat implementation in this paper. Architectural building blocks which are particular belonging to either Cospas-Sarsat or Galileo are highlighted. Simulation results reflect disparities in quantitative figures from which performance indicators for the SAR service quality are deduced. The necessity of Galileo SAR for high reliable and fast applications in emergency situations is shown and an optimal number of additional Galileo satellites carrying SAR payload is derived.

The need for high reliability wireless communication has triggered the interest in mesh networks recently. Even though IEEE 802.15.4/ZigBee offers basic meshing capabilities, it still lacks tight synchronization of the whole network by means of beacons in this topology mode. However, the goal of an energy efficient operation requires a close synchronization for effective duty cycle management. Beacon scheduling mechanisms for the cluster-tree topology on centralized basis are known, but extensions to meshing have not been defined. Therefore, this paper introduces a distributed beacon scheduling (DBS) algorithm to overcome this deficiency while addressing robustness and performance during reconfiguration, too. The results show the applicability of the approach by extensive simulations even for large networks of several thousand nodes in wireless mesh networks (WMNs) based on IEEE 802.15.4/ZigBee.

WPAN technologies such as IEEE 802.15.4 have been introduced to allow for energy-efficient communication of control and sensor data. In certain application areas, it is beneficial to leverage such a wireless control network to transfer multimedia data. A concrete example is a wireless cabin control network, which is deployed to control the ambient environment (light, sound, climate etc.) of a vehicle cabin (such as a bus, a train or an aircraft). Therefore, in this paper we introduce a distinct system architecture, called Ambient Multimedia ZigBee Service Environment (AMuZE) for WPANs and describe the results of performance evaluations achieved by simulations in an experimental test-bed. We demonstrate that it is possible to use an IEEE 802.15.4 networks to broadcast voice messages (e.g. for instructions in case of emergencies) as well as continuous sound streams (e.g. to create ubiquitous ambient sound environments), while at the same time control functions are performed with high reliability. For the control of the ambient lighting, video image transfer has been integrated to enable an optical feedback channel for auto-configuration of the system. The performance evaluation has been carried out by integrating the embedded device code into an event driven simulation framework. This approach allows for a realistic assessment of the performance in the simulation. As an asset, the optimized algorithms may be transferred directly to the target devices. Finally, results from the validation of the proposed concept in an experimental test-bed are presented

The estimation of correct triggers for handover in cellular networks belongs to the critical tasks for accurate network operation. The importance of seamless handover even rises according to the increasing number of available radio access technologies, demanding for a reliable vertical handover. To aid the handover process, mobility prediction technologies gain interest and provide the possibility to prepare for handover in advance. The approaches presented here feature prediction of macro-mobility as an additional measure to aid handover decisions. The prediction is based on statistical data gained from the observation of movement across multiple cells. One of the main features lies in a generic user centric calculation of the most likely next hop during user movement, compared to network specific technological methods

Group communication services in cellular networks have attracted a lot of attention recently and provide an entirely new way of communication in cellular networks. To guarantee acceptable service quality, special focus needs to be put on the impact of heterogenous wireless link qualities within one group of users. In this paper, we propose a light-weight IP-based group communication system, which relies on a network-assisted peer-to-peer architecture. We present results of performance evaluations related to the efficiency of group communication in heterogenous environments (i.e. using different wireless link technologies and operating the system across network boundaries). From the detailed results a "key performance indicator" (KPI) for group communication services is derived, which provides a generic mark to rate a push group-communication system. Furthermore we derive recommendations for the use of floor control mechanisms in situations, where some users within a group are affected by strong link delays

The analytical methods and the analysis as well as the simulative tools developed for the performance evaluation and validation of the dedicated vehicle-roadside short-range communication (DSRC) protocols proposed for standardization in Europe (CEN/TC 278/WG9) and in North America are presented. First, the basic concept of the medium access control protocol for vehicles entering the communication zone of a roadside beacon is described. Then, the analytical methods and tools (MARCO) for medium access using new multi-level Markov chain models are presented, in which the dynamic arrival of up to n vehicles and the completion of the full transaction (AFC, automatic fee collection) is taken into account. Furthermore, the characteristics of the two simulative tools SIMCO-F/DSRC and SIMCO3++/DSRC are described. These tools are validated by comparison of the analytical results of the MARCO tool with the results of the simulative tools. Finally, the advantages/disadvantages and the recommended usage of these methods and tools for the validation of the dedicated vehicle-roadside short-range communications proposed for standardization as well as for the DSRC system calibration are discussed.

Vehicle-roadside communication (VRC) systems are expected to be an important element of a future integrated road transport environment (IRTE), which aims to enhance the safety and efficiency of road traffic. For the standardization of suitable communication protocols, choices between several options are made on the basis of performance evaluations. The results of a performance evaluation of VRC systems using different medium access control (MAC) schemes proposed for standardization are presented. The asynchronous protocol proposed within the European standardization group CEN TC 278 WG 9 is compared with a synchronous protocol being proposed for the IVHS communication architecture. After introducing the basic requirements for vehicle-roadside communications, the characteristics of both protocol options are presented. Next, reference scenarios are introduced, which take into account different system configurations (SDMA/TDMA vs. a pure TDMA solution), application profiles (AFC-like interactive dialogue combined with broadcast-oriented route guidance), traffic and channel characteristics. The results of simulations, which have been carried out based on these reference scenarios, are discussed in detail. It is shown, that, in the analysed scenarios, the asynchronous protocol achieves better results than the synchronous option, because it produces less overhead and is able to cope better with transmission errors.

A proposal for a communication architecture for road transport telematics (RTT) infrastructure networks is presented. First, the technical approach which has been set up to cope with the relevant subjects is introduced. Then, the communication requirements of RTT applications are considered. Following that, the protocol profile is defined which includes a protocol stack for the vehicle-roadside communications via beacons (DSRC). Thereupon, the protocol evaluation and the co-operation between the beacons and the fixed network ape discussed. Finally, the performance analysis of medium access control (MAC) protocols for the DSRC and for the fixed network are addressed.>