Industrial applications with closed-loop control demand ultra-reliable, low-latency communication in fixed and mobile networks. Integrating Time-Sensitive Networking (TSN) with 5G offers a promising solution. However, coordinating prioritization across two different networks is challenging. This paper demonstrates an integrated 5G–TSN solution for end-to-end traffic prioritization. To demonstrate closed-loop stabilization, we employ inverted pendulums, stabilized by controllers over 5G networks. Our solution ensures prioritization by TSN in the wired domain and by 5G Quality-of-Service (QoS) mechanisms in the wireless domain.

This paper presents a Markov-based system model for microfluidic molecular communication (MC) channels. By discretizing the advection-diffusion dynamics, the proposed model establishes a physically consistent state-space formulation. The transition matrix explicitly captures diffusion, advective flow, reversible binding, and flow-out effects. The resulting discrete-time formulation enables analytical characterization of both transient and equilibrium responses through a linear system representation. Numerical results verify that the proposed framework accurately reproduces channel behaviors across a wide range of flow conditions, providing a tractable basis for the design and analysis of MC systems in microfluidic environments.

We consider a formalism ofquantum compiler functions– functions that map unitary matrices to corresponding gate-circuit approximations – and prove the infeasibility of digitally computing such functions. Since the gate-circuit model of quantum computing emerged, much research has been conducted to find algorithmic solutions to thequantum compiler problem. The renownedSolovay-Kitaev theoremproves the existence of quantum compiler functions that provide low-complexity gatecircuit approximations to arbitrary unitary matrices, which is indispensable for the practical feasibility of gate-based quantum computing. However, the mere existence of such functions does not imply theirrealizabilityby means of analgorithm– a constructive procedure executed by aTuring machine. In fact, no algorithm for computing any quantum compiler function is known today. The present article demonstrates that no such algorithm can exist. We prove that no quantum compiler function can satisfyBanach-Mazur computability, which is a formalization of algorithmic feasibility with (mathematically) weak requirements. In consequence, there definitely does not exist a Turing machine that computes any quantum compiler function in the above sense, nor can there exist aconstructive proofof the existence of any such function. Furthermore, we discuss proposed methods of quantum compiling and analyze them in the context of our results.

Extended Reality (XR) applications are emerging as a transformative technology, significantly enhancing user experiences in various domains, such as marketing and in-store shopping. In this context, object detection is a crucial component that enables XR-based gadgets to identify and interact with products in real-time. Object detection tasks typically employ AI-based models like YOLO (a.k.a. You Only Look Once), which require substantial computing power and currently are often offloaded to cloud servers. However, with advancements in hardware and Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, In-Network Computing (INC) has emerged as a viable alternative. This paper proposes the application of INC for XR applications, specifically for in-store shopping use case, by leveraging the capabilities of programmable network devices to distribute computational tasks across the network. By integrating and enabling INC, we aim to enhance the efficiency and scalability of the evaluated XR applications while reducing latency, bandwidth consumption and reliance on cloud servers.

The European Union is implementing measures to reduce automotive accidents. In response, the automatic alerting eCall system is a key initiative to respond more quickly and reduce fatalities. However, the system faces significant challenges in areas with limited terrestrial network coverage. Non-Terrestrial Networks (NTNs) present a promising solution by extending connectivity to remote and underserved regions. In this study, we adapted our NTN simulator, INTANS, to implement and evaluate the eCall system within a 6G NTN framework. Our research focuses on measuring and analyzing key performance indicators, such as end-to-end latency and packet loss rate, through detailed simulations. Initial results demonstrate that the latency observed in our simulations meets the requirements set forth in 3GPP Release 19, confirming the system's capability to deliver emergency communications. Furthermore, these findings underscore the necessity of a robust Inter-Satellite Link (ISL) and optimized satellite handovers to enhance the reliability of the eCall system. Future work will aim to refine these results by exploring advanced communication protocols like TCP, thereby optimizing re-transmission strategies and further improving system reliability.

Recent advances in networking technologies, such as in-network computing (INC), have demonstrated significant potential for mobile networks. Among these, Random Linear Network Coding (RLNC), a class of forward error correction codes, has proven especially promising for enhancing reliability. RLNC reduces latency by transmitting additional coded packets, making it well-suited for supporting Ultra-Reliable Low Latency Communications (URLLC), a key requirement in nextgeneration mobile networks. To fully harness the benefits of RLNC in mobile networks, it is essential to closely monitor its operations to design effective RLNC schemes. This need arises from key factors, including the dynamic nature of mobile environments and the continuous emergence of new applications. However, achieving this in traditional mobile networks remains challenging due to their closed architectures and vendor lockin. We propose HawkVision, a monitoring solution for RLNC operations that leverages the flexibility of Open Radio Access Network (O-RAN). HawkVision supports monitoring various RLNC schemes, such as Sliding Window and Systematic Block Code, within mobile networks. It uses xApps in O-RAN to observe RLNC behavior in the RAN. We implement HawkVision using FlexRIC, a widely used O-RAN platform, and deploy a Key Performance Metric (KPM) xApp to collect relevant metrics. Testbed results demonstrate HawkVision’s effectiveness in monitoring the operations for different RLNC schemes.

The rise of quantum computing poses a major threat to traditional ciphers, specifically making the Radio Access Network (RAN) vulnerable to future quantum-based attacks. To address this challenge, this paper proposes quantum-inspired RAN (Q-RAN), a next-generation framework designed to future-proof telecom networks against quantum threats. As a foundation, to evaluate, we integrate quantum network simulators with the Open-Source OpenAirInterface (OAI) stack to deploy a 5G standalone network capable of supporting quantum operations. We analyzed these, and the best fit is taken for quantum proofing. This paper considered four different quantum simulators — QuNetSim, SimQN, SeQuence, and NetSquid — for integrating with the 5G Open source stack. The framework generated by these simulators is called Quantum Gateways (namely Qtunnels) enables seamless message exchange between quantum and classical RAN components. This integrated architecture showcases how quantum simulators can be harmonized on existing 5G infrastructure, establishing a resilient and adaptable telco ecosystem catered to the quantum era or post-quantum era. In addition, this paper quantifies the Qtunnel impact — F1 Interface — between the Central Unit (CU) and Distribution Unit (DU) of RAN. The results show that the Post Quantum Cryptography (PQC)-enabled F1 interfaces provide a two-fold overhead delay when compared to the benchmark delay, however, the PQC-enabled RAN interfaces are resistant to quantum threats.

In today’s fast-evolving technological landscape, the Internet of Things (IoT) is fundamentally reshaping how systems connect, interact, and exchange data. As billions of devices become interconnected, the IoT brings both unprecedented opportunities and significant challenges across various domains. Emerging technologies, particularly quantum communication networks, offer transformative solutions by enabling ultra-secure data exchange within IoT infrastructures through advanced techniques such as Quantum Key Distribution (QKD). However, quantum link delay remains one of the key challenges that hinder the effective utilization of these networks. Traffic engineering is a robust method to address this challenge and optimize the performance of quantum networks. This technique involves assessing the current state of the network and making dynamic adjustments based on real-time conditions. Despite its proven benefits in classical systems, its role in quantum networks remains largely unexplored, with no comprehensive framework to date. As a result, in this paper, we propose a framework for quantum traffic engineering and discuss its core components—non-invasive measurements, quantum traffic matrices, data analysis, and performance control. Additionally, we integrate the Particle Swarm Optimization (PSO) algorithm into our framework to minimize the quantum link delay. Ultimately, this work establishes the foundation for researchers in quantum network traffic engineering and sets the stage for continuous, and dynamic monitoring of these networks.

In today’s fast-evolving technological landscape, the Internet of Things (IoT) is fundamentally reshaping how systems connect, interact, and exchange data. As billions of devices become interconnected, the Internet of Things brings unprecedented opportunities and significant challenges in various domains. Emerging technologies, particularly quantum communication networks, offer transformative solutions by enabling ultra-secure data exchange within IoT infrastructures through advanced techniques such as quantum key distribution. However, a significant obstacle to the efficient use of these networks is the lack of current status knowledge of network parameters, which is essential for continuous and dynamic management through quantum traffic engineering. In this paper, we propose the novel concept of quantum traffic matrices as a foundational framework to capture the dynamic operational state of quantum networks. We begin by distinguishing classical traffic matrices from their quantum counterparts. We then identify and present mathematical models for eleven key quantum network parameters that are essential to enable continuous and dynamic management of quantum networks. Our approach and results serve as crucial input for the development of quantum traffic engineering strategies, paving the way for intelligent control, optimization, and resilience enhancement of future quantum communication infrastructures.

DNA-based Molecular Communication (MC) has received significant attention for its potential in nanoscale bio-communication systems such as drug delivery, biosensing, and the Internet of Bio-Nano Things (IoBNT). This paper presents a theoretical model for DNA-based MC in spatially confined environments. In the proposed framework, the transmitter (TX) releases complementary DNA (cDNA) molecules in the channel, which reversibly bind to immobilized probe DNAs at the receiver (RX). The stochastic dynamics of diffusion, binding, and release are characterized by a Markov process. Closed-form channel characteristics, including the Channel Impulse Response (CIR), equilibrium distribution, and settling time, are derived. System performance is evaluated through both the proposed model and Particle-Based Simulation (PBS). The results demonstrate that the proposed model effectively describes the dynamic behavior of DNA hybridization-based MC, offering accurate predictions with significantly lower computational complexity than conventional simulation methods.

To meet the stringent requirements of emerging 5G use cases that demand high reliability and low latency, the potential of in-network computing is realized by deploying Random Linear Network Coding (RLNC) recoders directly within the network infrastructure. However, the choice of network infrastructure configuration can significantly impact traffic latency as demonstrated in this paper. To investigate this, we compare the performance of RLNC recoder when implemented on two different switches, the Cisco Catalyst 9500 (C9500) and the Cisco Catalyst 9300 (C9300), focusing on reducing one-way delay (OWD). Our results demonstrate that the C9300 significantly outperforms the C9500, with$\mathbf{9 5. 6 \%}$of haptic packets recovered within 70 ms compared to 85.8% for the C9500. Notably, the C9300 consistently minimizes packet loss better, especially in haptic traffic, where the C9300 with RLNC recoder reduces packet loss to nearly zero. Additionally, under stress conditions with a 10 Mbps transmission rate, the C9300 continues to excel in reducing OWD, although the in-network computing performance in both switches exhibits similar reliability challenges. This study focuses on analysing the performance of RLNC recoding in cloudnative$\mathbf{5 G}$systems under various hardware conditions.

The key features of 5G, particularly URLLC and mMTC, are designed to achieve low latency and high scalability. To support these requirements, a proposed approach for random access scenarios, known as the GF scheme, eliminates handshakes between the BS and mobile users to reduce latency and accommodate a large number of devices. However, this introduces a new challenge known as AUD, where users send messages indicating their activity status, requiring the BS to detect and decode them. Decoding these messages, however, demands high computational complexity, which increases exponentially with the number of users. Several algorithms, such as ZF and CCR, have been proposed to mitigate this complexity, but they suffer from suboptimal performance. On the other hand, the optimal solution, known as ML, performs well but suffers from high complexity. To address this, quantum algorithms, like Grover's algorithm, have been proposed due to their ability to reduce search complexity while also keep detecting active users performance better. However, Grover's algorithm requires adaptation in this context, as the optimal number of iterations depends on the number of solutions, which is always unknown in the AUD case. To address this, the BBHT and DHA algorithms have been proposed to minimize complexity when the solution is unknown, but both still exhibit relatively high computational demands. In this paper, we propose an Enhanced BBHT that aims to reduce this complexity while maintaining detection performance. Our findings demonstrate that the Enhanced BBHT reduces computational complexity while keeping the performance stable.

Evolving communication technologies and standards enable novel IoT use cases, such as real-time edge or fog assistance for distributed sensing and control in smart environments. Synchronizing the system state at centralized management entities with distributed components is a network resource-hungry task. Semantic communication is envisioned to cope with rapid network traffic increase and associated performance degradation by optimizing resource utilization for the application goal. Identification (ID) codes are a novel semantic technology that reduces network traffic for remote synchronization by representing a potentially multidimensional system state with a short tag. This work exploits ID codes in the intelligent transportation context. The vehicles in a truck platoon use ID codes to synchronize their distributed control decisions with the centralized edge server. We present one of the first frameworks demonstrating the advantages of using ID codes in practical scenarios. We show that the truck platoon management utilizing ID codes is$\sim 40 {\%}$more efficient than a fully distributed control scenario and never lags behind a fully centralized control. Moreover, ID codes reduce network traffic by$\sim 4$times compared to the latter.

To address the demand for near real-time applications in 6G networks, we propose FLINT, a context-aware task scheduling system for in-network computing. Unlike hardwaredependent approaches, FLINT utilizes dynamic compute nodes connected to routers, enabling flexible hardware utilization. By employing static code embeddings from a large transformer model and small metric estimation models on each compute blade, FLINT efficiently assigns unseen tasks based on their binaries and arguments. This cooperative scheduling approach between blades and the central router allows for dynamic deployments and blade exchangeability. Evaluation of diverse WebAssembly tasks and hardware demonstrates FLINT's effectiveness, achieving an F1 score and accuracy of over 0.9 on our taskset while maintaining low response time overhead.

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

The security of the Internet of Things has emerged as a critical concern in the age of interconnectivity. Quantum computers pose a threat to the public key-based Rivest-Shamir Adleman methods currently utilized in encryption. Quantum keys, which can create a Quantum Key Distribution network based security protection system for the quantum Internet of Things, can theoretically provide unconditional security. Among these technologies, Continuous Variable Quantum Key Distribution is currently experiencing rapid and significant growth. This can be attributed to its capacity to support multiple channels and its ease of integration into current optical communication networks. However, the absence of an efficient key allocation system may waste the generation of quantum keys and decrease the overall quality of service. As a result, this paper proposes a Quantum Key Management scheme based on continuous variable Quantum source and application scenarios. The scheme allocates quantum keys from the quantum source. Quantitatively weights the security requirements of key requests in proportion to the quantum key. Our findings indicate that the system successfully manages the keys; however, excessive network traffic can overwhelm the system’s capabilities, leading to increased errors. This underscores the importance of optimizing traffic load to ensure reliable Continuous Variable Quantum Key Distribution

While routing methods in the literature often assume the use of quantum memory, this study proposes a routing algorithm designed for memoryless quantum networks. The algorithm leverages lexicographic optimization principles to hierarchically prioritize and optimize multiple objectives such as distance and capacity. This approach identifies the shortest paths between source-destination pairs within the decoherence time and selects a path that satisfies the minimum capacity require ments. To prevent entanglement fidelity from dropping below the desired threshold, purification is applied. Simulation results demonstrate the algorithm’s ability to manage latency while enhancing throughput effectively. The study comprehensively analyzes performance metrics under varying capacity values, request numbers, and threshold fidelity levels. Additionally, to the best of our knowledge, it is the first study to calculate latency, including queuing delay, in the context of quantum networks. Future research aims to explore optimal threshold fidelity values that balance throughput and latency performance.

With the increasing security demands of nextgeneration communication standards and the looming threat posed by quantum computers to traditional encryption methods, quantum key distribution (QKD) has emerged as a promising solution in domains such as military, government, and finance, with significant potential for further commercialization. Although QKD is theoretically unconditional secure, practical implementations often face vulnerabilities arising from imperfections in channels and devices, as well as inherent characteristics of QKD protocols. This paper investigates the probability of eavesdropping from the perspective of error rates using probabilistic analysis. Such an approach not only facilitates the detection of potential eavesdropping but also helps estimate the amount of leaked information, providing a basis for subsequent countermeasures like privacy amplification. Additionally, the probabilistic analysis sheds light on the robustness of communication channels with varying quality against eavesdropping attempts.

Stochastic network codes for consensus testing (CT) via a relay are proposed, where each of two or more parties knows a message and can find out if all these messages are equal, e.g. as an integrity check in a decentralized storage system or the control of mobile autonomous robots. The proposed codes achieve the CT capacity for memoryless uplinks channels when common randomness (CR) is available and no local randomness is used. With only local randomness at the edge nodes, upper and lower bounds for the capacity are given. The lower bound is achieved by CR generation via decode-and-forward transmission, and then using a common-randomness (CR)-assisted code. The upper bound is imposed by the CT over the uplink, when this consists of independent parallel channels to the relay. A recent derandomization result for encoders shows that, unlike deterministic encoding and CR shared between both encoders, the use of local randomness prevents the relay from successfully testing consensus. Therefore, in the proposed coding scheme, the relay recodes only to transmit random seeds and message hashes generated with these seeds. This scheme relies on an underlying CT code based on almost-universal hashing, where hashing is done with random seeds.

Traditional RAN handover strategies often trigger excessive handovers in V2X systems, leading to unstable links and increased communication overhead. Open and softwarized RAN introduces centralized, intelligent control through custom xApps running on the RAN controller, enabling real-time handover and beam management. However, its application to beamforming-based handovers in multi-vehicle scenarios remains underexplored. In this work, we propose three strategies to minimize handover frequency in both analog and hybrid beamforming systems, with and without relay support. We also outline a simulation framework based on NS-3 and SUMO, the traffic and mobility simulator, to evaluate the effectiveness of our proposed solutions.

Evolution towards 6G communication networks aims to support burgeoning alternative technologies in order to overcome traditional classical trade-offs in various aspects of network performance, security, and latency. With this pursuit also striving to meet more nuanced aspects of purported trustwor-thiness standards, the soaring complexity of the next-generation networks is unsurprising; additional classical resources (third-party random number generators, pre-shared keys, etc.) are thus becoming increasingly relied upon for optimal network functioning. In this paper, we explore the potential of quantum technology and how it might be possible to utilize quantum phenomena like quantum entanglement to transfer decisional information in specific network contexts while also inherently providing destination anonymity without requiring additional classical resources.

Routing plays a pivotal role in quantum communication as it directly impacts the efficiency, reliability, and scalability of quantum networks. While several studies in the literature have explored routing algorithms leveraging quantum memories, current quantum memory technologies are unable to simultaneously achieve high fidelity, extended storage durations, wide bandwidths, multimode capacity, and high efficiency. To address this limitation, our study focuses on fidelity-guaranteed entanglement routing within a memoryless network architecture, employing both distributed and centralized routing approaches. Utilizing our proposed routing algorithm, MEFID, we achieved a throughput of 72 qubits per second under a fidelity threshold of 0.8 and within three iterative rounds. By integrating a purification process to ensure that the final fidelity consistently exceeds the specified threshold, our algorithm facilitates the development of robust and high-performance quantum networks.

Open interfaces in 5G Radio Access Network architecture facilitate flexibility, multi-vendor options, and agile development. Open RAN architecture utilizes open-source software (OSS) and proprietary products. The OSS enables numerous developers to participate in Open RAN technology and provides testing capabilities for interoperability and security assessment. Leveraging the openness of the software, developers can examine code flaws and identify overlooked vulnerabilities. To enhance the security of open-source software, particularly OpenAirInterface (OAI), we have conducted jamming and fuzzing attack tests for 5G private networks, focusing on Core and RAN networks. These threats were implemented on dedicated testbeds. The risk and impact of these threats have been elucidated to provide valuable insights into their prevention. In conclusion, our evaluations and findings will augment the technical capabilities of a resilient 5G system, encompassing reliability, availability, robustness, and security while safeguarding overall privacy.

The application of hybrid scheduling for both conventional data transmission and quantum key distribution (QKD) in next-generation hybrid 6G networks is examined in this research. The study simulates transmission over Multimode Fiber (MMF), where QKD keys and classical packets are sent in parallel without a scheduler, and over Single-Mode Fiber (SMF) utilizing time-based and event-based scheduling protocols. Bit Error Rate (BER) and Packet Error Rate (PER) comparisons between the two protocols show improvements in latency and resource allocation efficiency achieved by the SMF scheduler. Concurrent transmission of classical and quantum offers the possibility of boosting data throughput. The effect of cross-talk coefficient on the accuracy of transmitted data is also elaborated. These findings highlight the necessity for customized methods in hybrid quantum-classical networks by shedding light on the performance and adaptability of scheduling techniques on SMF and the advantages of space division multiplexing on MMF. For MMF specifically it showcases noise resistance quantum data transmission.

Measurements of one-way delay in 5G networks are essential for evaluating system performance in various practical scenarios. This paper presents a comprehensive study comparing three different packet generators on real-world deployments and their associated challenges. We explore different topolo-gies, types of terminals, and network congestion conditions, discussing the suitability and limitations of different packet generators. Moreover, we propose practical considerations to overcome limitations in capturing dynamic and heterogeneous 5G network environments and provide insights for system designers in precisely measuring and characterizing one-way delay in 5G networks for real-world scenarios.

Routing is essential for quantum communication since it controls the effectiveness, dependability, and scalability of quantum networks. Although research on routing algorithms using quantum memories has been published in the literature, there aren't any quantum memories that can simultaneously achieve high fidelity, long storage duration, wide bandwidth, multimode capacity, and high efficiency. Therefore, in this study, we developed the fidelity-guaranteed MEDIRA routing algorithm, utilizing a distributed routing approach in a memoryless quantum network. As a result of the algorithm, we achieve a throughput of 17.2 qubits per second, for a demand fixed between 1 and 20. To the best of our knowledge, this is the first work that utilizes a queuing approach in a memoryless distributed routing algorithm with fidelity assurance.

Wireless Mesh Networks can be a valuable enhancement to network infrastructures in many contexts. Mesh nodes can extend the range of WiFi networks by acting as relays between source and destination nodes, which can be crucial for contexts where cabling is something to be avoided. Due to unforeseen degradations in the environment, however, certain links within the mesh may fail, leading to a lack of end-to-end connectivity, at which point the nodes must form new routes based on the employed protocol. Better Approach To Mobile Ad-Hoc Networking (BATMAN) is a very commonly employed mesh routing protocol that handles routing within mesh networks. This paper evaluates the end-to-end reconnection establishment performance of the protocol utilizing a hardware testbed, and discusses potential enhancements to improve reconnection latency. We also open source the software developed to automate our operations and measurements done on the testbed, for public reference and usage.

5G Non-Public Networks (NPNs) for industrial networking focus in particular on reliable low-latency communication to enable new use cases. Traditionally, the network itself as well as the end devices are black-box, closed-source platforms. This prevents a fine-grained analysis of the influence of the different components, when evaluating the end-to-end One-Way Delay (OWD). Recently, open-source 5G Radio Access Network (RAN) implementations reached a certain level of maturity allowing a higher separation of the components. In this work, we perform a comprehensive delay analysis with a focus on the User Equipment (UE) induced impact. Our results show that devices containing the widely used Qualcomm X55 chipset introduce an additional delay of up to 6ms, compared to the successive Qualcomm X62. This puts a lot of previous 5G related measurement campaigns into a new perspective, as the ${X55 }$was the first chipset allowing the Stand Alone (SA) operation.

Feature-based Visual Simultaneous Localization and Mapping (V-SLAM) employs feature descriptors to recognize landmarks in successive frames. For networked (offloaded and/or collaborative) V-SLAM, the feature descriptors typically need to be transmitted over wireless communication networks with limited bitrates. Conventionally, V-SLAM systems have employed general computer vision feature descriptors whose size (in bytes) accounts for most of the data transmission. We develop a probabilistic model of the V-SLAM feature matching and identify three parameters that govern the probability of the correct feature match: the feature set size, the bit error probability between feature descriptors observing the same landmark, and the probability that the correct feature match exists in the feature set. Based on these three parameters, we formulate a research agenda for achieving high V-SLAM performance (i.e., high correct feature match probability) for reduced V-SLAM feature descriptor sizes (and thus reduced network throughput requirements). We address the first item on this research agenda by pursuing the reduction of the feature set size through a novel Orientation Restriction with Frame-to-Frame Projection (ORFFP). We evaluate ORFFP in comparison to the state-of-the-art ORB-SLAM2 approach using three representative V-SLAM test sequences. Our ORFFP approach reduces the size of the feature set to as little as 9.1% of ORB-SLAM2, allowing for the use of smaller feature descriptors. Thereby, ORFFP reduces the overall required throughput for networked V-SLAM down to roughly a third of the current approach, while achieving essentially the same V-SLAM performance. We also find that for learned descriptor-based models (HashSIFT, BAD), the feature descriptor size can be reduced using ORFFP without severely degrading the performance of V-SLAM. We outline future research directions towards comprehensively addressing the formulated research agenda for enabling networked V-SLAM on low-bitrate networks.

We investigate the feasibility of computing quantum gate-circuit emulation (QGCE) and quantum gate-circuit concatenation (QGCC) on digital hardware. QGCE serves the purpose of rewriting gate circuits comprised of gates from a varying input gate set to gate circuits formed of gates from a fixed target gate set. Analogously, QGCC serves the purpose of finding an approximation to the concatenation of two arbitrary elements of a varying list of input gate circuits in terms of another element from the same list. Problems of this kind occur regularly in quantum computing and are often assumed an easy task for the digital computers controlling the quantum hardware. Arguably, this belief is due to analogical reasoning: The classical Boolean equivalents of QGCE and QGCC are natively computable on digital hardware. In the present paper, we present two insights in this regard: Upon applying a rigorous theory of computability, QGCE and QGCC turn out to be uncomputable on digital hardware. The results remain valid when we restrict the set of feasible inputs for the relevant functions to one parameter families of fixed gate sets. Our results underline the possibility that several ideas from quantum-computing theory may require a rethinking to become feasible for practical implementation.

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN packet error handling using retransmission reduces packet loss but can increase transmission delay. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Previous research typically only simulates the integration of RLNC in 5G but does not demonstrate nor evaluate this integration in real 5G systems. In contrast, we implement and evaluate our approach through measurements with commercially available servers and switches, running the OpenAirInterface (OAI) 5G software stack. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols. Specifically, FlexNC provides a forwarder that flexibly interfaces with multiple RLNC protocols in a cloud-native (containerized) solution. Network operators can configure FlexNC based on network conditions and application requirements. For boosting network programmability, our Recoder in the Network (RecNet) leverages In-Network Computing (INC) in intermediate network nodes. We have developed an open-source cloud-native (Docker-based) implementation of both FlexNC and RecNet on OAI, including INC for the Recoder on a Cisco Catalyst switch. Measurements for video, haptic, and audio traffic indicate that i.) FlexNC adapts to various application needs in terms of latency and packet loss, and RecNet significantly reduces packet loss for a remote user with minimal increase in delay compared to pure RLNC. To the best of our knowledge, this is the first article to report testbed measurement results for cloud-native network coding in a 5G system, thus creating a baseline for reliable 5G communication.

The convergence of robotic arms, edge computing, and 5G non-public networks has opened new avenues for industrial automation. This paper presents a technically evolved demonstrator, based on the HYDRA concept, which embodies a novel approach to revolutionize industrial automation through collaborative and coordinated operations among multiple robotic arms. The HYDRA concept leverages the power of robotic arms working in close proximity, enabled by a two-layer architecture comprising a HYDRA core in a Multi-Access Edge Computing (MEC) environment and multiple HYDRA satellites connected via a non-public 5G network. This paper outlines the architecture, implementation, and impact of the HYDRA concept on industry.

The demonstrator described in this work shows wireless multi-path communication to enable Ultra Reliable Low Latency Communication (URLLC) for mobile industrial applications in demanding scenarios, that implement closed-loop control systems in a cloud-native manner. We show mobile vehicles that are coordinated precisely through an intersection, controlled by a central entity located in an edge cloud. In order to ensure seamless operation, the connection to the controller needs to be ultra-reliable and with low latency, otherwise the mobile vehicles may crash. To enable this we use a WiFi network as well as a Non Public Network (NPN) of the 5th Generation of cellular mobile communications (5G) network in parallel to increase robustness against delays and losses on each individual link. The audience can experience the benefits of multi-path communication by interacting with the demonstration.

This study demonstrates Functional Compression (FC) using a real-time control application. We deploy FC across the network using a ball-balancing robot with vision-based control. We exhibit seamless migration of visual computation between numerous edge cloud servers while the robot continues to balance the ball. The following demonstration showcases one of the earliest applications of FC while emphasizing the importance of beyond-Shannon technology.

Human-robot interaction is crucial in various industries and domains, such as manufacturing, healthcare, and entertainment. Natural and intuitive interactions between humans and robots are crucial. Legacy controllers were designed for two-dimensional visual display and, therefore, suboptimal for interaction in three-dimensional space. Hand gesture recognition with camera-based systems is often hindered by visual obstruction. We demonstrate a hand gesture system leveraging data gloves with inertial measurement units (IMU). This demonstration focuses on gesture recognition quality, enhancing robustness and responsiveness. Audiences can observe and directly participate using the data glove to maneuver a robotic dog remotely in real-time.

Visual Collaborative Simultaneous Localization and Mapping (SLAM) uses multiple mobile devices to capture images of their surroundings and map their environment collectively while simultaneously determining their position. In centralized approaches, a single server merges visual features from these devices to build a shared map. Offloading computationally intensive tasks benefits resource-limited mobile devices. However, due to unpredictable and heterogeneous communication links, maps at the server and on the devices can become asynchronous, potentially leading to real-time localization failures. To address these issues, we present Reliable Agents in Centralized Visual Collaborative SLAM (RAVIC), which employs an optimised server-to-agent communication strategy. This includes a novel keyframe selection method that exploits the server’s global map to improve tracking under high agent mobility and network latency. In addition, adjustments to agent-to-server communication and agent tracking algorithm reduce the computational demands on mobile devices, leading to a reduction in average tracking processing time of up to 27.2 ms. Our evaluation using benchmark datasets and different network conditions shows a drastic reduction in track loss probability from 61.4% to only 1.0% utilising merged map data in collaborative scenarios, demonstrating that collaboration increases the reliability of individual agents.

Communication systems are delicate to deliberate jamming, which may ruin legitimate communication. Two kinds of jammers exist: partial knowledge and full knowledge jammers. Jammers with partial knowledge only know the encoding and decoding functions, whereas jammers with full knowledge also know the actual message. This paper studies the detectability of Denial of Service (DoS) attacks by jammers with partial knowledge. The theoretical framework that characterizes the detectability of a DoS attack is expressed in the literature for Turing machines with no computing capacity scarcity. Even though there is no computational limitation for Turing machines, they cannot decide if a DoS attack is possible. Conversely, by following the theoretical framework, Turing machines can recognize an Arbitrarily Varying Channel (AVC) which a DoS attack is not possible. In these circumstances, we present an algorithm that detects the scenarios where DoS attack is impossible. We provide a complexity analysis and perform Monte Carlo simulations to investigate the performance of the proposed algorithm in terms of time consumption. We observe that the simulation results are compatible with the complexity analysis.

Physical-layer network codes for consensus-testing (CT) via a relay are proposed, where each of two parties knows a message and can find out if all messages are equal, e.g. as an integrity check in a decentralized storage system or the control of mobile autonomous robots. By assumption, the encoders cannot randomize. The proposed codes achieve the CT capacity for channels with a memoryless uplink multiple-access channel that is a binary adder channel or a pair of q-ary symmetric or erasure channels. There, the capacity of noiseless uplinks can always be achieved, by using generalized deterministic identification (ID) codes for the uplink, testing consensus at the relay, and broadcasting the one-bit result using zero rate. For pairs of Gaussian channels and Gaussian adder channels, the capacity bounds equal those known for ID over certain noiseless uplinks, where the code sizes scale superexponentially in the block length. Using a recent derandomization result for decoders, it is shown that for general channels, the ID capacity of certain noiseless uplinks upper-bounds the CT capacity. In contrast, both for transmission coding for the uplink and additive linear network codes, the asymptotically achievable code sizes and necessary block lengths are shown to be suboptimal.

Object recognition tasks are commonplace in industrial and medical applications, but resource-limited end devices common to the Internet of Things (IoT) are limited in capabilities and commonly require offloading of compute-intensive tasks. We evaluate the negative latency concept as a new approach to image detection and object recognition applications in low-bandwidth scenarios without a feedback channel. We employ image quality assessment and risk estimators associated with image quality degradation for progressively transmitted images to realize negative latency. Our comprehensive evaluation on image recognition and object detection tasks shows that image quality assessment over progressively transmitted images can be enabled by using risk estimators associated with image quality degradation during progressive transmission. In turn, our approach provides the framework for service latency and resource use reductions.

We consider the problem of computing gate-circuit approximations of quantum algorithms, i.e., unitary operators, from the perspective of computable (effective) analysis. The scientific community thinks the Solovay-Kitaev theorem a mile-stone in quantum compiling - the task of computing gate-circuit approximations - because it proves the existence of efficient quantum compilers in an analytic sense. However, since we cannot represent unitary operators in a mere analytical way on digital computers, contemporary digital implementations of quantum compiling resort to heuristic numerics and remain below the computational performance engineers hope to realize using the result of Solovay and Kitaev. This paper discusses quantum compiling within a framework of computable analysis, establishing a concept of computable unitary operators for digital computing based on the theory of Turing machines. Particularly, we prove that digital quantum compiling is uncomputable due to the underlying algebraic structure. Finally, we discuss several implications of our findings for heuristic digital implementations of quantum compiling, hinting toward possible research directions to thoroughly understand the relevant bottlenecks.

The Interplanetary File System (IPFS) is one of the largest peer-to-peer networks currently in use. In networks with heterogeneous peers, IPFS may take a long time to obtain all packets. Random Linear Network Coding (RLNC) can be added to IPFS to mitigate the effects of the suboptimal requesting strategy, as proposed by the Multi Access Recording System (MARS) protocol. We measure the power consumption, and the file processing and receiving delay for MARS in a test bed and find that MARS introduces significant delay overhead because it encodes its packets only after receiving a request for a file. As a modification of MARS, we propose Storing Coded Packets In-advance for IPFS (SPIFI) that addresses this issue by offloading the computation of the RLNC encoded packets to a point in time before the packets are requested. This way, the power consumption, and the delay incurred by the RLNC encoding does not deteriorate performance at run time. In additional measurements, we find that SPIFI reduces the energy consumption at run time and the data retrieval time compared to both MARS and IPFS. The encoding time can be selected freely, to perform the computation when power grids have spare capacity, avoiding peak loads in the grid. SPIFI works best for small files of a few MB and the benefit vanishes for larger file sizes due to the decoding complexity. If the client only requests single code packets to increase the file redundancy in the network, then SPIFI always outperforms IPFS and MARS.

In-Network Computing has brought computing and communication together at every communication node in the digital world, this work lays the foundations for doing the same in the quantum world, improving communication properties in the process - a combination of quantum computing and quantum communication. Full network softwarization, in-network intelligence, and massive connectivity will create an unprecedented demand for computing resources in the digital world. Accordingly, the scientific and industrial communities have begun to explore technologies such as quantum computing and have made significant efforts to demonstrate an algorithmic advantage for various problems. However, practical quantum computers are resource-inefficient and difficult to build. This article introduces a new communication paradigm leading to the concept of quantum in-network computing in the context of entanglement-assisted communication and computing for inherent distributed resilience and sensing. We review the fundamentals of digital hardware as characterized by Turing’s computability theory and demonstrate their relevance to mathematically rigorous characterizations of gate-based quantum computing. We then provide such a characterization using methods from effective analysis, leading to significant results that reveal the inherent theoretical limitations of universal gate-based quantum computers. These results support our assessment that gate-based quantum in-network computing is only possible through specialized, non-universal solutions that are seamlessly integrated with high-performance digital computing.

We investigate the feasibility of computing quantum gate-circuit emulation (QGCE)functions and quantum gate-circuit concatenation (QGCC) functions on digital hardware. QGCE functions serve the purpose of rewriting gate-circuits comprised of gates from a varying (possibly universal) input gate-set to gate-circuits comprised of gates from a fixed target gate set. Analogously, QGCC functions serve the purpose of finding an approximation to the concatenation of two arbitrary elements of a varying list of input gate circuits in terms of another element from the same list. Problems of this kind occur regularly in quantum computing and are often considered an easy task for the digital computers controlling the quantum hardware. However, recent results employing a rigorous mathematical theory of computability indicate that this may not be the case. This paper extends the aforementioned theory, providing two relevant insights: Upon applying a rigorous theory of computability, QGCE functions and QGCC functions turn out to be uncomputable on digital hardware. The results remain valid when we restrict the set of feasible inputs for these functions to one-parameter families of fixed gate sets, which is applicable even to standard one-qubit systems. Our insights underline the possibility that several ideas from the theory of quantum computing may require a rethinking in order to become feasible for practical implementation.

Identification is a new paradigm introduced for goal-oriented communication that verifies the semantics of the intended message rather than just transmitting the message bits, which is traditionally done in classical communication systems. One of the key techniques to improve randomized identification capacity is by introducing Common Randomness (CR). Physical Layer Secret Key (PLSK) is one such CR generation technique, where terminals generate secret keys based on changes in channel reciprocity between the wireless terminals and are mainly used for PHY layer encryption. However, the traditional way of evaluating PLSK techniques through bit discrepancies, bit generation rate, and entropy is not sufficient for evaluating their usefulness for identification. This paper proposes three key novel information theory paradigms that can be used alongside other mentioned parameters to evaluate key PLSK techniques for their usefulness in identification, i.e., CR rate, CR capacity, and cost function, followed by a supporting implementation using Software Defined Radio (SDR)s.

Edge clouds embrace Network Function Virtualization (NFV) to bring network functions (NFs) closer to end devices. The mobility nature of edge clouds and the emergence of new use cases, such as robot control or metaverse, demand NFs with high resilience. However, numerous NFs like firewalls require stateful processing that depends on historical processing values, referred to as NF states. Consequently, supporting stateful NFs with high resilience necessitates the maintenance of their states, often resulting in significant modification of the NFs. While providing high resilience for NFs, maintaining their processing performance is a critical challenge for existing solutions. Our proposed framework, StateProc, eliminates this concern by enhancing the processing capabilities of NFs through the utilization of NF states for custom processing. Through custom processing for state transfer, the evaluation results show that StateProc significantly improves the state transfer time, up to 93% faster than a notable state-of-the-art framework, without additional processing overhead.

Teleoperation has become mainstream in several applications, such as telesurgery, manufacturing, and construction. The human operator relies on video, audio, and haptic feedback data from the remote site to adjust operations, ensuring safety and collision avoidance. These applications require real-time perception and synchronization of modalities to maintain a high quality of experience, which presents a significant challenge. Human sensitivities are different among modalities, while communication networks rely on statistical multiplexing to maximize bandwidth utilization, often neglecting the specific latency requirements of each modality. We introduce SynCoDel to minimize asynchrony between video and haptic streams by considering event pairs, which are synchronized events. By prioritizing video packets within these pairs, we effectively utilize bandwidth resources initially reserved for high-priority haptic streams. Evaluation on a practical testbed with the programmable data plane and synthetic data shows that SynCoDel reduced asynchrony over half while maintaining low latency for haptic and video streams in congestion scenarios.

Emerging use cases, such as Tactile Internet, typically demand high reliability and low-latency communication. To fulfill these stringent requirements, 5G networks employ re-transmission within the Radio Access Network (RAN) in the event of packet loss, but at the expense of increased latency. An alternative approach to address this challenge involves leveraging Random Linear Network Coding (RLNC) to recover lost packets, thereby eliminating the necessity for retransmission within the RAN. We demonstrate the ability of in-network computing to run RLNC, particularly with the use of RLNC recoder, to enhance reliability and reduce latency within the network.

Network Functions (NFs) in edge clouds are required to provide scalability, fault tolerance, and mobility support. They all require maintaining NF states (i.e., processing results), e.g., for recovery, especially for stateful NFs like firewalls. Even though current solutions provide an alternative to storing states in memory, their design can support only a single requirement, either fault tolerance or scaling. Advocating the versatility, we propose StateOS - an operating system of NF states for user-defined programs supporting different requirements. Additionally, we propose a state transfer scheme, namely Divide-and-Conquer (DAC), to accelerate StateOS. The combination of DAC and StateOS demonstrates its efficiency for all three scenarios: scaling, fault tolerance, and service function chain acceleration.

Network Modularization, i.e., the refactoring of the functionalities of 5G Network Functions (NFs), will be an important tool to meet future requirements in the 6G System (6GS). In particular, via network modularization, it is possible to streamline the NF compositions and the inter-Nfinteractions. However, to achieve the highest possible efficiency without loss on inter-operability, we need a clear understanding of the different Key Performance Indicators (KPIs) and modularization techniques which are required to achieve the necessary end-to-end performance. In this work, we investigate the KPIs, the modularization methods prioritized by the research community, and the industrial implications thereof. We also outline the or-chestration requirements for the successful integration of modules to 6GS and the one possible integration of quantum technology to solve the synchronization challenge.

Time synchronization is crucial in the architecture of modern communication networks, supporting numerous high-stakes applications like financial transactions, autonomous vehicle control, and data center operations. While traditional time synchronization protocols, specifically the Network Time Protocol (NTP) and Precision Time Protocol (PTP), are reliable for various applications, they fall short in scenarios requiring ultra-high precision and resilience. To address these limitations, this paper provides a comprehensive comparative analysis of two emerging quantum technologies, namely Time-Correlated Entangled Photons (TCEP) and Optical Lattice Clocks (OLC). Using Monte Carlo simulations, we examined the synchronization in terms of the accuracy of these technologies under various noise conditions, revealing that while TCEP works perfectly in low-noise environments, its efficacy diminishes significantly with increasing noise levels. On the contrary, OLCs demonstrate consistent performance across various noise levels, making them more versatile for diverse application scenarios. This study is foundational for integrating quantum technologies in time synchronization for communication networks and sheds light on their merits and challenges. Our findings open new avenues for research in scalability, environmental resilience, and the development of hybrid quantum-classical timekeeping systems. Integrating quantum-enhanced time synchronization into a communication network will be the key step for achieving full-fledged Quantum Internet and quantum-enhanced communication networks.

Time-Sensitive Networking (TSN) is becoming increasingly important. Especially in the field of industrial applications, the demand for uniform, converged real-time networks is continuously increasing. Furthermore, the request to integrate wireless, mobile, and real-time capable network elements is getting more and more relevant to industrial automation use cases. To address these requests, the 3rd Generation Partnership Project (3GPP) has extended their specifications for mobile telecommunication protocols by descriptions to integrate 5G mobile networks into TSN starting from Release 16 onwards. While the specifications provide a good theoretical overview, there is still a lack of real implementations or even proof of concepts. Therefore, we started an implementation of a 5G network that is ready to be integrated into existing TSN. This work gives an overview of the current work in progress, mainly focusing on the implementation of the TSN Application Function (TSN AF) and the time synchronization features within the TSN Translators (DS-TT and NW-TT). It also shows current limitations and difficulties and how we have overcome them with our setup.

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

The generation of random numbers and the study of its properties have been an elusive field for a fair portion of the century. The application of random numbers is employed in many use cases such as cryptography, neural networks, numerical simulation, and gambling. The performance of each of these use cases is profoundly impacted by the employed random numbers; henceforth, the quality of randomness is of critical importance when it comes to their usage. A poorly generated random number can make a security system vulnerable, or any numerical or statistical evaluation misleading. Although various modes of classical random number generators exist and still function to provide strong cryptographic properties, emergence of quantum mechanical randomness has shed light on a novel path of generating certified randomness which supersedes the classical counterpart in terms of security. Harnessing quantum mechanical phenomena enables generation of true random numbers which can be certified and further implemented to elevate the net quality of the specific use cases. In this work, we generate and analyze random numbers from three different sources — 50: 50 beam splitter (BS), quantum key distribution (QKD) setup with classical post-processing scheme, and a commercially available quantum random number generator (QRNG) (ID Quantique (IDQ)). The quality of the generated random numbers from the various sources is checked in statistical tests and compared. Further on, we have developed a system which implements the QRNG-based random numbers to facilitate message authentication code (MAC) and one time password (OTP) protocols, demonstrating a communication network application. In this manner we discuss about a network which integrates quantum mechanics to the current classical networking approaches to enhance certain aspects of the networking protocol — in this case, the security.

This study focuses on the innovative field of quantum synchronization for satellite-based navigation systems including Global Navigation Satellite Systems (GNSSs) and the Non-Terrestrial Network (NTN) component of future 6G networks integrating both communication and navigation services. By combining a four-qubit system with the theoretical approach of the Lindblad master equation, we transcend the inherent limits of standard synchronization techniques. This achievement represents a quantum leap in satellite-based positioning, highlighting the scalability and cost-effectiveness of our technique for smaller satellites. The study demonstrates the possibility of reducing synchronization errors to less than one meter, significantly improving the reliability and precision of satellite-based navigation systems. The results of this study may contribute to the future development of both user-centric localization systems (typically GNSS systems) and network-centric localization systems (typically through the NTN component of 6G networks), leading to better positioning performance, more flexible multi-functional systems with the potential to limit both cost and size of satellites.

Emerging network application paradigms, such as the Tactile Internet, re-emphasize the need for different Quality of Service (QoS) levels. Due to the large packet buffers in the underlying network data plane, Active Queue Management (AQM) is generally required to curtail packet latencies for flows requiring high QoS levels. At the same time, programmable data planes, such as P4, enable packet processing at line-speed, albeit with limited packet processing functionalities. However, the existing AQM mechanisms that support QoS differentiation are too complex to readily run on P4, while the existing AQM mechanisms that run on P4 do generally not support effective QoS differentiation. We address this gap by developing to the best of our knowledge the first AQM mechanism that supports effective QoS differentiation while running on P4. Specifically, we propose SP-CoDel, which combines the well-known Controlled Delay (CoDel) AQM mechanism with Static Priority (SP) scheduling for QoS differentiation. Also, we propose RED-SP-CoDel, which adds a RED AQM component to SP-CoDel so as to make the AQM with priorities essentially parameterless. As a community resource contribution, we substantially extend the existing P4Simulator to the novel fused P4-NS3 Simulator so as to enable the evaluation of packet processing mechanisms through the combined functionalities of P4 device emulation and NS3 packet simulation. Evaluations conducted with the P4 reference switch model in the P4-NS3 Simulator indicate that SP-CoDel and RED-SP-CoDel provide high QoS to high-priority data streams, i.e., significantly reduce latency and packet loss compared to CoDel, while effectively mitigating bufferbloat.

In order to provide consistent low-latency communication network services, Time-Sensitive Networking (TSN) unites a set of standards for time-synchronization, flow control, enhanced reliability, and management. We design the TSN-FlexTest testbed with generic commodity hardware and open-source software components to enable flexible TSN measurements. We have conducted extensive measurements to validate the TSN-FlexTest testbed and to examine TSN characteristics. The measurements provide insights into the effects of TSN configurations, such as increasing the number of synchronization messages for the Precision Time Protocol, indicating that a measurement precision of 30 ns can be achieved. The TSN measurements included extensive evaluations of the Time-Aware Shaper (TAS) for sets of Tactile Internet (TI) packet traffic streams. The measurements elucidate the effects of different scheduling and shaping approaches, while revealing the need for pervasive network control that synchronizes the sending nodes with the network switches. We present the first measurements of distributed TAS with synchronized senders on a commodity hardware testbed, demonstrating the same Quality-of-Service as with dedicated wires for high-priority TI streams despite a 200% over-saturation cross traffic load. The testbed is provided as an open-source project to facilitate future TSN research.

Computing in the Network (COIN) has the potential to reduce the data traffic and thus the end-to-end latencies for data-rich services. Existing COIN studies have neglected the impact of the size of the data unit that the network nodes compute on. However, similar to the impact of the protocol data unit (packet) size in conventional store-and-forward packet-switching networks, the Computing Data Unit (CDU) size is an elementary parameter that strongly influences the COIN dynamics. We model the end-to-end service time consisting of the network transport delays (for data transmission and link propagation), the loading delays of the data into the computing units, and the computing delays in the network nodes. We derive the optimal CDU size that minimizes the end-to-end service time with gradient descent. We evaluate the impact of the CDU sizing on the amount of data transmitted over the network links and the end-to-end service time for computing the convolutional neural network (CNN) based Yoho and a Deep Neural Network (DNN) based Multi-Layer Perceptron (MLP). We distribute the Yoho and MLP neural modules over up to five network nodes. Our emulation evaluations indicate that COIN strongly reduces the amount of network traffic after the first few computing nodes. Also, the CDU size optimization has a strong impact on the end-to-end service time; whereby, CDU sizes that are too small or too large can double the service time. Our emulations validate that our gradient descent minimization correctly identifies the optimal CDU size.

As we become more and more dependent on communication technologies, resilience against any attacks on communication networks is important to guarantee the digital sovereignty of our society. New developments of communication networks approach the problem of resilience through in-network computing approaches for higher protocol layers, while the physical layer remains an open problem. This is particularly true for wireless communication systems which are inherently vulnerable to adversarial attacks due to the open nature of the wireless medium. In denial-of-service (DoS) attacks, an active adversary is able to completely disrupt the communication and it has been shown that Turing machines are incapable of detecting such attacks. As Turing machines provide the fundamental limits of digital information processing and therewith of digital twins, this implies that even the most powerful digital twins that preserve all information of the physical network error-free are not capable of detecting such attacks. This stimulates the question of how powerful the information processing hardware must be to enable the detection of DoS attacks. Therefore, in this paper the need of neuromorphic twins is advocated and by the use of Blum-Shub-Smale machines a first implementation that enables the detection of DoS attacks is shown. This result holds for both cases of with and without constraints on the input and jamming sequences of the adversary.

Cellular-connected Unmanned Aerial Vehicles (UAVs) require reliable wireless connections and will benefit from ultra-reliable and low-latency communication (URLLC) in 5G and beyond networks. 5G campus solutions, when integrating with Time-Sensitive Networking (TSN), can provide extended coverage and deterministic behavior for wireless links. However, one of the challenges for research in this direction is reliable yet cost-effective and flexible evaluation, which applies to academia and industry. We believe that open-source testbeds can effectively address the challenge due to their maturity and transparency in verifying evaluation results. Subsequently, we systematically survey and categorize testbeds in the literature that use commercial-off-the-shelf hardware. Our study outlines options for building a full-fledged testbed for integrating TSN with 5G and beyond for research and development. This study initiates one of the first steps toward facilitating future research toward practical deployment of TSN-5G integration.

Compressed sensing aims to retrieve sparse signals from very few samples. It relies on dedicated reconstruction algorithms and well-chosen measurement matrices. In combination with network coding, which operates traditionally over finite fields, it leverages the benefits of both techniques. However, compressed sensing has been primarily investigated over the real field. F2OMP is one of the few recovery algorithms to reconstruct signals over finite fields. However, its use in practical cases is limited since its performance depends mainly on binary matrices for signal recovery. This paper reports results of extensive simulations enhancing the features of well-performing measurement matrices for F2OMP as well as methods to build them. Moreover, a modified version of the algorithm, F2OMP-loop, is proposed. It offers a compromise between performance, stability, and processing time. This allows to design a joint compressed sensing and network coding framework over finite fields.

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

In a communication scenario with two transceivers (TRXs) and one relay, four time slots are required to communicate one packet in each direction if the relay uses a store-and-forward technique. With digital network coding (NC) three time slots are needed, and if analog netwok coding (ANC) is used, only two time slots are necessary. Communication systems with a low transmission rate such as molecular communication (MC) systems can especially benefit from the traffic reduction of ANC. The available literature only considers theoretical studies on the application of NC in MC nanonetworks. In this paper, we present for the first time a macroscale MC testbed for ANC to fill the gap between theory and practice. By using our testbed, we demonstrate the influence of time synchronization on the bit error ratio (BER) performance of the network coded MC system. We also study the influence of the bit sequence length on the error ratio of the system. Furthermore, we implemented duplex NC and demonstrated that the error ratios of half duplex and full duplex NC are close, but full duplex NC results in a time gain of up to$(n-1)$time slots for a bit sequence length n.

We consider the task of converting different digital descriptions of analog bandlimited signals and systems into each other. Albeit fundamental, the problem of finding the proper digital description of analog information is crucial to digital twinning. The latter is an emerging concept in the field of digital data processing that is regularly mentioned as key approach in the optimization of future communication technologies like 6G. We prove that quantities such as the peak-to-average power ratio and the bounded-input/bounded-output norm, which determine the behavior of the real-world analog system, cannot generally be determined from the system's digital twin, depending on which of the above-mentioned descriptions is chosen. As a main result, we introduce a new digital description of analog signals and systems and prove it to be algorithmically more powerful than the traditional description based on Shannon's sampling approach.

Visual Simultaneous Localization and Mapping (SLAM) systems have their numerous applications in robotics and autonomous driving. Computational offloading enables the computationally demanding Visual SLAM systems to run on hardware-constrained mobile devices, such as Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs). The offloading of SLAM modules to a central server also enables cooperative collaboration between multiple mobile devices. In this paper, we investigate the process of map synchronization between the edge and mobile devices in edge assisted SLAM systems. Due to the condition of uninterrupted execution, it is necessary that the map synchronization can be executed in the running process without losing the localization on the mobile device. Using a state-of-the-art edge assisted SLAM system, we investigate the influences of the movement speed as well as the network latency on the map synchronization and the resulting success ratio of the tracking continuation. By introducing a frame buffer on the mobile device, we have managed to compensate for the negative effect of the synchronization delay and thus increase reliability by up to 37%.

Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle- to-anything communication allows for the transmission of detailed vehicle states to the infrastructure that can be used for intelligent traffic light control. The other way around, the infrastructure can provide vehicles with advice on driving behavior, such as appropriate velocities, which can improve the efficacy of the traffic system. Several research works applied deep reinforcement learning to either traffic light control or vehicle speed advice. In this work, we propose a first attempt to jointly learn the control of both. We show this to improve the efficacy of traffic systems. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios. Analyzing the qualitative behavior of the vehicle speed advice policy, we observe that this is achieved by smoothing out the velocity profile of vehicles nearby a traffic light. Learning joint control of traffic signaling and speed advice in the real world could help to reduce congestion and mitigate the economical and environmental repercussions of today's traffic systems.

Human activity recognition is crucial for tactile internet, virtual reality, and digital-twin applications. Previous works have analyzed machine learning for this purpose but often need to pay more attention to typical challenges when deploying these machine learning models in production. First, data must be transmitted to the network node on which the machine-learning model is running. However, scaling human activity recognition by the number of devices and users puts additional constraints on the available transmission channel. While transmitting less data saves bandwidth, removing redundant information from the data can also benefit machine learning. This paper addresses the problem of transmitting wearable sensor data for human activity recognition. To this end, we analyze the extent to which wearable sensor data can be compressed via sparse coding without sacrificing loss in recognition performance. We empirically illustrate, on various datasets, that only a fraction of sensor information is relevant for human activity recognition.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Forward Error Correction (FEC) has become an integral part of communication technology to address expected losses during transfer of data. Among various layer three block codes, Random Linear Network Coding (RLNC) has emerged as an adaptable, powerful approach. However, its most straightforward implementation, Full Vector Coding (FVC), introduces too much delay for widespread adoption. Handcrafted schemes were introduced to optimise coding delay, while keeping resilience reasonably high. These works have resulted in tailRLNC and PACE. For the first time we analyse their respective behaviours in a fair and comparable manner, as non-recovered packets were statistically ignored previously. We then introduce an environment that uses a consistent, unbiased simulator and interface it with a Deep Reinforcement Learning (DRL) agent. This is the first time RLNC is joined with DRL. Our deep Q-network (DQN) based agent effectively uses an optimisation loop and utilises a customisable, expressive and extendable parametric loss function to learn a protocol. We demonstrate our agent recovers hand-tailored schemes and achieves state of the art.

New generations of mobile networks are opening up novel possibilities for controlling robots remotely in real-time. With 5G’s requirement to support use cases that demand low latencies at a high reliability from the communication network, wireless control applications become feasible. A remote operator typically uses a handheld device with buttons or joysticks to control a mobile robot. Joysticks are widely used today. The limitations of two-dimensional controlling and displaying of camera data can cause difficulties. Augmented Reality (AR)-based control provides the ability to control in three dimensional space. Therefore, new user-friendly Human Machine Interfaces (HMIs) can improve the interaction with these robots. In this demonstration, we present a human-in-the-loop application with a novel HMI. With our HMI a remote operator controls a four-legged Boston Dynamics Spot robot with gestures while wearing the AR-Headset Microsoft HoloLens 2. The remote operator receives feedback from the robot in the form of live camera streams visualised on holographic screens.

The trade-offs between storage and repair traffic for replacing failed storage nodes with new nodes (newcomers) in data centers with an omniscient controller are well understood. However, in edge storage settings, newcomers are not readily available, necessitating resilient data storage (caching) without newcomers. Edge storage nodes can often communicate via a broadcast wireless medium, which can be exploited to reduce the transmitted repair traffic via network coding. Repairs for resilient distributed caching without newcomers over a broadcast medium with Random Linear Network Coding (RLNC), which does not require an omniscient controller, have not been previously studied. We develop an information-theoretic model to characterize the theoretically achievable trade-offs between stored data and transmitted repair data in the RLNC broadcast setting without newcomers. Specifically, we formulate an Information Flow Graph (FG) model and identify all cuts in the resulting FG. We validate the theoretical FG model with simulations that demonstrate that the practically achievable trade-offs are close to the theoretical trade-offs.

Acoustic data from the Industrial Internet of Things (IIoT) are widely used in anomaly detection because audio information reflects richer internal statuses of monitored working machines than the video does. Since multiple acoustic data sources interfere with each other by nature, source data estimation is a prerequisite of subsequent anomaly detection. Existing schemes often use a centralized manner to separate full data on a remote node in clouds. However, such a centralized manner may delay reactions to anomalies due to data transmission delay and the complexity of solving data separation problems. This article shows that the data separation phase can be substantially accelerated with an in-network computing approach. The key idea is to offload data processing jobs to intermediate network nodes along the forwarding path. We first propose a distributed algorithm so that the data separation jobs can be done in a progressive manner; likewise, we modify the forwarding layer in order to eliminate hop-by-hop data transmission delay that hurts the performance of using in-network computing. We further derive theoretical upper and lower bounds of the required number of intermediate nodes that achieve the maximum acceleration. We also implement our proposed solution in a full-stack network emulator. Based on an open and professional data set, evaluation results justify the feasibility and advantages of our idea with nearly 32.18% acceleration on total processing time. This work exemplifies the convergence of IIoT, edge, and clouds.

Identification via channels (ID) is a goal-oriented (Post-Shannon) communications paradigm that verifies the matching of message (identity) pairs at source and sink. To date, ID research has focused on the upper bound$\lambda $for the probability of a false-positive (FP) identity match, mainly through ID tagging codes that represent the identities through ID codeword sets consisting of position-tag tuples. We broaden the ID research scope by introducing novel ID performance metrics: the expected FP-error probability$\overline {p_{\mathrm {fp}}}$which considers distance properties of ID codeword sets in conjunction with the probability for selecting ID pairs, the threshold probabilities$p_{\epsilon }$that characterize quantiles of FP-probabilities, and the distance tail uplift ratio DiTUR giving the fraction of ID pairs whose distance is increased above the minimum distance (which corresponds to$\lambda $). We define a No-Code (NC) approach that directly conducts the ID operations with the messages (identities) without any additional coding as a baseline for ID. We investigate a concatenated Reed-Solomon ID code and a Reed-Muller ID code, and find that they do not always yield advantages over using no ID code. We analytically characterize the reduction of error-prone ID pairs through sending multiple tags. Overall, our insights point to investigating the distance distribution of ID codes and to incorporating the ID pair distributions of real ID systems in future ID research.

Existing Network Function Virtualization (NFV) service placements that reuse already deployed network functions either reuse an entire Service Function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions. Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support. We formulate the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. We develop Tabu-SAP, a Tabu search approach to solve the SAP optimization problem. Furthermore, we introduce the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks; APMEC supports multiple MANOs through a loose coupling MANO-MEC design. Our Tabu-SAP evaluations indicate an around eightfold increase of the number of supported SFCs compared to the state-of-the-art reuse of individual network functions, while substantially reducing the total cost, which includes the chain configuration cost. Also, for long SFCs of seven or more network functions, the Tabu-SAP total cost is less than 10% higher than the optimal solution (which requires over ten times longer execution time).

In recent years, robots are taking on more and more tasks throughout many different application domains. Many of those robots work fully automatically and without human intervention. However, more complex tasks still require to be done by a human in remote control. One example would be robot remote surgery. Remote controlling a robot requires ultra- low latency on the communication, to allow fast and precise movements. To make this possible with today’s systems, the human operator must be in the same room. Enabling the user to operate from anywhere around the world, would bring a huge benefit as travels for highly trained experts can be minimized.In order to allow wider distances between the user and robot, we propose an AI-based prediction. Predicting the robots behavior can generate negative latency, which improves the precision of control. In large-scale communication networks, the main part of experienced latency comes from the propagation delay and is therefore not avoidable. Predicting upcoming data before it actually arrives, can create a zero-latency experience for the human operator. To prove this idea in the context of remote robot control, we propose JAVRIS. Using the CMIYC demonstrator, JAVRIS improved the score of an inexperienced user by over 1000%.

Recently private 5G networks so called 5G campus networks have gained a lot of public attention. Typically, such networks are associated to be implemented in huge production companies to control assembly lines. However, we present a demonstrator for the deployment of a private 5G network in a SME. Our demonstrator was designed in cooperation with a local bakery to relocate dough pieces in the middle of the night. This is an example of a monotones task, which alikes can be found in even the smallest companies. Such tasks can be better solved by robots, providing human workers more time for varied and creative tasks. We also point out some options how SMEs can get 5G campus networks at reasonable costs.

Simultaneous Localization and Mapping (SLAM), among other critical functions of mobile robots, such as navigation, are computationally expensive. When deployed at the robot, those functions demand high energy consumption and result in shorter operation time. Offloading SLAM to an Edge Cloud (EC) can significantly reduce the robot’s computing demand and resources, subsequently reducing energy consumption. We offload intelligence of mobile robot control functionality, i.e., navigation, localization, and control to an EC. The EC processes sensor data and sends the robot the directional velocities. Meanwhile, a 5G wireless connection ensures the necessary low latencies and high throughputs. We demonstrate the feasibility of offloading SLAM and navigation in an EC based on a use case in automotive production. Additionally, we developed a digital twin of the robot and visualized its current sensor data.

The increasingly difficult challenges in managing big data are inciting for revolutionary and fundamental techniques, as well as for technologies that can handle the complexity and the never-before-seen volume of data. Our goal is to enable high-density industrial networks to transmit and store production-related information efficiently and confidentially while retaining any existing network infrastructure. As the deployment of sensors leads to both the benefits and the challenges of industrial big data, our Bitteiler solution represents a means to reduce the data volume at the early stages of the data cycle to curb its impact on communications and storage systems. Bitteiler is a plug-and-play solution that allows customers to leverage smart IoT solutions to the fullest to achieve an industry 4.0-ready production process that is both efficient and secure. Our practical demonstration provides a hands-on experience with the Bitteiler technology – including in-network compression and coding – using a number of live IoT devices that can be manipulated by the audience.

The future Tactile Internet with Human in the Loop (TaHiL) [1] enables a perceived real-time interaction between a human and a remote physical or virtual object. Human intention needs to be inferred from data captured by sensors throughout the body and even the brain. Therefore, the prediction can happen already in a machine attached to the human body (or so-called a Body Computing Hub, BCH). This human-machine coaugmentation requires Tactile electronics [2] with extreme requirements, such as ultra-small, stretchable, and ultra-low-energy consumption, allowing for sensing at extremely low latency. However, for a human to interact with a real or virtual object across the globe, tactile electronics require tight integration with softwarised networks [3] , wireline or wireless, with extremely low latency. Such networks can also bring the computing capability to human’s proximity, such as a network edge, by leveraging Network Function Virtualization (NFV) and Software-Defined Networking (SDN). Even though critical, the integration between tactile electronics in a body area network (BAN) and external softwarised networks is uncovered in the literature. This paper explores vital connections between tactile electronics and fully softwarised networks, focusing on adapting all layers from electronics to network and application.

This demonstration focuses on in-network computing as an enabler for low-latency Industrial Internet of Things (IIoT) applications, such as audio source separation for anomaly detection. By demonstrating a specific industrial application, we show that our method Progressive ICA (pICA), improves accuracy and reduces overall service latency progressively. The idea is to parallelize data transmission and processing along a multi-hop path consisting of in-network computing nodes. The audience can experience the benefits of the novel concept of in-network computing by interacting with the demonstration remotely via the Internet or in person.

Edge Simultaneous Localization and Mapping (SLAM) retains only the tracking on the mobile device, while offloading the compute-intensive local mapping and loop close to edge computing. Existing Edge SLAM approaches incur relatively high delays for offloading, resulting in high failure probabilities, i.e., low reliability, for commonly used public SLAM datasets. We discovered that two parameters which had not previously been studied in detail, namely the number of features and the number of keyframes that are bundled for a local map update, play a critical role in the offloading delay. Also, previous approaches updated the local map in the mobile device in a serial manner, incurring map update latencies. We study the numbers of features and bundled keyframes in detail and we parallelize the local map update. We find that judicious parameter settings, namely relatively small numbers of features (750 per frame) and bundled keyframes (1, i.e., effectively no bundling), reduce the map update latency to less than half compared to the previously common settings (1000 features per frame and 6 keyframes used for a map update). For a low network latency of 20ms, these judicious parameter settings in conjunction with our parallelized local map updating, reduce the 79% failure rate of the previous Edge SLAM systems down to 2%.

Applying renewable energy in a smart micro grid (MG) is increasingly receiving attention to reduce greenhouse gas emissions. However, the mismatch between supply and demand hinders the realization of this process. With the widespread use of plug-in electric vehicles (EVs) and the development of emerging mobile edge cloud (MEC), intelligent energy optimization becomes a way to address the challenge. Therefore, in this paper, we propose a novel two-stage approach based on deep learning (DL) to reduce overall energy cost for sharing EVs integrated MG by forecasting its state and optimizing EVs scheduling. Our simulation results show that the joint design of forecasting and optimization reduces the overall energy consumption and the payment to the external grid.

Mobile robots are an essential part of the vision of flexible production in a smart factory. To monitor and connect the robots, reliable and low latency communication is necessary. In this work, we conduct packet-based active measurements to evaluate the performance of a state-of-the-art 5G standalone system in a production environment. The focus is on whether 5G connections can meet the requirements specified by 3G PP in terms of delay and reliability. The results indicate that without cross-traffic, the requirement of a delay of less than 10 ms for 99.9 % of the packets can be met for the remote control and fleet management of mobile robots. However, as soon as cross-traffic is injected, especially in the uplink, the upper percentiles of the delay increase significantly, thus failing to hold the reliability requirements.

Deterministic communications are required for industrial environments, yet their realization is a challenging task. Time-Sensitive Networking (TSN) is intended to enable deterministic communication over inexpensive Ethernet networks. Standardized by the IEEE TSN working group, TSN enables precise control of time synchronization, traffic shaping, reliability enhancements, and network administration to answer the demands of industrial control applications. Subsequently, there is a significant need to enable turnkey research and implementation efforts. However, a current lack of open-sourced testbed implementations to investigate and study the behavior of TSN network devices limits verification to simulation and theoretical models. We introduce a publicly available, flexible, and open-sourced measurement testbed for evaluating TSN in the context of industrial automation applications to address the need to perform real-world measurements. In this contribution, we describe our testbed combining Commercial-Off-The-Shelf (COTS) hardware and existing open-source tools as a platform for in-depth evaluation of TSN devices. Providing detailed TSN backgrounds, we describe an in-depth performance analysis for our implementation. For a common Tactile Internet scenario, we observe an accuracy of close to 5 ns achievable with our publicly available COTS setup.

Digital Twin (DT) implementations can reduce latency drastically in future communication systems for steering and control of cyber-physical systems (CPS). When the CPS and its DT periodically exchange information to repair possible desynchronisations between the two systems, this can lead to large data traffic flooding the networks. This work proposes a goal-oriented communication approach based on message identification (ID), which only sends repair data when necessary, but adds control traffic and a probability of not repairing some desynchronisations between the two systems. We compare optimal ID codes and a hash function for detection whether desynchronisation correction is necessary. Our method can reduce the traffic to within 0.3% of its optimal value while repairing 99.97% of all desynchronisations for 4 kbit of data and grows even better for larger data.

5G campus networks, whose advantages include flexible deployment, can be a promising candidate for production plants to complement existing Wifi-based networks. Toward that goal, 5G has to satisfy strict requirements about real-time communication to facilitate novel use cases. However, the realtime-capability of 5G is not well understood yet. In this work, we deliver insights into the functioning of 5G NR RAN Release 15, which includes actual one-way delay and Round-Trip Time (RTT) measurements for Downlink and Uplink in a private 5G Standalone campus network. The extensive measurement results reveal that these delays are correlated, and the corresponding RTT, i.e. the sum of Downlink and Uplink delays, is discreetly clustered, ranging between 12ms and 40ms. The measurements also show that the distribution of RTTs is mainly dependent on the packet rates and their inter-arrival times. Our study helps expand the current understanding of 5G used for latency-critical applications. We make the code and the measurement data traces publicly available as the IEEE DataPort 5G Campus Networks: Measurement Traces dataset (DOI 10.21227/xe3c-e968).

Delivering content from a network via a client-server architecture is expensive not only for content owners but also for network operators. Moving content closer to the end user is already used in Content Delivery Networks (CDN). Multi-Access Edge Computing (MEC) enables us to shift the content even closer by using the storage of end users. But, due to the large media files, storage and transport costs for peers increase significantly. Network Coding can reduce these costs. However, peers in CDNs tend to be highly fluctuating and often need to be restored, making continuous availability of data at the network edge a problem. While for uncoded data, individual packets lost due to peer failures can be tracked to determine availability, the availability of coded data is currently distinguished only in two cases: either there are still enough linearly independent packets to decode the file, or there are not. However, we have found that the network’s combined coded cache loses quality over time due to recovery. This quality loss, which we refer to as grade, can be measured by very cost-effective monitoring. If the grade falls below a certain limit, we can intervene in the network by performing a cache refresh to prevent data becoming unavailable preemptively. In this paper, we present the cases in which such monitoring is useful, how the grade is calculated, and when a cache refresh is necessary. The results show that we can reduce network traffic by up to 34% with minimal storage costs through efficient monitoring.

Data privacy concerns are increasing significantly in the context of the Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next-generation networks. Homomorphic encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This article comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. This article delves into the mathematical foundations required to understand fully homomorphic encryption ($\textsf {FHE}$). It consequently covers design fundamentals and security properties of$\textsf {FHE}$and describes the main$\textsf {FHE}$schemes based on various mathematical problems. On a more practical level, this article presents a view on privacy-preserving machine learning using homomorphic encryption and then surveys$\textsf {FHE}$at length from an engineering angle, covering the potential application of$\textsf {FHE}$in fog computing and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art$\textsf {FHE}$libraries and tools, implemented in software and hardware, and the performance thereof.

Emerging microservices demand flexible low-latency processing of network functions in virtualized environments, e.g., as containerized network functions (CNFs). While ensuring highly responsive low-latency CNF processing, the computing environments should conserve energy to reduce costs. In this systems integration study, we develop and evaluate the novel XDP-Monitoring Energy-Adaptive Network Functions (X-MAN) framework for managing the CPU operational states (P-states) so as to reduce the power consumption while prioritizing low-latency service. Architecturally, X-MAN consists of lightweight traffic monitors that are attached to the virtual network interfaces in the kernel space for per-CNF traffic monitoring and a power manager in user space with a global view of the CNFs on a CPU core. Algorithmically, X-MAN monitors the CPU core utilization via hybrid simple and weighted moving average prediction fed by the traffic monitors and a power management based on step-based CPU core frequency (P-state) adjustments. We evaluate X-MAN through extensive measurements in a real physical testbed operating at up to 10 Gbps. We find that X-MAN incurs significantly shorter and more consistent monitoring latencies for the CPU utilization than a state-of-the-art CPU hardware counter approach. Also, X-MAN achieves more responsive CPU core frequency adjustments and more pronounced reductions of the CPU power consumption than a state-of-the-art code instrumentation approach. We make the X-MAN source code publicly available.

Live migration is a technology that seamlessly relocates a virtualized service between physical hosts, which allows services to rapidly adapt to environmental changes. Despite the large amount of research, there is still a lack of understanding of its performance. Towards a better understanding of live migration, we build a testbed and use it to migrate a computation-intensive application with docker and KVM. We evaluate the service downtime, migration time, and network usage under different conditions. The results show that KVM outperforms docker in most of the scenarios, with some critical exceptions where only docker manages to perform miaration.

This demonstrator enables remote audiences to control robots within our lab. The purpose is to play a little game in order to show remote control over the public internet. Users are able to interact with the demo remotely through the web interface. This demonstrates an straight forward approach for remote robot operation independent from a specific device. Any current web browser running on a commercial off-the-shelf computer or mobile phone can be used to interact with the demo. However, it also shows the limitations of this kind of robot control. Dependent on the user's location and network connection, different amounts of latency come into play. Within this demo this effects the difficulty, as only fast or precise movements are possible.

Currently, most IoT services are hosted centralized in a cloud. However, they rely on data gathered by numerous distributed sensors, which require to be connected to these clouds. At the moment this is realized with non-IP based proprietary wireless sensor networks that use inflexible networking concepts and require to be carefully configured. The Thread protocol, with its implementation OpenThread, is a new wireless mesh approach to solve this problem. OpenThread is maintained by the Thread group, which is an organization supported by almost 300 companies, including Google, Amazon, and Apple. Its design is based on an IPv6, for seamless integration in existing IP networks. This work, after a short a practical overview of the Thread protocol in general, investigates the network performance in different interference scenarios based on results from a hardware testbed, set up in an office building. This testbed is based on the Nordic Semiconductor nRF52840 System on a Chip (SoC), running OpenThread from the official github repository. Besides a thorough examination for round-trip-time (RTT) and losses, a stability test was performed over five weeks, to see the impact of interference during business hours. We measured RTTs in the range of 10 to 100 ms and losses up to 40 % depending on interfering wireless services. Despite its obvious advantages, from our experimental results, it is evident how some practical limitations make Open Thread not always a good choice.

Industry 4.0 is now much more than just a buzzword. However, with the advancement of automation through digitization and softwarization of dedicated hardware, applications are also becoming more susceptible to random hardware errors in the calculation. This cyber-physical demonstrator uses a robotic application to show the effects that even single bit flips can have in the real world due to hardware errors. Using the graphical user interface including the human machine interface, the audience can generate hardware errors in the form of bit flips and see their effects live on the robot. In this paper we will be showing a new technology, the SIListra Safety Transformer (SST), that makes it possible to detect those kind of random hardware errors, which can subsequently make safety-critical applications more reliable.

Future networks relying on models in their applications, e.g., control loops in the Tactile Internet, will exhibit a need to have model parameters available before interactions take place. This anticipatory delivery of model weights effectively results in required negative latencies for the overall services. We provide an exemplary motivation based on advertising to mobile users that is optimized by user-modeled preferences. We employ this example for our discussion of anticipatory networking in this paper, with an overall focus on the overarching concepts.

The Simultaneous Localization and Mapping (SLAM) method is becoming more and more established for the localization of mobile robots in indoor environments. Due to the high complexity of SLAM, high computing resources are necessary, which leads to a shorter runtime. By using edge computing and higher bandwidths of new wireless technologies, the computing can be outsourced. In this work, the SLAM process is offloaded from a mobile robot to an edge cloud and the impact of more computing power is investigated. We show that outsourcing has performance advantages in terms of the update rate of the map generation as well as the localization.

URLLC is the key feature of 5G enabling extensive V2X applications and control of complex industrial processes via wireless communication systems. In order to ensure the highest reliability requirements with low latency communication, the fluctuating characteristics of a wireless link must not become a limiting factor for the use case. Cooperative communication approaches eliminate this problem by exploiting additional links via relay nodes to transmit data redundantly on different paths in the network to the destination node. We present a suitable multi-path scheme for integration in 5G and B5G systems. The theoretical analysis shows nearly one order of magnitude improvement of reliability for each additional path with the proposed architecture. Furthermore, we demonstrate a significant gain in terms of reliability for the overall link even for highly disrupted relay paths. Using experimental vehicle platooning measurements in real-world traffic based on LTE and IEEE802.11p, we confirm the conceptual advantage of our multi-path approach.

An autonomic network management system (ANMS) is expected to play a significant role in fifth and sixth-generation (5G and 6G) networks. It enables the network to manage itself with minimum or no human intervention. Recently, an ANMS architecture called multi-agent-based network automation of the network management system (MANA-NMS) architecture was presented. The article discussed a multi-agent service decomposition architecture, defining atomic network-functions (ANFs). These ANFs are proposed to be intelligent and autonomous agents. The agents are designed as independent atomic decision elements incorporating machine learning (ML) as an internal cognitive component. The atomic units are used as a building block for an ANMS. In line with this approach, this article proposes a network traffic classifier agent (NTCA) as a part of the network traffic management system. We first design and implement a NTCA using an ML algorithm as a cognitive component of the agent. To compare, we used K-Nearest Neighbors (K-NN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes in the agent design. We perform an evaluation using classification accuracy, training latency, and classification latency. Finally, we tested the performance of the NTCA by implementing it in the MANA-NMS conceptual framework. The results show that the Decision Tree NTCA has the highest mean classification accuracy, the least mean training latency, and the lowest mean classification latency.

Modern manufacturing is now deeply integrating new technologies such as 5G, Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a new level – Smart Factory. Autonomic anomaly detection (e.g., malfunctioning machines and hazard situations) in a factory hall is on the list and expects to be realized with massive IoT sensor deployments. In this paper, we consider acoustic data-based anomaly detection, which is widely used in factories because sound information reflects richer internal states while videos cannot; besides, the capital investment of an audio system is more economically friendly. However, a unique challenge of using audio data is that sounds are mixed when collecting thus source data separation is inevitable. A traditional way transfers audio data all to a centralized point for separation. Nevertheless, such a centralized manner (i.e., data transferring and then analyzing) may delay prompt reactions to critical anomalies. We demonstrate that this job can be transformed into an in-network processing scheme and thus further accelerated. Specifically, we propose a progressive processing scheme where data separation jobs are distributed as microservices on intermediate nodes in parallel with data forwarding. Therefore, collected audio data can be separated 43.75% faster with even less total computing resources. This solution is comprehensively evaluated with numerical simulations, compared with benchmark solutions, and results justify its advantages.

Internet of Things (IoT) network is dominating both the research and industry. There are numerous emerging IoT connectivity Technologies such as Sigfox, LoRa, NB-IoT, LTEM. However, these IoT connectivity technologies have different protocols and packet/message formatting. Thus, IoT devices are usually not able to interact with one another, causing interoper-ability challenges. This is creating the so-called network island or silos. Interoperability between different IoT networks needs to be achieved to fully exploit IoT potential. This is required at each level of the network. Different solutions have been proposed to tackle the interoperability problem at different levels reducing the difficulty in defining a solution breaching the vertical silos barrier. In this article, we focus on addressing network-level interoperability. We provide a network format translator in a virtualized environment as a flexible and lightweight deployment. As a proof of concept, a testbed is developed implementing the proposed translator using NS3. Using the testbed, we can communicate with different IoT technologies sending packets between each device in each type of IoT network. For example, sending a LoRaWAN packet to Wi-Fi and 6LoWPAN and visa-versa. Finally, we have measured the latency introduced by the translator.

In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today’s vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.

Vehicle Platooning combines lower fuel consumption with safe and efficient transportation in a cooperative driving application. To ensure this, vehicles must communicate quickly and reliably. The sliding window protocol Caterpillar Random Linear Network Coding (CRLNC) is used as it does not rely on acknowledgements and uses network coding to reduce losses. However, the protocol was intentionally designed for single hop scenarios. Therefore, three contributions to the CRLNC protocol are presented in this paper to optimize it for multi hop/single path transmissions. The contributions are idle-slot-management, adaptive-window, and queue-management. By effectively combining these contributions, a large gain in throughput of up to 40% is achieved. Furthermore, it was investigated what are the optimal protocol parameters for CRLNC in single path scenarios are. It is shown that the best transmission in terms of throughput and delay is achieved, when the code rate is less than one minus the error probability and a large window size is used. Even if the error probability is unknown, a large window size will bring more benefits to the transmission.

Random Linear Network Coding (RLNC) has been proven to improve the performances of Internet of Things (IoT) devices, such as throughput and resilience. However, for latency-sensitive applications, sliding window RLNC (SWNC) outperforms the block-based RLNC because it allows instantaneous packet decodeability and reduces the in-order delay. However, most conventional SWNC approaches have been applied to single-hop communication networks since recoding at intermediate nodes destroy the properties of the coding windows. In this work, we consider multi-hop communication networks and propose SWNC-based recoding algorithms that can be applied at inter-mediate nodes. The simulation results show that the proposed recoding algorithms outperform block-based RLNC by 53% in terms of the per-packet delay performance.

In wireless multi-hop and multi-path computer networks, such as the ones that potentially emerge with Internet of Things (IoT) deployments, channel coding has become a tried and tested solution to handle packet losses. Random Linear Network Coding (RLNC) is one of such block coding techniques, which also provides the unique feature of recoding already coded symbols on intermediate nodes, thereby increasing the general throughput of the network. However, the computation overhead of decoding on the receiver device counteracts the much desired low-latency requirements of various fifth generation (5G) use cases. Sliding-window RLNC is a technique, which can minimize such overheads. Nonetheless, recoding over a sliding window destroys the low-latency decodable properties of the scheme. In this paper we present a low-latency recoding technique that balances packet forwarding and recoding on the intermediate nodes in way that potentially preserves the sliding window characteristics. Our simulation results show that the performance of the proposed recoding algorithm achieves a two factor improvement in latency and losses, as well as a more than three times gain in throughput.

Future communication systems will require low latencies, especially for the small packets used in control applications. In WiFi systems, the main contributors to delays are retransmissions and congestions. Many of the transmitted packets are received with errors at the destination, and they are dropped at the MAC layer. Even if the number of errors is small the packets are discarded. Dropped partial packets are retransmitted, increasing the delays of the system because retransmissions involve new access to a shared medium. We show in our measurement campaign of IEEE 802.11 networks that between 5% and over 50% of the received packets, depending on the Modulation and Coding Scheme (MCS), have errors. However, only a few bits are erroneous (between 2% and 10%). Therefore, we show that a partial packet recovery technique based on network coding can make use of 55% more packets that otherwise would be dropped, which reduces the number of retransmissions and delays. Furthermore, if the length of the packets is short (between 50 and 250 Bytes) the computational overhead is low, which makes the implementation feasible, in such networks, for the short packets typical of applications of control of cyber-physical systems.

The introduction of the Internet of Things (IoT) and massive machine-type communications has implied an increase in network size and complexity. In particular, there is already a huge number of IoT devices in the market in various sectors, such as smart agriculture, smart city, smart home, smart transportation, etc. The IoT interconnectivity technologies are also increasing. Therefore, these are increasingly overwhelming the efforts of network administrators as they try to design, reconfigure and manage such networks. Relying on humans to manage such complex and dynamic networks is becoming unsustainable. Network automation promises to reduce the cost of administration and maintenance of network infrastructure, by offering networks the capability to manage themselves. Network automation is the ability of the network to manage itself. Various standardization organizations are taking the initiative in introducing network automation, such as European Telecommunication Standardization Institute (ETSI). ETSI is leading the standardization activities for network automation. It has provided different versions of reference architecture called generic autonomic network architecture (GANA), which describes a four-level abstraction for network-management decision elements (DEs), protocol level, function level, node level, and network level. In this article, we review and survey the existing works before and after the introduction of software-defined networking (SDN) and network-function-virtualization (NFV). We relate the main trending paradigms being followed, such as SDN, NFV, machine learning (ML), microservices, multiagent system (MAS), containerization, and cloudification, as a pivotal enabler of full network automation. We also discuss the autonomic architectures proposed in the literature. Finally, we presented possible future research directions and challenges that need to be tackled to progress in achieving full network automation.

The advent of network softwarization is enabling multiple innovative solutions through software-defined networking (SDN) and network function virtualization (NFV). Specifically, network softwarization paves the way for autonomic and intelligent networking, which has gained popularity in the research community. Along with the arrival of 5G and beyond, which interconnects billions of devices, the complexity of network management is significantly increasing both investments and operational costs. Autonomic networking is the creation of self-organizing, self-managing, and self-protecting networks, to afford the network management complexes and heterogeneous networks. To achieve full network automation, various aspects of networking need to be addressed. So, this article proposes a novel architecture for the multi-agent-based network automation of the network management system (MANA-NMS). The architecture rely on network function atomization, which defines atomic decision-making units. Such units could represent virtual network functions. These atomic units are autonomous and adaptive. First, the article presents a theoretical discussion of the challenges arisen by automating the decision-making process. Next, the proposed multi-agent system is presented along with its mathematical modeling. Finally, MANA-NMS architecture is mathematically evaluated from functionality, reliability, latency, and resource consumption performance perspectives.

Here we show how, based on 5G-enabled technologies, a connected power distribution grid can be split into self sufficient islands integrating self driving electric vehicles (EVs). End users form self sufficient (from the energy demand point of view) clusters called virtual power plants (VPPs), and EVs serve as energy delivery devices between different VPPs. Moreover, we show how phase-to-ground faults can be detected and isolated even in the presence of distributed energy generation, thanks to low latency communication and massive IoT.

Live service migration with low service downtime is one of the main challenges in mobile edge cloud (MEC). In other words, it is important to guarantee that users continue to receive good service even if they cross through multiple MEC servers. In this paper, we propose a seamless service migration framework and apply it to an autonomous driving demonstration. We expect this paper to provide a perspective on how MEC can be used for practical autonomous driving in the near future.

The emergence of 5G services includes those requiring extremely low-latency for command and control application scenarios in the low millisecond range. With a significant human-machine interaction component, the Tactile Internet will require that the experiences based on the human visual interface can be dynamically adjusted within similar time frames. In this paper, we evaluate the impact that different delays currently attainable with commercial hardware would have on predicting the Quality of Experience (QoE) with immersive images. Specifically, we employ electroencephalography (EEG) data to predict how future subjects would determine the media quality in a Passive Human In-the-Loop (PHIL) scenario. This initial extension of our prior work focuses specifically on the delay in the gathering and processing of data and presents a first foray of bringing the passive human-in-the-loop QoE adjustment approach to the Tactile Internet. We find that there is limited value in increasing the delays of two different approaches to predicting the QoE. We additionally note that current approaches to predicting a user's QoE based on other users' EEG patterns exhibit only limited prediction accuracy.

The ever-growing hunger for high quality, up to date, and efficient datasets to be used in data-driven models and control systems lead to the diffusion of a number of database management systems specifically tailored to support operations on timeseries: Timeseries Databases. We focus on the systematic performance assessment of some of the most popular TSDBs available. To this end, we developed a tool specifically designed for the task at hand: SimpleMetric. By means of SimpleMetric, we measure the performance of the selected TSDBs in terms of the computational overhead introduced by a number of aggregation functions in a closed loop control scenario (i.e., monitoring and actuation).

Time-sensitive applications on wireless networks often demand high reliability and high throughput. Random Linear Network Coding (RLNC) is a forward error correction (FEC) method for wireless lossy channels that organizes the information in blocks, commonly called generations. Hybrid Automatic Repeat reQuest (HARQ) is a combination of a FEC method, such as RLNC, and an ARQ often used to ensure reliable data transmission. Many RLNC-based protocols provide in-order delivery, which helps in general cases but backfires in high-jitter channels. Multigeneration RLNC protocols are more resilient in high-jitter channels than conventional RLNC protocols because they are capable of handling multiple generations at the same time. However, they still struggle against losses. Despite the considerable amount of research done in this field, there is still no solution that combines HARQ with multigeneration RLNC protocols. Our contribution in this paper is two-fold: (i) we propose a new ARQ mechanism adapted for multigeneration RLNC protocols, and (ii) we compare its performance in terms of per-packet delay and throughput to conventional RLNC, HARQ RLNC, and multigeneration RLNC. The results show an increase in the decoding probability from 25% without ARQ to 80% with ARQ. Moreover, we observe a decrease of 35% in the average packet delay.

It is vital for Tactile Internet to constantly maintain a low-latency control loop between sensors, actuators, and their controlling software applications. Mobile Edge Computing (MEC) is an essential technology that brings the elasticity of cloud computing to run controlling applications at a close proximity to controlled objects, thus reducing latency. To support the mobility of the objects, the underlying network has to be capable of migrating MEC applications seamlessly to guarantee the close proximity. However, it is challenging to migrate application states quickly and flexibly without interrupting the control loop. We propose FAST, a flexible scheme for direct and low-latency state transfer leveraging Software-Defined Networking (SDN). Evaluation results show that compared to state of the art, FAST reduces the service migration time by 80%.

Coding techniques have improved the performance of distributed cloud systems (DCS). On one hand, they increased the reliability while trading off storage and bandwidth costs, and on the other hand, they provide high download speeds of data without centralized protocols. The higher data rates and lower delays are achieved by increasing the redundancy stored in clouds with higher bandwidth, allowing them to send more information before running dry. However, storing much information in a small set of clouds can violate confidentiality of the data in the presence of an attacker that breaks into these clouds. In this paper, we propose a technique that uses light-weight secure network coding schemes to increase the download rates of DCS. We compare the secure network coding techniques in terms of delay and codecs throughput, and we show that in multi-cloud systems, it is possible to obtain gains of 379% over DCS with equally shared redundancy and 192% over SotA techniques that aim to maximize the data rates by distributing more redundancy to the clouds with highest bandwidth.

While connectivity is available almost anytime and anywhere on ground, aircraft during flight still lack high-throughput communication. We investigate air-to-ground networks consisting of direct air-to-ground, air-to-air and satellite links for providing high throughput to aircraft. We formulate an optimization problem to maximize the minimum throughput of all aircraft. We solve the problem using realistic aircraft and base station positions and also model physical limitations such as maximum number of antennas per aircraft and interference. We investigate different scenarios and parameters and analyze the influence of the parameters on the max-min throughput per aircraft. We show that the satellite and direct air-to-ground links are the bottleneck, as all throughput can be distributed among aircraft. Furthermore we show that air-to-air communication is dispensable for achieving a high throughput when having direct air-to-ground coverage.

Computation offloading is one of the main use-cases of the Multi-access Edge Computing (MEC) paradigm which can help to save the battery life of the resource-poor mobile devices by transferring the computation-intensive tasks to the resource-rich edge cloud servers. However, the ever-increasing internet traffic can negate this benefit due to possible failures of the MEC servers. On the other hand, the growth of computation capabilities of end devices as the result of recent developments of Central Processing Units (CPUs), can help to enhance the MEC systems performance and broaden the concept of edge computing by using device to device communication (D2D). The majority of existing works on computation offloading assume the tasks are independent and can be executed in parallel; however, the dependency among tasks can introduce new problems. To investigate this issue, in this paper, we consider a basic three-node MEC system consists of a user node with sequentially-dependent tasks, a helper/relay node, and a MEC server located at the base station (BS). We formulate the offloading problem into an energy-efficiency minimization problem while satisfying the task-dependency and completion deadline requirements. The simulation results show the superior performance of our proposed method compared to the other approaches.

Fulcrum decoders can trade off between computational complexity and the number of received packets. This allows heterogeneous nodes to decode at different level of complexity in accordance with their computing power. Variations of Fulcrum codes, like dynamic sparsity and expansion packets (DSEP) have significantly reduced the encoders and decoders' complexity by using dynamic sparsity and expansion packets. However, limited effort had been done for recoders of Fulcrum codes and their variations, limiting their full potential when being deployed at multi-hop networks. In this paper, we investigate the drawback of the conventional Fulcrum recoding and introduce a novel recoding scheme for the family of Fulcrum codes by limiting the buffer size, and thus memory needs. Our evaluations indicate that DSEP recoding mechamism increases the recoding goodput by 50%, and reduces the decoding overhead by 60%-90% while maintaining high decoding goodput at receivers and small memory usage at recoders compared with the conventional Fulcrum recoding. This further reduces the resources needed for Fulcrum codes at the recoders.

The Tactile Internet is a prominent example for the need to re-evaluate the trade-off between compression and real-time needs for smaller messages from/to sensors and actuators. Considering the ultra-low latency requirements in the millisecond range, we developed a use-case agnostic low-level approach to compress the amount of data that need to be sent. Our approach combines threshold-based delta-coding for individual values (BIN) and bit-plane level flexible compression through precision reduction (BLISS). We demonstrate how realistic data savings can be obtained with low compression losses and overheads using common Tactile Internet data sets through application of our approach. We find that our agnostic and case-independent approach yields data savings over the 50% range for low compression losses for Tactile Internet scenarios.

Blind Source Separation (BSS) for time-sensitive applications in the Internet of Things (IoT) results in a tradeoff between separation speed and accuracy. Data extraction has been widely employed recently to solve this problem. Although the introduction of current data extraction methods reduces the required time for separation, it is at the expense of separation quality. In this paper, we propose Adaptive extraction-based Independent Component Analysis (AeICA) to address these limitations. Specifically, the speed of separation is improved by using the extracted subset of the available data without affecting the overall separation accuracy, which we demonstrate through extensive numerical evaluations. In particular, AeICA reduces the total separation time by 50% to 75%, compared to the most remarkable related work.

The amount of consumer data transmitted over the internet will increase in the future. At the moment, this traffic is mainly handled by Content Delivery Networks (CDN) via Hypertext Transfer Protocol (HTTP). However, this server-based approach has the disadvantage that the servers themselves and their connections to the Internet are under a high load, which makes them a bottleneck. Therefore, new approaches for efficient content distribution are sought. One outstanding approach is the Interplanetary File System (IPFS), the synthesis of various successful Peer-to-Peer (P2P) approaches. Theoretically, the load on the server can be reduced by distributing the data to several nodes. To transmit data optimally, however, a high degree of coordination between nodes is necessary. Research on other content distribution schemes has shown that the cooperation effort can be avoided by using Random Linear Network Coding (RLNC). This has not been used in IPFS so far. In this work we present Multi Access Recoding System (MARS), a protocol which combines IPFS with RLNC. Simulations on the Interplanetary Testbed (IPTB) have shown that MARS is able to reduce the server load and download time by up to 50% compared to a transmission using only a single server and up to 45% compared to the same setup without RLNC. Even without coordination, this only causes an additional network load of 30%. In addition, MARS improves the download time by at least 15% even for very few peer nodes or peers with little storage.

Present-day and future network protocols that include and implement Forward Error Correction are configurable by internal parameters, typically incorporating expert knowledge to set up.We introduce a framework to systematically, objectively and efficiently determine parameters for Random Linear Network Codes (RLNC). Our approach uses an unbiased, consistent simulator in an optimization loop and utilizes a customizable, powerful and extendable parametric loss function. This allows to tailor existing protocols to various use cases, including ultra reliable, low latency communication (URLLC) codes. Successful configurations exploring the search space are under evolutionary pressure and written into a database for instant retrieval. We demonstrate three examples, Full Vector Coding, tail RLNC, and PACE with different focus for each.

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting of billions of small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority of applications require location-based services, it is necessary to improve the accuracy of localization algorithms. DV-Hop is one of the most attractive range-free localization algorithms in wireless sensor networks and several works have been undertaken to improve its accuracy, however, since sensor nodes have limited power resources, the energy consumption of nodes should be also considered. In this paper, we propose a method based on DV-Hop to improve both accuracy and power consumption. Each unknown node calculates the Hopsize of each anchor node according to the limited information it has from the network topology; therefore there is no need to broadcast the Hopsize from anchor nodes, and in this way energy can be saved. In the next step, we use Shuffled Frog Leaping Algorithm (SFLA) as an evolutionary algorithm to improve the accuracy of estimated Hopsizes and a hybrid Genetic-PSO algorithm is applied to the third step of DV-Hop to achieve more accurate values for unknown nodes' positions. Simulation results show that our proposed method decreases the localization error significantly by jointly considering the energy consumption of sensors and is overall 44% more accurate than DV-Hop.

In time-sensitive applications within industry 4.0, e.g. anomaly detection and human-in-the-loop, the data generated by multiple sources should be quickly separated to give the applications more time to make decisions and ultimately improve production performance. In this paper, we propose a Component-dependent Independent Component Analysis (CdICA) method that can separate multiple randomly mixed signals into independent source signals faster, for further data analysis in time-sensitive applications. Based on the Independent Component Analysis (ICA) algorithm, we first generate an initial separation matrix relying on the known mixture components, so that the separation speed of the traditional ICA can be increased. Our simulative results show that the CdICA method reduces the separation time by 55% to 83% compared to the most notable related work called FastICA and meanwhile it does not diminish the accuracy of the separation.

Wireless networks have to support an increasing number of devices with increasing demands on mobility and resilience. Mesh network routing protocols provide an elegant solution to the problem of connecting mobile nodes, due to their ability to adapt to topology changes. However, with increasing number of nodes and increasing mobility of the nodes, maintaining sufficiently recent routing information becomes increasingly challenging. Existing routing protocols fail to operate reliably in case of sudden link or node failures.In this work, we propose a new routing approach called SourceShift to resiliently handle dynamic networks in the absence of current network status information. SourceShift uses opportunistic routing and network coding, like MORE, but also makes use of link local feedback, like ExOR. We evaluate SourceShift in random network topologies with link and node failures and compare the results with the state of the art. The evaluation shows that SourceShift can ensure the delivery of the message when feasible. Additionally, the use of local feedback can improve the airtime efficiency compared to other routing protocols, even in cases without link or node failures. As a result, SourceShift requires less than half the airtime of state of the art routing protocols in more than 60% of the evaluated cases.

Data transmission in wireless networks is vulnerable to errors, due to the nature of wireless characteristics. As a result, corrupted packets are a common case in wireless data transmissions. Many techniques have been proposed to tackle these issues and one of the recommended techniques is the partial packet recovery (PPR) scheme. There are various existing PPR methods that have been proposed in wireless networks over the past two decades. In addition, the recent works have shown the possibility of network coding along with its capabilities in recovering the partial packets. A review study of PPR approaches in wireless networks is presented in this study. The authors classify the approaches into several groups based on the soft information in the PHY layer. Furthermore, they describe the PPR techniques that have used in each of these groups. Their studies found that there are 24 protocols have been proposed using PPR to improve the performance in wireless networks.

5G and beyond need to meet stringent requirements of latency, reliability, and support for heterogeneous devices. However, the existing wireless network architecture is limited to fulfill these constraints. Cloud radio access network, along with network function virtualization, is suggested to provide flexibility and network agility. It decouples network functions, such as firewall and packet gateway, from hardware to software deployed in the cloud. Thus comprehensive end-to-end formulation of this architecture is required for virtual network function placement. Most of existing works focus on virtual functions placement with different objectives, addressing different service requirements separately. In this article, six 5G constraints are considered simultaneously to find optimal virtual network function placement with service differentiation. The selected six parameters reflect services' requirements, network constraints and computing constraints. We first model the overall cloud radio access network as a multi-layer loopless-random hypergraph and we provide the overall formulation of the system. Then, we reformulate such model considering backup virtual functions and CPU over-provisioning techniques to improve both virtual function's reliability and processing latency. Finally, we propose service differentiation to reduce CPU utilization and energy consumption, while using the above techniques. The results suggest that the application of service differentiation can significantly improve assignment of computing resources and energy efficiency.

5G ecosystems will benefit significantly from Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV). While NFV allows for dynamical deployment of virtualized network functions, MEC allows applications to be deployed close to mobile users, thus reducing latency. When used together, NFV and MEC bring flexibility and enhanced performance to meet the user's demand. However, to increase service availability and maximize the convenience of MEC users, an MEC framework has to interface with multiple NFV orchestrators to utilize running network functions in an efficient manner. We introduce in this demonstration APMEC, a framework addressing the above two challenges. Via a combination of an interactive GUI, KPI views and a live-demo setup, we will showcase the advanced features of the framework.

This demonstrator, which is completely realized in software and hardware, shows the influence of latency in a very descriptive and interactive way with the example of a Mobile Edge Cloud or legacy cloud operated ball sorting machine. The demonstrator has every essential element that would be found in a real production machine or control loop. It consists of a sensor, a controller and several actuators. Through the Control and Monitoring Interface, the audience of the demo has a simple and self-explanatory way to interact with the demonstrator. In addition the advantages of Multi-access Edge Computing compared to legacy cloud computing and embedded computing are explained in the context of industry 4.0.

The following topics are dealt with: telecommunication traffic; Internet; mobile computing; 5G mobile communication; wireless LAN; Internet of Things; resource allocation; cloud computing; optimisation; software defined networking.

Virtualization of services in factory production allows achieving higher service reliability and smaller cost of industrial equipment. The demonstrator shows Mobile Edge Cloud (MEC) implementation for services such as path planning and movement control of a robot arm, collaboration between two robot arms, and remote control of a robot arm. The audience can participate in the demonstrator through guiding a robot arm remotely using a wireless controller. The MEC resources required for such virtualized services are shown in terms of traffic (throughput and inter-arrival time) and server load.

This proposal for demonstration focuses on network slicing as an enabler for flexible and efficient Industrial IoT (IIoT) networks. We show that by using network slicing in a novel three-layer architecture, self-organization and flexibility, as well as maximization of network efficiency can be achieved in IIoT networks. A practical scenario of conditional monitoring is demonstrated by means of distributing sensor devices on site to engage the audience.

This demonstration shows both the utility of Mobile Edge Cloud (MEC) for 5G connected cars, as well as the impact of MEC server selection (i.e. migration) strategy on latency. The demonstration simulates cars moving according to a realistic model inside the city of Munich, and implements different MEC server selection strategies. A player can steer an ambulance using a controller, and can also choose one among four server selection strategies while driving. With each strategy, the player will experience a different latency.

Conditional monitoring for industrial IoT often uses acoustic signals for non-invasive anomaly detection. The acoustic sensors capture the mixed sound of several working machines, which should be separated in per-machine components for further analysis. The accuracy can be in part improved by installing redundant acoustic sensors. However, this would increase the amount of the transmitted data. In this paper, we propose a joint application of (i) Blind Source Separation (BSS) to separate the mixed sound of several working machines, and (ii) Compressed Sensing (CS) for reducing the amount of data transmitted over the network for partially correlated data sources. We also propose a set of key performance indicators to evaluate the whole system. Our simulation results, performed using the FastICA and CVXPY libraries, show that our solution provides a well balance between the amount of transmitted data and the separation quality. In other words, it optimizes the network throughput for the given value of desired separation quality.

The sharing of skills over the Internet enables professionals to democratize their expertise and skills without exhausting their availability, e.g., through excessive traveling. To enable this Internet of Skills, we present a novel Digital Twin (DT) platform for the remote control of machines with human-in-the-loop. The DT of a remotely controlled machine acts effectively as an inter-layer between the operator and the controlled machine, e.g., robot arm. The DT can be optimized for a particular application to interact with the operator with an intuitive low-latency interface and, on other side, to control and monitor the quality of the remote task. Essentially, the human operator controls the DT, while the DT controls the remote robot. This paper introduces the DT framework for the remote control. The human-machine-human control loop is split into Virtual Reality (VR), remote control, and robot control loops. The proposed framework achieves low latency visual feedback and very short system reaction times for unexpected changes with arbitrary distances between operator and robot. Within the DT framework, this paper proposes a robot control algorithm for controlling time-critical robot applications over networks with considerable delays and jitter. The proposed framework has been implemented in a demonstrator with a robot arm and its DT in VR.

Random Linear Network Coding (RLNC) is widely considered a key enabler for 5G networks. It compensates for lost and corrupted packets by sending redundant coding packets. Despite the considerable attention RLNC received from the research community, the impact of the number of redundant coded packets on the network performance is poorly understood. An excessive number of redundancies would pollute the network with useless packets that give no additional information, and an insufficient number of redundancies would not provide enough packet loss resilience. We introduce a novel formal model for predicting the number of losses a system would have when RLNC is applied, by optimizing simulation results with the Newton-Raphson method. The model allows the sender to set the minimum amount of RLNC redundancies needed to keep the packet loss under a certain threshold. We validate our model against simulations by calculating the MSE between simulation results and our model.

The number of aircraft equipped with broadband connectivity is increasing. With several hundred users per aircraft, a high capacity air-to-ground link needs to be ensured. Today, several systems provide connectivity to aircraft. However, these systems are not optimized for changing aircraft densities in different geographical areas and thus the actual throughput per aircraft varies during the flight. Therefore, we investigate the achievable throughput during any given flight by analyzing actual flight routes in order to determine the relevant parameters to satisfy current and future connectivity needs. We show that multiple air-to-ground communication systems need to be exploited to offer sufficient throughput to all aircraft.

Providing broadband in-flight Internet connectivity to aircraft is challenging. Today's options include satellite communications (SC) and direct air-to-ground communication (DA2GC). To overcome data rate, delay and cost limitations of SC and coverage limitations of DA2GC, one can extend DA2GC with air-to-air communication (A2AC) by enabling multi-hop communication. To investigate the A2AC performance, we construct a mixed integer linear programming (MILP) problem of DA2GC and A2AC, jointly considering interference in topology formation and flow assignment. Our objective is to maximize the number of aircraft that can be connected with a given specific minimum data rate threshold. The evaluation is performed for low aircraft density scenarios over the North Atlantic. We show that in the investigated scenarios, over 90 % of aircraft can have at least 50 Mbps, some being up to 1600 kilometers away from the closest base station (BS). Furthermore, we identify antenna capabilities as an important factor for A2AC performance.

Responding to network security incidents requires interference with ongoing attacks to restore the security of services running on production systems. This approach prevents damage, but drastically impedes the collection of threat intelligence and the analysis of vulnerabilities, exploits, and attack strategies. We propose the live confinement of suspicious microservices into a sandbox network that allows to monitor and analyze ongoing attacks under quarantine and that retains an image of the vulnerable and open production network. A successful sandboxing requires that it happens completely transparent to and cannot be detected by an attacker. Therefore, we introduce a novel metric to measure the Quality of Deception (QoD) and use it to evaluate three proposed network deception mechanisms. Our evaluation results indicate that in our evaluation scenario in best case, an optimal QoD is achieved. In worst case, only a small downtime of approx. 3s per microservice (MS) occurs and thus a momentary drop in QoD to 70.26% before it converges back to optimum as the quarantined services are restored.

Next generation use-cases of mesh networks, such as connected vehicles and industrial devices, require low latency transmissions while fulfilling high reliability constraints. However, they also suffer from an increased protocol encapsulation overhead when handling a large number of messages with small payloads. A solution to this problem is to employ header compression algorithms in order to reduce the size of the individual protocol headers. Unfortunately, the current state-of-the-art header compression schemes cannot be readily applied to network topologies that contain a combination of multiple-hops and paths, as the compression only works favourably on a peer-to-peer, single-hop basis. With the unique combination of network coding and header compression one can always utilise unidirectional compression with maximum gain. In this paper we introduce and evaluate, for the first time, an integrated network coded header compression solution, which we call unidirectional Robust Header Compression (uRoHC). We show that one can - proportionally to the logical payload size - double the payload delivery efficiency compared to standard IPv4 and that we achieve results 10-15 % better than that of RoHCv2 for streams containing 33 bytes of payload.

Mobile Edge Computing (MEC) and Network Function Virtualization (NFV) are widely considered to be key players in the 5G era. Whereas MEC enables to reduce latency significantly by allowing applications to be deployed close to end users, NFV allows for flexible deployment of virtualized network functions. The performance and flexibility can be improved further by combining MEC and NFV. Existing frameworks for managing and orchestrating MEC applications and NFV are either tightly coupled or completely separated. The former design is inflexible and increases the complexity of one framework, while the latter leads to inefficient use of computation resources. In this paper, we extend our Automated Provisioning Framework for MEC (APMEC), which combines each MEC application and its respective network service (comprising a chain of virtual network functions) in a MEC service. We propose a novel MEC service placement algorithm allowing to reuse a subset of yet underloaded network functions. Our evaluation results, obtained from a testbed implementation and simulations, show that our solution allows to remarkably increase the utilization of network functions and simultaneously reduce routing cost. Specifically, it allows to accept at least 60% more user requests, and at the same time lowering the routing cost by more than 30%, as compared to the baseline approach.

Implementing DASH, the most popular method for multimedia streaming, over NDN, a potential future Internet architecture, can substantially increase the network bandwidth utilization. However, inherent features of NDN can create new security risks for adaptive multimedia streaming. We propose a novel attack called Bitrate Oscillation Attack (BOA), which adversely exploits NDN's autonomous on-path caching and interest aggregation to unsettle DASH functionality. BOA forces the resolution and quality of video received by the attacked client to oscillate with high frequency and amplitude. Subsequently, we present CoMon-DAS, a framework for lightweight coordination that mitigates BOA and other attacks in NDN. Through extensive simulations, we demonstrate that BOA is very harmful for DAS over NDN, but can be significantly mitigated by CoMon-DAS.

A wide variety of applications ranging from distributed storage to Forward Error Correction (FEC) benefit from Random Linear Network Coding (RLNC). Recent research activities exploited Network Coding as a Service (NCaaS) as FEC for Software-Defined Networking (SDN). Based on the aforementioned research, in this paper we propose a more efficient approach to use RLNC in a reliability-centric network. By leveraging both, the knowledge possessed by the SDN switches on channel conditions and the dynamic flexibility provided by the virtualized coding instances, we developed a novel method to adapt the code rate of the underlying FEC, to support two different transport protocols with varying reliability and throughput requirements. We have implemented our approach in a realistic SDN emulator to evaluate it in a communication network with time-variant links for TCP and UDP flows comparing against existing fixed-code rate approaches. The results show that our method adapts the flows respectively to the channel conditions and thus delivers a good trade-off between reliability, bandwidth, and overhead. Specifically, our loss estimation algorithm can precisely estimate future losses with a deviation of up to 3%. Our approach is also able to precisely determine the delivery probability with a maximum deviation of 1.5%. In addition, we show that our adaptive redundancy enables TCP to achieve a stable throughput despite losses.

The upcoming wireless network generation has put a large emphasis on the fulfilment of high reliability constraints. Nonetheless, the trade- off between these and other network aspects, mainly delay and bandwidth, is a constant optimisational question and a tough challenge. The various employed protocols add certain encapsulation overheads, which albeit necessary, however could potentially be excessive, such as in the case of various IoT, and similar applications with small payloads. Header compression aims to reduce these headers, but a general problem still plagues the standards since their introduction to loss-prone wireless networks, which is the issue of lost context (re)initialisation packets that can make the compression upstart and the transmission of major changes unreliable, slow and costly. In this paper we propose a solution that circumvents some concerns of traditional header compression context initialisation by the employment of network coding, which we call the reliable base proposal technique. This provides a finely tunable method for balancing reliability and delay of decompression with bandwidth gain. Our results show that both compression gain and reliability can be increased over the previous standards.

Systems for assisted and autonomous driving increasingly depend on information received and updated through wireless communication. But wireless communication often faces performance degradation because of its dynamic nature. Using multiple available communication channels, such as WiFi, LTE or 5G New Radio, simultaneously can increase the throughput and reliability, but also increases the dynamics of the system. MPTCP estimates the channel capacity and latency and schedule packets accordingly. However, in conditions with unstable channels MPTCP fails to fully utilize the available capacity. In this paper, we propose the use of Network Coding to efficiently utilize the available resources. We use a channel agnostic, random scheduler to maximize the utilization of all available channels. This prevents underestimations, but also produces a high number of packet loss and duplicate transmissions. We use Network Coding to repair the losses and reduce the overhead of redundant data. Our implementation of this protocol is evaluated against MPTCP in an emulated multipath network with time-varying path properties. The evaluation shows, that the proposed protocol utilizes the channels efficiently even in unstable conditions. In the evaluated dynamic network, the proposed protocol efficiently utilizes 94% of the available capacity, while MPTCP is below 80% due to underestimation. While our protocol is not suitable for general purpose traffic, it provides good performance for large file transfers in unstable wireless multipath networks.

Coding techniques that are employed to resolve packet losses on wireless channels, such as Random Linear Network Coding (RLNC), market themselves with the advantage of requiring less signalling, as well as, retransmissions of missing packets to compensate for losses on unreliable links. However, as packet sizes of IP-based protocols can be distributed irregularly over the available maximum frame size and can vary considerably packet-by-packet, the current implementations of RLNC suffer from shifting header and/or payload lengths and lack a suitable way of compensation. The simplest solution adopted by most RLNC approaches is to pad the unequal packets with zeros to the maximum packet size, thus creating an unnecessary transmission overhead of 100 % or more. This paper presents a practical implementation of the new progressive shortening based RLNC for the first time. This scheme utilizes fixed-sized regions inside the packets to resolve the zero-padding overhead and generates unequal-sized coded packets. Furthermore, it introduces a recoding feature for this scheme, which is another advantage of RLNC over other coding techniques, and breaks with the point-to-point topology considered in previous works. Moreover, we combine this novel macro-symbol based coding scheme with Robust Header Compression version 2 (RoHCv2) to show the gain over traditional implementation of RLNC in a real-life application where varying packet lengths dominate during transmissions. Our implementations results, using the KODO network coding library, show that the encoding throughput is as good as the established RLNC methods, and the payload delivery efficiency can be enhanced by up to 20 %.

Next generation use-cases of wireless networks require a great deal of flexibility in order to adopt to the constantly changing state of innovation and to accommodate future application requirements. Recent research has mostly focused on the integration and enhancement of both versions of Robust Header Compression instead of advancing the core concept of the compression. In this paper we present some of our novel concepts for multi-path/multi-hop, unidirectional, adaptive, reliable and traffic agnostic flow compression, which can diversify the application of header compression and make it suitable for a multitude of fifth generation demands.

Software-defined networking (SDN) and network function virtualization (NFV) processed in multi-access edge computing (MEC) cloud systems have been proposed as critical paradigms for achieving the low latency requirements of the tactile Internet. While virtual network functions (VNFs) allow greater flexibility compared to hardware-based solutions, the VNF abstraction also introduces additional packet processing delays. In this paper, we investigate the practical feasibility of NFV with respect to the tactile Internet latency requirements. We develop, implement, and evaluate Chain-based Low latency VNF ImplemeNtation (CALVIN), a low-latency management framework for distributed Service Function Chains (SFCs). CALVIN classifies VNFs into elementary, basic, and advanced VNFs; moreover, CALVIN implements elementary and basic VNFs in the kernel space, while the advanced VNFs are implemented in the user space. Throughout, CALVIN employs a distributed mapping with one VNF per Virtual Machine (VM) in a MEC system. Furthermore, CALVIN avoids the metadata structure processing and batch processing of packets in the conventional Linux networking stack so as to achieve short per-packet latencies. Our rigorous measurements on off-the-shelf conventional networking and computing hardware demonstrate that CALVIN achieves round-trip times from a MEC ingress point via two elementary forwarding VNFs (one in kernel space and one in user space) and a MEC server to a MEC egress point on the order of 0.32 ms. Our measurements also indicate that MEC network coding and encryption are feasible for small 256 byte packets with an MEC latency budget of 0.35 ms; whereas, large 1400 byte packets can complete the network coding, but not the encryption within the 0.35 ms.

With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management.

Future communication systems, such as those enabling the Tactile Internet, will face disruptive changes compared to the state-of-the-art systems, which are 1) highly dynamic topology changes; 2) replacement of the end-to-end paradigm by real mesh topologies; and 3) a massive number of devices. To overcome these disruptive changes, future communication systems will substitute specialized hardware with generic hardware boxes and the softwarization paradigm. Furthermore, this approach will allow for a quick deployment of new services, which was known to the cloud service already. In this paper, we will introduce the most prominent candidates for softwarization such as software-defined networking (SDN) and network function virtualization (NFV) and explain the importance of these technologies for the upcoming 5G communication system and Tactile Internet applications realizing novel mobile edge computing, storage, and networking solutions. Specifically, we will discuss use cases of SDN/NFV such as network coding as a service, and ultrareliable distributed edge caching. Finally, we will describe our holistic testbed at the 5G Lab Germany as a fundamental step toward creating an experiment infrastructure that anticipates the 5G communication systems and Tactile Internet applications.

Although cloud systems provide a reliable and flexible storage solution, the use of a single cloud service constitutes a single point of failure, which can compromise data availability, download speed, and security. To address these challenges, we advocate for the use of multiple cloud storage providers simultaneously using network coding as the key enabling technology. Our goal is to study two challenges of network coded storage systems. First, the efficient update of the number of coded fragments per cloud in a system aggregating multiple clouds in order to boost the download speed of files. We developed a novel scheme using recoding with limited packets to trade-off storage space, reliability, and data retrieval speed. Implementation and measurements with commercial cloud providers show that up to 9x less network use is needed compared to other network coding schemes, while maintaining similar download speeds and reliability. Second, the ability to update coded fragments from a linear erasure code when the original file is modified. We exploit code structure to provide efficient representations of the evolution of the file. Evaluations using file changes on software library repositories show that a five-order of magnitude reduction in network and storage use is possible compared to state-of-the-art.

The 5G communication system will provide flexible and programmable networks by leveraging softwarization technologies such as NFV and SDN. That advanced feature of 5G networks will be demonstrated in the context of real-time video surveillance for public safety. Specifically, we demonstrate for the first time a practical deployment of Random Linear Network Coding (RLNC) with NFV and SDN technologies to improve video quality against packet loss due to congestion at the core network and signal impairment of lossy channels at network edges. The demonstration implements NFV and SDN applications on COTS devices to prove its flexibility and portability.

The sharp increase in the video traffic and hand-held devices in the last decade warrant for new paradigms of applications including massive video multicasting, such as in the football stadiums, or concert halls where multiple angles of the live feed could be multicasted to the users' smart phones or tablets. However, the current state-of-the-art cellular solution is simply individually unicast the video stream to each node, which is extremely resource-inefficient. In this demo, we show how we perform reliable massive video multicasting over standard LTE links by combining the principle of cooperative networking and Random Linear Network Coding. We offload the majority of the traffic from LTE onto Wifi multicasts, thus increasing the efficiency of the LTE channel usage by multiple orders of magnitude. This demo has been demonstrated at the Mobile World Congress 2017 as a part of Nokia's booth to demonstrate the 5G stadium experience.

The massive increase in the number of connected devices with the advent of the Internet of Things era calls for self-organizing and dynamic networks. The state-of-the-art wireless mesh networking protocols that provide self-organizing networks like BATMAN and OLSR do not address the demands of 5G networks such as low latency and high reliability. In this demonstration, we exhibit how we build a reliable low latency network out of a chaotic and dynamic wireless mesh network. We combine the principles of opportunistic routing and network coding to achieve this. We let the audience to interact with the nodes of the wireless mesh networks, i.e. causing node failures, link changes, etc. We demonstrate how our mesh network retains all of its flows intact without any service interruption despite these interactions and node failures.

The future 5G mobile communication network, which aims to provide many more use cases not only for people but also for connecting machines. Some of these applications, like VR/AR and automation, call for low latencies, which cannot be achieved by aggregated data centers. Also, for VR/AR, offloading of computation will be a key element to bring new experiences to mobile devices. In order to fulfill low latency, outsourcing of computation and mobility, the Mobile Edge Cloud will play a key role in 5G networking. This demo shows an implementation of a VR game with the capability to move game servers across the world without service interruption.

Factory automation and production are currently undergoing massive changes, and 5G is considered being a key enabler. In this paper, we state uses cases for using 5G in the factory of the future, which are motivated by actual needs of the industry partners of the “5Gang” consortium. Based on these use cases and the ones by 3GPP, a 5G system architecture for the factory of the future is proposed. It is set in relation to existing architectural frameworks.

Many envisioned applications for the Tactile Internet require latencies that cannot be achieved by current wireless networks. In this paper, we present a method to optimize the reliability of a live stream using multiple paths. Compared to other approaches, our method does not rely on feedback to achieve the reliability and thus does not add delay for retransmission attempts. We use the diversity of multiple paths to compensate for the loss of capacity in individual paths. By using an FEC code to generate redundant data the transmission is more robust against fluctuations in capacity of single paths. We present an optimization problem to find a rate allocation for the paths that minimizes the interruptions of the stream. The performance is evaluated in an emulated network based on real-world measurements. The evaluation shows that the theoretical values can be achieved with a small margin. The optimized allocation of rates to the paths increases the reliability by up to 12%. With coding the reliability can be increased by up to 20%, depending on the code rate.

Massive content delivery in cellular networks is in the spotlight of the research community as data traffic is increasing at an incredibly fast pace. The existing LTE-A implementation for content broadcast presents several issues such as indoor coverage, along with low energy and spectral efficiency. Therefore, novel systems that provide efficient massive content delivery and reduced energy consumption are needed. In this paper we present a massive content distribution protocol that combines the benefits of cooperative mobile clouds (CMCs) with Random Linear Network Coding (RLNC) through multicast WiFi links. Our main goal is to offload data traffic from the LTE-A link and to reduce the energy consumption at the cooperating UEs. We solve the problem of excessive signaling that oftentimes arises in cooperative approaches by eliminating feedback messages within the CMCs. Instead, we provide a simple but accurate analytic model to correctly configure the number of coded transmissions to be performed within the CMCs. Results show that energy savings of more than 37 percent can be achieved with our protocol when compared to direct content download from the cellular base station. Furthermore, bandwidth utilization at the LTE-A link is sharply reduced.

Based on the impressive features that network coding and compressed sensing paradigms have separately brought, the idea of bringing them together in practice will result in major improvements and influence in the upcoming 5G networks. In this context, this paper aims to evaluate the effectiveness of these key techniques in a cluster-based wireless sensor network, in the presence of temporal and spatial correlations. Our goal is to achieve better compression gains by scaling down the total payload carried by applying temporal compression as well as reducing the total number of transmissions in the network using spatial compression and real field network coding. Furthermore, we compare our approach with benchmark schemes. As expected, our numerical results run on NS3 simulator show that overall our scheme dramatically drops the number of transmitted packets in the considered cluster topology by almost 94 % with a very high reconstruction SNR.

Service Function Chaining allows the flexible and efficient deployment of network functions for different applications. With Network Function Virtualization the elements of the chain can be provisioned in virtual environments on any COTS hardware. This introduces the question of where to position the individual network functions within the virtualization environment. This problem of network function placement has been studied in theory as an optimization problem on a graph. However it is challenging to apply theoretical work on practical deployments. In this paper, we perform a measurement campaign to study the delay introduced by Service Function Chaining. We propose placement heuristics and evaluate the performance on OpenStack. With the proposed heuristics, the service delay can be reduced by more than 20%. We measured the overhead introduced by a network function implemented in user space. The processing delay in user space can be twice as much as the same function in kernel space. More interestingly, we identified a service interruption of more than 1 second after activation of the chain.

Video on Demand (VoD) streaming constitutes a continuously increasing proportion of the global IP traffic. Conventional streaming approaches use a client-server topology, where one server or a group of servers serve all the clients. The main drawbacks of this technology is poor scalability. An alternative is the well known Peer-to-Peer (P2P) network topology which addresses this problem. However, for browser-based application, there is no standard method for distributed data dissemination. We present an application that uses WebRTC to establish direct browser to browser connections. We have designed two protocols WebPeer and CodedWebPeer for browser based P2P streaming. WebPeer is a conventional P2P protocol, while CodedWebPeer is a network coding enhanced P2P protocol. With our system, we demonstrate that using WebRTC P2P assisted VOD streaming can be accomplished. Furthermore, it outperforms the conventional client-server setup in terms of throughput by 70%, for the investigated scenario. In addition, by applying network coding, the performance of the network improves even further.

Future applications such as driverless cars, industrial Internet and smart grids will demand high bandwidth, resilience, security and low latency communication at the same time. Those technical requirements will be met by the 5G communication system, which is not only focusing on the future air interface but also the deployment in the core network. The paradigm shift from agnostic store-and-forward towards intelligent networks paves the ground for the mobile edge cloud - a concept to not only place cloud computing in close proximity to the users or things, such as in the base stations or even in access points in the houses, but also move the cloud with the user as the device moves along the network. This would enable minimum latency services, which require advanced migration techniques that facilitate not just quick but very frequent migrations. In this paper, we study and compare the state of the art migration techniques offered by virtualization tools like docker and KVM and propose an application level migration protocol that eliminates the drawbacks of the former. We also present the implementation of the proposed protocol in a latency sensitive gaming application, where the server is migrated live during the gameplay between hosts, transparently to the users, as a proof of concept and study the handover in detail.

This proposal for demonstration focuses on the realization of the mobile edge cloud for low latency 5G applications by means of a game to engage the audience. Through the use of intelligent application level migration techniques, we demonstrate an agile migration of a tron-like game between multiple potential edge cloud servers, while the game is running, with uninterrupted user interaction. The following demonstrator marks one of the first implementations of mobile edge cloud as a 5G enabling technology, while conveying the significance of latency in real time applications.

This demonstration proposal shows the benefit of connected cars over autonomous cars or cars controlled by street signs. The demo comprises six miniature cars that can be controlled over a wireless network while they are driving on a street with an intersection. The audience can always interact with the cars by stopping them at any place of the track and watch the system react to prevent any potential collision. The cars are controlled by a mobile edge cloud that is placed in an emulated network by Nokia. By using an Xbox Kinect, ultrasonic, and infrared sensors in the cars to emulate GPS and radar functionalities of a real car, this setup accurately mimics the ecosystem of a futuristic connected traffic scenario.

Video on Demand (VoD) streaming makes up an increasing portion of global IP traffic. Traditionally, data is served by content providers directly, putting a huge network load on the servers. We propose using our WebRTC-based protocols WebPeer and CodedWebPeer to create a P2P-assisted VoD streaming system with network coding. We introduce two metrics: network health to measure overall data saturation and network stability to show if the peers in the network are able to serve each other without the help of the server. We have implemented a testbed to emulate real-life network scenarios that includes a probabilistic model of peer behavior. We show through measurements that by applying network coding, network health is increased by up to 100% without changing the cache size or number of peers. Furthermore, network stability is achieved using up to half the cache compared to the uncoded approach, without increasing the servers load. Finally, to validate the scalability of our solution, we tested our protocols in a network with more than 100 tablets to deliver smooth video playback.

Next generation applications of wireless IP networks face an ever increasing demand of real-time dissemination of sensory and similar data to nearby devices. During situations when the transmission capabilities become scarce due to overused bandwidth, such as vehicular and various IoT use-cases, limiting the number of transmitted bytes over the wireless interfaces could ease the network load considerably. One of the main potential approaches for reducing the traffic in such scenarios is broadcasting, which could be even more efficient when paired with compression approaches, such as header compression. Normally header compression is employed to minimise the overhead of IP-based cellular traffic between two directly connected peers. However, in broadcasting scenarios, the compression has to balance the trade- off between servicing the least reliable channels (i.e., robustness to losses) and performing at peak compression rates (generating smaller packet headers). In order to circumvent these limitations, we propose a constraint on various IP and RTP fields when employing the industry-standard Robust Header Compression version 2 (which was designed for direct links) in a broadcast scenario without modifications of the underlying mechanisms, i.e., with full RFC compliance. With this approach the compression becomes impervious to packet losses up to 50% and for UDP profile compression with IPv6 it is practically unaffected by the link quality. In turn the compression retains at least 50% efficiency but can be as high as 90%.

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

Modern cellular networks utilising the long-term evolution (LTE) and the coming 5G set of standards face an ever-increasing demand for low-latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. We employ machine learning approaches for the prediction of Robust Header Compression version 2's (RFC 5225) compression utility for VoIP transmissions, which enables the compression to dynamically adapt to varying channel conditions. We evaluate the prediction models employing R2and mean square error scores next to complexity (number of coefficients) based on an RTP specific training data set and a separately captured live VoIP audio call. We find that the proposed weighted Ridge regression model explains about 70% of the training data and 72% of a separate VoIP transmission's utility. This approach outperforms the Ridge and first-order Bayesian regressions by up to 50% and the second and third order regressions utilising polynomial basis functions by up to 20%, making it well-suited for utility estimation.

It has been shown in the literature that Network Coding (NC) can boost the performance of wireless networks. However, to really obtain the potential gain of NC, efficient Medium Access Control (MAC) protocols that operate with awareness of the NC functions are necessary. In this paper, we propose a novel NC-aware MAC protocol for IEEE 802.11 wireless networks that combines k-batch transmissions and multiple receiver-initiated reverse direction exchanges involving NC data to boost the overall network performance. The proposed protocol allows any node to transmit a burst of data packets in a single channel access invocation. Then, an intermediate node can transmit an NC data packet when receiving a valid data packet from a source node, without contending for channel access. Both analytical and simulation results presented in this paper show the high throughput and energy efficiency of the proposed protocol with gains ranging from 33% to 298% when compared to existing mechanisms based on the IEEE 802.11 Standard.

Cyber-physical systems (CPS) use distributed feedback loops to control physical processes. Designing practical distributed CPS controllers often benefits from a logically centralized approach, where each node computes the control law locally based on global knowledge of the system state. We present Mixer, an all-to-all communication scheme that enables all nodes in a multi-hop low-power wireless network to exchange sizable packets with one another. Mixer's design integrates synchronous transmissions with random linear network coding, harnessing the broadcast nature of the wireless medium. Results from testbed experiments with an early Mixer prototype show that our design reduces latency by 1.1-2.6× for 16-96-byte packets compared with the state of the art, while providing a reliability above 99.9% in most settings we test.

Peer-to-Peer network topology is well known for its beneficial characteristic, like good scalability high robustness. In spite of this, web browsers are still using the standard server-client topology for data download. In this paper we investigate the methods of implementing Peer-to-Peer data streaming in web browsers, using only JavaScript, without the need of any third party plugin. We are using WebRTC to establish direct browser to browser connections. With its help, we are designing two efficient protocols for browser based Peer-to-Peer streaming. The first protocol is an efficient content sharing Peer-to-Peer protocol. The second one a network coding enhanced Peer-to-Peer protocol. In order to demonstrate the characteristics of the solution and prove the advantages of it we have established a testbed. In this testbed we run several measurements to analyze the behavior and the throughput of our protocols. Through our results, we show that modern browsers are capable of maintaining Peer-to-Peer connections and carrying out complex network coding calculations. We show that employing our protocols for data streaming, average data download speed can be significantly increased and server load can be decreased up to 80%. This research can be considered as a pioneer work in the field of Peer-to-Peer solutions with network coding, based purely on web technologies.

Modern cellular networks utilising the long-term evolution (LTE) and the coming 5G set of standards face an ever-increasing demand for low-latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. Since Robust Header Compression, among others, is primarily designed for the compression of live audio transmissions on endpoint devices, it performs best if certain fields, like the IP ID and RTP Timestamp, stay constant or change at a predetermined rate. Moreover, the compressor expects the uncompressed stream to be loss-free as well, which might not be the case in general scenarios. We employ machine learning approaches for the prediction of Robust Header Compression version 1's and version 2's compression utility under various loss rates and header field dynamics. We analyse how the compressions react to different fluctuations in the headers and choose the compressor configuration which maximises utility. We show that the appropriate choice of compressor repetition configuration increases the overall utility under dynamic channel conditions, such as in the case of remote steering and various IoT applications, and finding the optimal configuration could produce significant benefits, like 1.2 speed-up in a fully utilised network.

Although random linear network coding (RLNC) constitutes a highly efficient and distributed approach to enhance communication networks and distributed storage, it requires additional processing to be carried out in the network and in end devices. For mobile devices, this processing translates into energy use that may reduce the battery life of a device. This paper focuses not only on providing a comprehensive measurement study of the energy cost of RLNC in eight different computing platforms, but also explores novel approaches (e.g., tunable sparse network coding) and hardware optimizations for Single Instruction Multiple Data (SIMD) available in the latest generations of Intel and Advanced RISC Machines (ARM) processors. Our measurement results show that the former provides gains of two-to six-fold from the underlying algorithms over RLNC, while the latter provides gains for all schemes from 2× to as high as 20×. Finally, our results show that the latest generation of mobile processors reduce dramatically the energy per bit consumed for carrying out network coding operations compared to previous generations, thus making network coding a viable technology for the upcoming 5G communication systems, even without dedicated hardware.

In this paper, we propose an energy-efficient solution for implementing Network Coding (NC) in wireless networks based on the IEEE 802.11 Standard. The proposed mechanism, called GreenCode, allows nodes to duty cycle by switching to a low-power (sleep) state when they overhear coded packet transmissions that will not provide any new information for them. To facilitate the sleep operation, bidirectional transmissions involving both coded and non-coded packets between pairs of sender-receiver nodes are integrated into the operation of GreenCode. Both analytical and simulation results presented in this paper show the high energy efficiency of GreenCode with gains of up to 360% when compared to the existing mechanisms based on the IEEE 802.11 Standard.

Conventional cloud storage services offer relatively good reliability and performance in a cost-effective manner. However, they are typically structured in a centralized and highly controlled fashion. In more dynamic storage scenarios, these centralized approaches are unfeasible and developing decentralized storage approaches becomes critical. The novelty of this paper is the introduction of the highly dynamic distributed mobile cloud, which uses free resources on user devices to move storage to the edges of the network. At the core of our approach, lies the use of random linear network coding to provide an effective and flexible erasure correcting code. This paper identifies and answers key questions regarding the feasibility of such a system. We show that the mobile cloud has sufficient network resources to adapt to changes in node numbers and also study the redundancy level needed to maintain data availability. We have found that as little as 75% redundancy is enough to offer 99.28% availability for the examined period and essentially 100% availability is achieved when using 50% redundancy along with high-availability nodes. We have leveraged traces from a popular P2P mobile application to simulate the processes governing user behavior to show feasibility of mobile storage clouds in real scenarios.

Many networking visioners agree that 5G will be much more than the incremental improvement, in terms of data rate, of 4G. Besides the mobile networks, 5G will fundamentally influence the core infrastructure as well. In our vision the realization of the challenging promises of 5G (e.g. extremely fast, low-overhead, low-delay access of mostly cloudified services and content) will require the massive use of multipathing equipped with low overhead transport solutions tailored to fast, reliable and secure data retrieval from cloud architectures. In this demo we present a prototype architecture supporting such services by making use of automatically configured multipath service chains implementing network coding based transport solutions over off-the-shelf software defined networking (SDN) components.

Although Random Linear Network Coding (RLNC) has been shown to reduce the number of transmissions for multi-cast scenarios, there is an inherent signaling overhead to achieve this benefit. Employing a large field result in less transmissions but at the cost of a large coding vector of coefficients. Conversely, a smaller field fewer bits to represent the coding coefficients, but has the drawback of requiring more transmissions to deliver the data due to a high probability of linear dependence. This work advocates for the use of telescopic network codes as a way to achieve low overhead for cooperation in multicast transmissions over mobile networks. The idea behind them is to design a field with a larger size as a composite of a smaller field in order to be able to use some of the arithmetics of the extended field as operations in the base field, in order to allow for the use of different fields within a single generation. The resulting codes posses very low overhead by having a short coded packet representation and still maintain a low linearly dependent probability for the last packets. We provide an analytical framework and numerical results showing that is feasible to attain less than 3% total mean overhead. This is considerably lower than what can be achieved with RLNC schemes in most of the considered cases and achieving at least 1.5-2x gains.

Distributed storage solutions have become widespread due to their ability to store large amounts of data reliably across a network of unreliable nodes, by employing repair mechanisms to prevent data loss. Conventional systems rely on static designs with a central control entity to oversee and control the repair process. Given the large costs for maintaining and cooling large data centers, our work proposes and studies the feasibility of a fully decentralized systems that can store data even on unreliable and, sometimes, unavailable mobile devices. This imposes new challenges on the design as the number of available nodes varies greatly over time and keeping track of the system's state becomes unfeasible. As a consequence, conventional erasure correction approaches are ill-suited for maintaining data integrity. In this highly dynamic context, random linear network coding (RLNC) provides an interesting solution. Our goal is to characterize RLNC's guaranteed data integrity region in terms of the total number of storage devices that need to be available and stored data per device. We compare our fully distributed RLNC approach to centralized (genie aided) and fully decentralized replication and Reed-Solomon mechanisms. Our results use traces from a BitTorrent client for Android devices to show that RLNC outperforms the next best scheme (fully centralized Reed-Solomon) not only by having a much lower probability of data loss, but by reducing storage requirements by up to 50% and reconstruction traffic by up to 40%. Gains over decentralized schemes are even larger.

The articles in this special issue focus on fundamental approaches to network coding in wireless communications systems. Wireless communication network providers are constantly striving for more efficient and reliable service provision to billions of customers across the globe. As such, there exist great opportunities in the research and development of advanced network coding techniques in emerging wireless communication systems and applications for further improving network capacity and performance. Arguably, bandwidth-hungry applications,such as multimedia, are to benefit the most from the many advantages that wireless network coding can offer, particularly higher throughputs, lower delays, and better scalability. Wireless network coding has a great potential to be applied at the physical layer, harnessing inherent interference in the wireless channel for more spectral efficiency. It can significantly enhance the performance of relay-based, device-to-device, and cooperative communication techniques in current and future wireless systems.

This paper studies the problem of optimal use of a relay for reducing the transmission time of data packets from a source to a destination using network coding. More importantly, we address an effect that is typically overlooked in previous studies: the presence of active transmitting nodes in the neighborhood of such devices, which is typical in wireless mesh networks. We show that in systems with a fair medium access control mechanism (MAC), the use of a relay in a crowded medium brings forth considerable and unforeseen improvements, including up to 3.5x gains in terms of throughput compared to using only the direct link in some of our examples, and a considerable extension of the operating region where using a relay is beneficial. The problem is formulated as a Markov Decision Process (MDP) and numerical results are provided comparing simple, close-to-optimal heuristics to the optimal scheme.

An important challenge for the implementation of network coding in IEEE 802.11-based wireless networks is to give additional priority for channel access to the relay stations responsible for coding. These relay stations are able to provide more information in a single transmission than those that forward single packets, hence improving throughput and energy efficiency. The Distributed Coordination Function (DCF) of the IEEE 802.11 standard is a contention-based Medium Access Control (MAC) protocol that provides an equal distribution of channel access opportunities for all competing stations. However, the relay station represents a congestion point and additional transmission slots should be assigned to it to increase the overall network performance. To address this issue we investigate a coding-aware MAC protocol, called Reverse Direction DCF (RD-DCF), which enables bidirectional communications between the relay station and another station with a single channel access invocation. This simple and backwards compatible mechanism allows the relay station to transmit a coded packet together with the acknowledgement immediately after receiving a data packet. The simulation results show a gain of up to 130% in terms of both throughput and energy efficiency for RD-DCF with network coding when compared to DCF.

Packet losses in wireless networks dramatically curbs the performance of TCP. This paper introduces a simple coding shim that aids IP-layer traffic in lossy environments while being transparent to transport layer protocols. The proposed coding approach enables erasure correction while being oblivious to the congestion control algorithms of the utilised transport layer protocol. Although our coding shim is indifferent towards the transport layer protocol, we focus on the performance of TCP when ran on top of our proposed coding mechanism due to its widespread use. The coding shim provides gains in throughput that exceed 10x for TCP traffic while requiring a limited sacrifice in terms of fairness towards other flows on the channel.

Random Linear Network Coding (RLNC) provides a theoretically efficient method for coding. The drawbacks associated with it are the complexity of the decoding and the overhead resulting from the coding vector. This adds to the overall energy consumption and is problematic for computational limited and battery driven platforms. In this work we present an approach to RLNC where the code is sparse and non-uniform. The sparsity allow for fast encoding and decoding, and the non- uniform protection of symbols enables recoding where the produced symbols are indistinguishable from those encoded at the source. The results show that the approach presented here provides a better trade- off between coding throughput and code overhead. In particular it can provide a coding overhead identical to RLNC but at significantly reduced computational complexity. It also allow for easy adjustment of this trade-off, which make it suitable for a broad range of platforms and applications. Finally it is easy to perform recoding and coding vectors can be efficiently represented.

Peer-to-peer networks constitute a widely used, cost-effective and scalable technology to distribute bandwidth-intensive content. The technology forms a great platform to build distributed cloud storage without the need of a central provider. However, the majority of todays peer-to-peer systems require complex algorithms to schedule what parts of obtained content to forward to other peers. Random Linear Network Coding can greatly simplify these algorithm by removing the need for coordination between the distributing nodes. In this paper we propose and evaluate the structure of the BRONCO peer-to-peer system, which applies random linear network coding. We focus on an experimental evaluation of the performance on 36 real nodes. The evalution shows that BRONCO outperforms regular HTTP transfers, and, with a extremely simple protocol structure, performs equivalently to bittorrent distribution. Furthermore, we evaluate the performance of different parameters and suggest a suitable trade- off between CPU utilization and network overhead. Within the limitations of the used test environment, we have shown that networkc coding is usable in peer-assisted content distribution and we suggest further improvements to reduce redundancy overhead.

This paper presents the design and performance evaluation of an inexpensive testbed for network coding protocols composed of Raspberry Pis. First, we show the performance of random linear network coding primitives on the Raspberry Pi in terms of processing speed and energy consumption under a variety of configuration setups. Our measurements show that processing rates of up to 230 Mbps are possible with the Raspberry Pi. Also, the energy consumption per bit can be as small as 3 nJ/bit, which is several orders of magnitude smaller than the transmission/reception energy use. Surprisingly, overclocking the Raspberry Pi from 700 MHz to 1000 MHz not only produces an increase in processing speed of up to 68 % for large generation sizes, but also provides a reduction of 64 % in the processing energy per bit for most tested scenarios. Then, we show Raspberry Pi as an inexpensive, viable, and flexible platform to deploy large research networking testbeds for the evaluation of network coding protocols. We propose key parameters and representations to evaluate protocol performance in network nodes as well as validating the testbed's statistics using the case of a one-hop broadcast with random linear network coding, which is well understood in theory.

Traditionally, the idea of overlapping generations in network coding research has focused on reducing the complexity of decoding large data files while maintaining the delay performance expected of a system that combines all data packets. However, the effort for encoding and decoding individual generations can still be quite high compared to other sparse coding approaches. This paper focuses on an inherently different approach that combines (i) sparsely coded generations configured on-the- fly based on (ii) controllable and infrequent feedback that allows the system to remove some original packets from the pool of packets to be mixed in the linear combinations. The latter is key to maintain a high impact of the coded packets received during the entire process while maintaining very sparsely coded generations. Interestingly, our proposed approach naturally bridges the idea of overlapping generations with that of tunable sparse network coding, thus providing the system with a seamless and adaptive strategy to balance complexity and delay performance. We analyze two families of strategies focused on these ideas. We also compare them to other standard approaches both in terms of delay performance and complexity as well as providing measurements in commercial devices to support our conclusions. Our results show that a judicious choice of the overlapping of the generations provides close-to-optimal delay performance, while reducing the decoding complexity by up to an order of magnitude with respect to other schemes.

In this paper, we present results from a practical evaluation of network coding in a setup consisting of eight nodes deployed in a chain topology. With the tradition pure relaying, delay increases dramatically as the network gets congested, and here network coding helps to moderate this increase in delay, as well improving throughput. The practical evaluation shows that network coding provides up to a five-fold decrease in delay, while retaining the expected gain in throughput. To address an unecessary delay when using network coding in low-load scenarios, we propose and evaluate a scheme for adaptive buffering. With this, we show that the benefits from pure relaying can be combined with the improved performance from network coding. The software used to apply network coding and evaluate this in a practical network is made publicly available for further research and tests.

Creating efficient finite field implementations has been an active research topic for several decades. Many applications in areas such as cryptography, signal processing, erasure coding and now also network coding depend on this research to deliver satisfactory performance. In this paper we investigate the use of prime fields with a field size of 232- 5, as this allows implementations which combines high field sizes and low complexity. First we introduce the algorithms needed to apply prime field arithmetics to arbitrary binary data. After this we present the initial throughput measurements from a benchmark application written in C++. These results are finally compared to different binary and binary extension field implementations. The results show that the prime field implementation offers a large field size while maintaining a very good performance. We believe that using prime fields will be useful in many network coding applications where large field sizes are required.

Network coding (NC) has attracted tremendous attention from the research community due to its potential to significantly improve networks' throughput, delay, and energy performance as well as a means to simplify protocol design and naturally providing security support. The possibilities in code design have produced a large influx of new ideas and approaches to harness the power of NC. But, which of these designs are truly successful in practice? and which designs will not live up to their promised theoretical gains due to real-world constraints? Without attempting a comprehensive view of all practical pitfalls, this paper seeks to identify key ingredients to a successful design, critical and common limitations to most intra-session NC systems as well as promising techniques and ideas to guide future models and research problems grounded on practical concerns.

This paper presents a detailed performance evaluation of inter-session network coding in wireless meshed networks in terms of throughput and energy consumption. A full analytical model is given for three different communication approaches for the bidirectional cross topology using an IEEE 802.11 medium access. One of the three approaches is pure relaying, while the other two approaches are using network coding with and without overhearing of other flows. The main outcome of the paper is that network coding without and with overhearing can increase the throughput by the factor of two and four, respectively, for high load scenarios. Furthermore we show that the energy/bit ratio is decreased by the use of network coding approaches, underlining that the added complexity of network coding pays off when considering the overall system.

This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [?] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows as the generation size grows. These results also indicate that the use of high field sizes could be problematic from an energy point of view due to the additional complexity.

State-of-the-art in network coding for wireless, meshed networks typically considers two problems separately. First, the problem of providing reliability for a single session. Second, the problem of opportunistic combination of flows by using minimalistic coding, i.e., by XORing packets from different flows. Instead of maintaining these approaches separate, we propose a protocol (CORE) that brings together these coding mechanisms. Our protocol uses random linear network coding (RLNC) for intra- session coding but allows nodes in the network to setup inter- session coding regions where flows intersect. Routes for unicast sessions are agnostic to other sessions and setup beforehand, CORE will then discover and exploit intersecting routes. Our approach allows the inter-session regions to leverage RLNC to compensate for losses or failures in the overhearing or transmitting process. Thus, we increase the benefits of XORing by exploiting the underlying RLNC structure of individual flows. This goes beyond providing additional reliability to each individual session and beyond exploiting coding opportunistically. Our numerical results show that CORE outperforms both forwarding and COPE-like schemes in general. More importantly, we show gains of up to 4 fold over COPE-like schemes in terms of transmissions per packet in one of the investigated topologies.

One of the biggest problems nowadays for network operators are occurring call drops. This problem has been increasing in the last years specially since the advent of 3G. The investigation in the operator's network is very time intensive and due to the highly priced hardware only a few measurements can be done per day. In this paper we present a new methodology to investigate call drops by using mobile phones to do the measurements following the concept of citizen sensing. Therefore, a mobile application for Android is made that collects all necessary data and dumps the measurement results in a centralized database where the measurements are evaluated and represented on Google Maps. With a post analysis of the measurements, a classification of the call drops results is made. The collected data is also used to show some statistics related to the battery level and the received signal strength between mobile phone and network. This low cost variant of field testing is developed for call drops but could be used for any other parameter of interest.

The long propagation delays of the underwater acoustic channel make traditional Medium Access schemes impractical and inefficient under water. This paper introduces and studies Interference Avoidance and Network Coding for Medium Access protocol design aiming to cope with the underwater channel constraints and achieve efficient data transmission under water. Network Coding can exploit the broadcast channel to send different information to several receivers simultaneously. With Interference Avoidance the long propagation delay can be used to communicate in full-duplex mode. Alone and combined these concepts could increase channel utilisation as well as improve energy efficiency of the network nodes. The main goal is to investigate the potential benefits of new strategies for data dissemination over a string topology scenario. Comprehensive simulations prove the feasibility of Interference Avoidance and Network Coding improving the system efficiency when compared with CSMA/CA.

This paper investigates the problem of efficient data dissemination in Mobile Ad hoc NETworks (MANETs) with high mobility. A testbed is presented; which provides a high degree of mobility in experiments. The testbed consists of 10 autonomous robots with mobile phones mounted on them. The mobile phones form an IEEE 802.11g ad hoc network to communicate with each other. A dynamic network topology is assumed, where the mobile devices form a cooperative cluster in order to exchange data packets among each other. In our multimedia exchange scenario, the initial state is that one device carries all information, and the goal is to convey that information to all devices. A strategy is proposed that uses UDP broadcast transmissions and random linear network coding to facilitate the efficient exchange of information in the network. An application is introduced that implements this strategy on Nokia phones. The measurement results collected from the testbed are presented, and the performance of the proposed strategy is compared to a reference strategy that is based on TCP unicast connections.

In group-oriented applications for wireless networks, reliable multicast strategies are important in order to efficiently distribute data, e.g. in Wireless Mesh Networks (WMNs) and Mobile Ad-hoc NETworks (MANETs). To ensure that developed protocols and systems will operate as expected when deployed in the wild, a good understanding of several factors such as packet loss characteristics is necessary. In this paper the correlation of erasures in a cluster of receiving mobile devices is measured and analyzed. In the considered scenario, a source node broadcasts packets to a cluster of receivers located relatively far away. To ensure that the obtained data can easily be applied in analysis, we introduce the \textit{cluster erasure transition matrix}. We then analyze a simple broadcast and cooperative scheme, and show that the assumption of independent packet erasures unfairly favors the cooperative scheme according to the obtained measurements.

This paper investigates the performance of network coding for an IEEE802.11 enabled meshed network. By means of basic setups the impact of the medium access control in combination with network coding is investigated. In contrast to prior work the network coding approach is tailored to commercial WiFi hardware without any special tweaks. The implementation of network coding is done on top of an existing routing scheme known as B.A.T.M.A.N. which has some inherent advantages to support network coding. We present schemes to utilize the B.A.T.M.A.N. routing to detect coding opportunities. One finding is that the performance gain for the well known Alice and Bob scenario using network coding is 60\% compared to a pure relaying scheme. The software used in the presented measurement campaign is made publicly available.

The advantages of network coding have been extensively studied in the field of wireless networks. Integrating network coding with existing IEEE 802.11 MAC layer is a challenging problem. The IEEE 802.11 MAC does not provide any reliability mechanisms for overheard packets. This paper addresses this problem and suggests different mechanisms to support reliability as part of the MAC protocol. Analytical expressions to this problem are given to qualify the performance of the modified network coding. These expressions are confirmed by numerical result. While the suggested reliability mechanisms introduce some signaling overhead, the results show that the performance is yet improved.

This paper discusses the future of content distribution among mobile devices forming the so-called mobile clouds. This paper introduces the current-technology-based problems of the approach, but also highlights its future potential. One core element of this paper is the technical development in this area and the social paradigm that will be used to create cooperation among users. We conclude that the future of mobile clouds will be in novel technologies such as network coding as well as in combination with social networks in order to boost cooperation among users as well as connect people over the shared content.

This paper presents the implementation of synchronized multimedia streaming for the Apple iPhone platform. The idea is to stream multimedia content from a single source to multiple receivers with direct or multi-hop connections to the source. First we look into existing solutions for video streaming on the iPhone for point-to-point architectures. After acknowledging their limitations, we propose a solution based on network coding to efficiently and reliably deliver the multimedia content to many devices in a synchronized manner. Then we introduce an application that implements this technique on the iPhone. We also present our testbed which consists of 16 iPod Touch devices to showcase the capabilities of our application.

This paper investigates the possibility of packet error recovery in a cooperative cluster of mobile devices. We assume that these devices receive data from a broadcast transmission on their primary network interface (e.g. LTE network), and they are using a secondary network interface (e.g. ad hoc WLAN network) to form a cooperative cluster in order to exchange missing data packets among each other. Our goal is to devise a protocol that minimizes the number of packets exchanged on the secondary network whilst maximizes the number of packet errors recovered on the primary network. Moreover, we aim to repair the packet losses on-the-fly (as the data is being received), which also imposes real-time constraints on the protocol. We propose a solution based on random linear network coding to form cooperative clusters of mobile devices to facilitate the efficient exchange of information among them. We also introduce a demo application that implements this technique on Nokia phones. Then we present our testbed and the collected measurement results in order to evaluate the performance of our protocol.

In this paper user cooperation with network coding is applied to MBMS (Multimedia broadcast/multicast service) where Raptor codes are currently used. User cooperation together with network coding is used to save bandwidth and improve user perceived QoS for broadcast/multicast services in the future mobile communication networks. The proposed approach is tailored to LTE networks and extended with local cooperation. The simulation results show that local retransmissions can reduce the amount of redundant information on the cellular link with up to 80% as long as there are at least two cooperative mobile devices. The results also show that network coding can save more than half of the traffic on the short-range link as long as there are four devices in the cooperation cluster.

Random Linear Network Coding (RLNC) provides a theoretically efficient method for coding. The drawbacks associated with it are the complexity of the decoding and the overhead resulting from the encoding vector. Increasing the field size and generation length presents a fundamental trade-off between packet-based throughput and operational overhead. On the one hand, decreasing the probability of redundant packets' being transmitted is beneficial for throughput and, consequently, reduces transmission energy. On the other hand, the decoding complexity and amount of header overhead increase with field size and generation length, leading to higher energy consumption. The main findings of this work are bounds for the transmission overhead due to linearly dependent packets. The optimal trade-off is system and topology dependent, as it depends on the cost in energy of performing coding operations versus transmitting data. We show that moderate field sizes are the correct choice when trade-offs are considered. The results show that sparse binary codes perform the best, unless the generation size is very low.

Wireless cooperation is emerging as a viable approach for next generation networks to offer important benefits in terms of content transfer delay, data rate, energy consumption, and cell capacity. One of the possible applications of a wireless user cooperation paradigm is represented by the provision of content sharing services. Data is seeded into a cooperative cluster through cellular links while short-range links are used to distribute data to all the interested nodes. Besides showing the beneficial effects of cooperation, this paper focuses on the best way to seed information into such a cluster of nodes and on a solution offering benefits in terms of either the time or the energy consumption for the service. Starting from static environments, the potentialities offered by an ad-hoc conceived Genetic Algorithm (GA) are shown. The analysis is then extended to the more realistic nomadic environments with low speed pedestrian mobility for the nodes.

This paper compares the energy and time needed to convey one information unit from source to destination using multi hop networks versus overlay networks. For a set of realistic parameters the paper will show that multi hopping with more than 50 relays will need more time than the overlay network and with respect of energy the usage of more than 6 relays is using more energy than the overlay network. The paper underlines the importance of the idle power value for such calculations.

This paper investigated the possibility of intrinsic information conveying in network coding systems. The information is embedded into the coding vector by constructing the vector based on a set of predefined rules. This information can subsequently be retrieved by any receiver. The starting point is Random Linear Network Coding (RLNC) and the goal is to reduce the amount of coding operations both at the coding and decoding node, and at the same time remove the need for dedicated signaling messages. In a traditional RLNC system, coding operation takes up significant computational resources and adds to the overall energy consumption, which is particular problematic for mobile battery-driven devices. In RLNC coding is performed over a Finite Field. We propose to divide this field into sub fields, and let each sub field signify some information or state. In order to embed the information correctly the coding operations must be performed in a particular way, which we introduce. Finally we evaluate the suggested system and find that the amount of coding can be significantly reduced both at nodes that recode and decode.

This paper introduces a user cooperation technique thought to support content sharing services in wireless environments. WLAN inter-device data exchange is effectively exploited, combined with network coding techniques. Specifically, the content of interest is seeded into the WLAN user group through cellular-enabled devices, while short-range links are used to cooperatively distribute data to all the interested users. The focus is on scenarios where nodes in the same network area are not all interested in the same content, they rather form different cooperative groups. Beneficial effects of intra-group cooperation have already been demonstrated in the past; differently, this research focuses on inter-group cooperation. Objective of this paper is the investigation of any obtainable benefit (either in terms of energy consumption, service latency, or probability of successful service distribution) from such a cooperative attitude of different groups.

The full degree of freedom in mobile systems heavily depends on the energy provided by the mobile phone's batteries. Their capacity is in general limited and for sure not keeping pace as the mobile devices are crammed up with new functionalities. The discrepancy of Moore's law, offering twice the processing power at least each second year, and the development in batteries, which did not even double over the last decade, makes a shift in researchers' way of designing networks, protocols, and the mobile device itself. The bottleneck to take care of in the design process of mobile systems is not only the wireless data rate, but even more the energy limitation as the customers ask for new energy-hungry services, e.g., requiring faster connections or even multiple air interfaces, and longer standby or operational times of their mobile devices at the same time. In this survey, the energy consuming entities of a mobile device such as wireless air interfaces, display, mp3 player and others are measured and compared. The presented measurement results allow the reader to understand what the energy hungry parts of a mobile device are and use those findings for the design of future mobile protocols and applications. All results presented in this work and further results are made public on our web page [2].

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

In areas where Internet access is only sporadically available, or not available at all, delivering content (text, image, video, audio, application, etc.) to the people is not easy. In this paper, we introduce a novel method to spread content in such areas in a mobile peer-to-peer way. First we describe the architecture of the system along with the proposed communication protocol among the nodes. Then we discuss the advantages for the users and some scenarios where such a system may be useful. Finally, we examine the cooperation among the nodes and how users may be motivated to share their resources with others.

This paper is looking into the possibility of multimedia content distribution over multiple mobile platforms forming wireless peer--to--peer networks. State of the art mobile networks are centralized and base station or access point oriented. Current developments break ground for device to device communication. In this paper we will introduce a mobile application that runs on Symbian as well as iPhone/iPod devices and is able to exchange multimedia content in a point to multipoint fashion. The mobile application coined PictureViewer can convey pictures from one source device to many neighboring devices using a wireless 802.11 network together with network coding and user cooperation. The advantage of network coding in this context is that the source devices only need a minimal amount of knowledge about the sinks received packets and therefore only a minimal amount of feedback is needed to ensure reliable data delivery.

We propose a linear network coding scheme to disseminate a finite number of data packets in arbitrary networks. The setup assumes a packet erasure channel, slotted time, and that nodes cannot transmit and receive information simultaneously. The dissemination process is completed when all terminals can decode the original data packets. We also assume a perfect knowledge of the information at each of the nodes, but not necessarily a perfect knowledge of the channel. A centralized controller decides which nodes should transmit, to what set of receiver nodes, and what information should be broadcasted. We show that the problem can be thought of as a scheduling problem, which is hard to solve. Thus, we consider the use of a greedy algorithm that only takes into account the current state of the system to make a decision. The proposed algorithm tries to maximize the impact on the network at each slot, i.e. maximize the number of nodes that will benefit from the coded packet sent by each active transmitter. We show that our scheme is considerably better, in terms of the number of slots to complete transmission, than schemes that choose the node with more information as the transmitter at every time slot.

In this paper we develop and analyze energy- efficient cooperative techniques for multimedia streaming applications in wireless networks. The concept of combined cellular- SR network architecture is coupled here with different video coding schemes for the goal of achieving robust transmission over wireless channels and energy saving. The strategies are analyzed in different technology settings, showing promising power saving potentials in comparison to the state-of-the-art non-cooperative operation.

To solve the performance degradation issue in current WLAN caused by the crowded unlicensed spectrum, we propose a cognitive radio (CR) media access protocol, C-CSMA/CA. The basic idea is that with cognitive radio techniques the WLAN devices can not only access the legacy WLAN unlicensed spectrum but opportunistically access any other under-utilized licensed spectrum without a license. The application scenario of C-CSMA/CA is infrastructure BSS (Basic Service Set) WLAN. C-CSMA/CA efficiently exploits the inherent characteristics of CSMA/CA to design distributed cooperative outband sensing to explore spectrum hole; moreover, it designs dual inband sensing scheme to detect primary user appearance. Additionally, C-CSMA/CA has the advantage to effectively solve the cognitive radio self-coexistence issues in the overlapping CR BSSs scenario. It also realizes station-based dynamic resource selection and utilization. It is compatible with any legacy WLAN (BSS) system. We develop and implement the simulation of C-CSMA/CA by OPNET. The simulation results show that C-CSMA/CA highly enhances throughput and reduces the queuing delay and media access delay.

In this paper we propose the one4all cooperative access strategy to introduce a more efficient media access strategy for wireless networks. The one4all scheme is designed for the infrastructure based distributed wireless network architecture. The basic idea is that mobile devices can form a cooperative cluster using their short-range air interface and one device contends the channel for all the devices within the cluster. This strategy reduces the number of mobile devices involved in the collision process for the wireless medium resulting in larger throughput, smaller access delay, and less energy consumption. Based on an analytical model, the proposed strategy is compared with the two existing strategies RTS/CTS (request to send/ clear to send) and packet aggregation. The results show that the proposed cooperative scheme has similar throughput performance as packet aggregation and it has much higher throughput than the conventional RTS/CTS scheme. Furthermore, the newly introduced cooperative scheme outperforms packet aggregation in terms of channel access delay and energy consumption.

Packet reservation multiple access (PRMA) protocol is an implicit reservation MAC protocol. It is initially designed for voice packets in the cellular networks, but it is currently also used for data packets in OFDM based fixed wireless access networks. When it is applied for data packets, the system throughput depends on the size of packets and the number of consecutive packets. From the statistics of existent wireless data networks using PRMA protocol, it shows that the system throughput is quite low because of the inconsecutive small packets. In order to improve the throughput, packet aggregation scheme is considered to be applied in PRMA. Before designing packet aggregation algorithm, it is worth investigating the effect of packet aggregation scheme on the performance of throughput and delay. In this paper we develop a generic Markov chain model for PRMA with packet aggregation. Based on this model the throughput and delay are derived and analyzed. A numerical example is calculated, which illustrates the effect of packet aggregation on the throughput and delay with varying packet arrival rate. The results of the paper are valuable inputs for designing optimal packet aggregation algorithm, considering the tradeoff between throughput and delay.

In this paper we advocate to exploit terminal cooperation to stabilize IP communication using header compression. The terminal cooperation is based on direct communication between terminals using short range communication and simultaneously being connected to the cellular service access point. The short range link is than used to provide first aid information to heal the decompressor state of the neighboring node in case of a packet loss on the cellular link. IP header compression schemes are used to increase the spectral and power efficiency loosing robustness of the communication compared to the uncompressed version. By introducing the terminal cooperation supporting header compression the robustness is increased. Within this article we will show that header compression should be applied to reduce the energy consumption of the terminals and moreover the header compression should be supported by cooperation to increase the robustness in terms of a decreased packet loss rate.

This paper reports the results of a measurement campaign on the connectivity level of mobile devices using Bluetooth (BT) to form cooperative mobile mesh networks. Such mobile mesh networks composed of mobile devices are the basis for any peer-to-peer communication like wireless grids or social networks. The results represent typically worst-case situations, as more and more people tend to switch off their short-range technology due to the battery consumption and the possible attacks. We explore typical public places like airports, convention centers, shopping malls and bars and extract from them some statistics of the size of the mesh network and type of constituent nodes. This information is essential for analyzing and devising cooperative strategies among the terminals of mesh networks in different scenarios.

Reliable multicast applications such as software distribution, data distribution and replication and mailing list delivery, etc. [1] are getting more and more interests from network and service providers. The conventional error/loss recovery schemes are not efficient when they are applied to multicast scenarios in wireless networks. The reason lies in the unreliable wireless channel, the limited wireless bandwidth and resource, the battery powered wireless devices, and others. To have an effective error/loss recovery scheme for reliable multicast in wireless networks, we advocate a new communication architecture. It is referred to as cellular controlled peer-to- peer network, where the mobile devices communicate directly with each other to perform cooperative retransmissions using their short-range communication capabilities in addition to their cellular links. Based on the cooperative architecture a novel retransmission scheme is proposed exploiting the short-range retransmission in this paper. The state of the art, the non-cooperative error recovery schemes (e.g., ARQ, Layered FEC and Integrated FEC II) and the proposed scheme are compared with each other in terms of energy consumption to show the benefit of the newly introduced scheme.

Packet reservation multiple access (PRMA) protocol is an implicit reservation MAC protocol. It is initially designed for voice packets in the cellular networks [2, 3] but it is currently also used for data packets in OFDM based fixed wireless access networks [8, 9], When it is applied for data packets, the system throughput depends on the size of packets and the number of consecutive packets. From the statistics of existent wireless data networks using PRMA protocol, it shows that the system throughput is quite low because of the inconsecutive small packets. In order to improve the throughput, packet aggregation scheme is considered to be applied in PRMA. Before designing packet aggregation algorithm, it is worth investigating the effect of packet aggregation scheme on the performance of throughput and delay. In this paper we develop a generic Markov chain model for PRMA with packet aggregation. Based on this model the throughput and delay are derived and analyzed. A numerical example is calculated, which illustrates the effect of packet aggregation on the throughput and delay with varying packet arrival rate. The results of the paper are valuable inputs for designing optimal packet aggregation algorithm, considering the tradeoff between throughput and delay.

A cooperative scheme enabling spatial reuse, namely cooperative spatial reuse (CSR), is proposed as a cooperative extension of the current TDMA-based MAC in wireless networks. In the CSR, a cooperative group is formed by the links that are willing to do spatial reuse. In the group, every cooperating link contributes its time slots for spatial reuse among the cooperating participants. Following the cooperation principle, a link joins the group only if it can benefit. Otherwise, the link will stop doing CSR and switch back to the TDMA-based MAC. In this work, we focus on the transmit beamforming techniques to enable CSR on MISO (Multiple Input Single Output) links. We compared the CSR scheme using zero-forcing (ZF) transmit beamforming, namely ZF-CSR, to the TDMA-based MAC using maximum ratio combining (MRC) transmit beamforming, namely MRC-TDMA. The numerical results of a simulated two 2 x 1 MISO links scenario show the great potential of CSR to substantially increase the capacity and energy efficiency.

In this paper, we look at multiple antenna systems and code information on the communication medium itself to improve the link security. In the case of single data stream transmission, the medium refers to the response across the receive sensor array. In multiple data stream transmission, the medium is the set of virtual orthogonal channels that result from the singular value decomposition of the channel transfer matrix. We code phase information on the orthogonal channels by rotating the received signal constellation. This does not impair the signal detection of differential-phase coded signals on the intended receiver. However the rotation at an eavesdropper location is different from the one on the intended receiver even at a distance of lambda/10 (6 mm), and therefore security is enhanced.

This paper introduces power saving strategies for cooperative wireless communication systems. The described scenario focuses on IP-services over DVB-H networks showing the strength of non-altruistic cooperation between mobile devices. The envisioned cooperation is based on cellular reception of data, which is then shared among mobile devices within each others' proximity over short-range links. As the state-of-the-art, we use Bluetooth technology for the short-range link communication in this cooperative scheme. In this paper, three topology based cooperative algorithms for the short-range link communication are designed. Then numerical results show that a power saving gain of over 50% can be achieved by cooperative networking of three mobile terminals in fully cooperating mode.

Currently available video traces for scalable encoded video with more than one layer are a convenient representation of the encoded video for the evaluation of networking mechanisms. The video distortion (RMSE) or quality (PSNR) for individual video frames in these traces, however, only allow for the calculation of the video quality of correctly received video frames; for lossy network transport, only a rough approximation can be made. With the availability of scalable offset distortion traces, which we introduce and evaluate in this paper, networking researchers are enabled to accurately calculate the video quality of scalable encoded video as it is perceived by the receiving client after lossy network transport.

We describe a low-complexity scheme for lossless compression of short text messages. The method uses arithmetic coding and a specific statistical context model for prediction of single symbols. Our particular contribution is a simple yet effective approach for storing highly complex statistics in a succinct yet effective data model that can easily be trained by text data. The proposed model already gives good compression rates with a RAM memory size of 128 kByte, thus making lossless data compression with statistical context modeling readily applicable to small devices like wireless sensors or mobile phones

This paper focuses on the header compression for wireless technologies with fixed link layer packet types. Exploiting specifics of packet transmission over air in this situation, a novel IP header compression scheme is developed. The novelty of the scheme consists in the unique CONTEXT repair mechanisms that ensured high robustness and at the same time does not degrade bandwidth savings. After the general concept of the novel scheme is introduced, it is illustrated by an example of Bluetooth technology. By means of extensive simulation study, we show the performance of the introduced approach. Simulation results verify the robustness of the novel scheme and its ability to deal with characteristics of the wireless channel.

In this paper we advocate exploiting channel descriptor information in packet data communication networks to gain transmission capacity. Besides the normal data transmission also the channel descriptor (or character of the channel) can be used to convey data. This novel access technique is suitable for wired as well as for wireless networks. By the example of a wireless spread spectrum system with pseudo-noise spreading sequences, we can report that a gain of nearly an order of magnitude in terms of capacity can be achieved compared to the standard spread spectrum transmission for a given scenario. Our approach is not limited to spread spectrum technologies, but applies to all systems with the property that the number of channel descriptors is larger than the actual number of simultaneously usable resources.

The goal of this paper is to highlight the benefits of using variable guard interval (VGI) in orthogonal frequency division multiplexing (OFDM) based wireless local area network (WLAN) systems. An algorithm is derived and its performance merit for implementing VGI in the presence of carrier frequency offset is provided. OFDM system needs to maintain orthogonality among sub carriers, which may be lost due insufficient guard interval and carrier frequency offset (due to local oscillator mismatch and Doppler effects). Static guard interval is usually kept large enough to tolerate worst case channel delay spread conditions, which in turn causes loss in efficiency of the system. The use of variable guard interval is expected to improve the system throughput. Since insufficient guard interval and carrier offset together cause orthogonality loss among sub carriers, the dynamic selection of guard interval in the presence of carrier frequency offset is considered in this work. Results are presented to analyze achievable gains. It has been shown that significant improvement in throughput is achievable by this scheme.

A novel multi rate orthogonal frequency division multiplexing (OFDM) system is proposed in this paper. It is expected to provide a flexible physical layer support to next generation wireless networks. It is mainly suited to inter carrier interference limited situations, when a large number of sub carriers are used within the available bandwidth constraint. The proposal is to alter the otherwise uniform performance of the sub carriers in an OFDM system by using different bandwidths for different subcarriers. This is advantageous in the situation where different users and data channels in a wireless network will have different data rates and quality of service requirements. Different sub carriers with different performance figures can be mapped to the data channels with matching requirements. This scheme can enhance the conventional OFDM systems to become flexible. It is shown that, using different sub carrier bandwidths can improve the throughput of some sub carriers at the cost of the others. Results show that overall system performance also improves with the proposed scheme even without any optimal mapping of data stream to the sub carriers for slightly higher frequency offsets.

In this paper we introduce a novel header compression technique for parallel channels as they are found in meshed networks. The approach introduced advocates the cooperative behavior of parallel channels to maintain the compression state on sender and receiver side. The general concept as well as one particular implementation are shown. Our approach is characterized by no need of a feedback channel and a low complexity of a compression strategy. By means of analytical study combined with our testbed results we show the performance of the introduced approach. The designing goals were to have a low complex, highly efficient and also robust header compression dealing with the characteristics of the wireless channel. As a first result we can show that for independent error pattern a number of three cooperative channels achieves both robustness and efficiency for a wide range of errors.

In this paper we advocate the use of multiple base stations for providing wireless link to an OFDM-based terminal and thus obtain macro diversity, in particular site diversity. The wireless link to a terminal is defined through a subset of sub-carriers that is optimized in a greedy manner over the union of base stations. The wireless terminal is unaware of how many and which base stations are providing its allocated set of sub-carrier, which simplifies the terminal design. We give an analytical comparison of our approach to the conventional solutions. The results show that the proposed schemes with multiple base stations can outperform the single base station case, while keeping the complexity of the wireless terminal unchanged. We further evaluate our schemes by considering minimize signalling over the air interface.

Video traces containing the sizes and (PSNR) qualities of the individual frames of a video encoding are a convenient video representation for the evaluation of video networking mechanisms. These video traces can be used to find the frame loss probabilities of a lossy networking mechanism, but can not give the PSNR video quality after lossy network transport. To date the video quality after lossy network transport could only be determined through experiments with actual video or by approximating the quality of the frames affected by a loss with some low PSNR quality. In this paper we introduce and evaluate offset distortion traces with which the video quality after lossy network transport can be accurately determined without requiring experiments with actual video. We explain how the offset distortion traces can be used by networking researchers without equipment or experience in video signal processing to accurately evaluate video networking mechanisms in terms of the PSNR video quality.

4G wireless communication networks are characterized by the need to support heterogenous terminals differing in size, display, battery, computational power, etc. For efficient usage of the wireless spectrum all devices should be served by the same spectrum instead of allocating spectra dedicated to the different terminal classes. This feature is naturally supported when the source coding of the traffic is done as a multiple description coding. The restoration quality of the information source is proportional to the quantity of the descriptors used in the restoration process. Hence, terminals with less capabilities may simply discard or not receive some of the descriptors, while high class terminals try to receive all information. In the case of partial reception of descriptors the performance can be improved by selection strategies for the descriptors. We advocate the usage of new descriptor selection schemes for multiple description coded services. The proposed schemes differ with respect to the availability of a feedback channel. All solutions are terminal oriented and are beneficial in the design of terminals that have robust and high quality services. Furthermore, our approaches inherently achieve fairness among different terminals. As an example of our results for video communication, we can show gains of 4 dB when the proposed approach is utilized in the system.

DVB-H is an upcoming technology for service distribution on mobile and wireless handhelds. For a huge market success three main key feature are important such as the services provided, the costs involved and the stand-by time as a level of personal freedom. In this paper we investigate the power saving potentials for DVB-H services. By means of simulation we have shown that for different video sequences power saving potentials between 17% and 22% compared to the original transmission power can be achieved. The proposed mechanism is fully standard compliant.

The dynamic selection of guard interval in orthogonal frequency division multiplexing (OFDM) based wireless local area network (WLAN) systems is described in this work. It derives an algorithm and provides its performance merit for implementing variable guard interval (VGI). The proposed system improves mean, outage and maximum throughput significantly as compared to standard OFDM system using fixed guard interval without sacrificing bit error rate performance.

Video services are expected to account for a large portion of the traffic in future wireless networks. Therefore, realistic traffic sources are needed to investigate the network performance of future communication protocols. Previously, we provided a publicly available library of frame size traces of long MPEG-4 and H.263 encoded videos in the QCIF format resulting in low bandwidth video streams. These traces can be used in 3G network simulations. Some future communication systems, such as WLAN systems, offer high data rates and therefore high quality video can be transmitted over such higher speed networks. We now present an addition to our existing trace library. For this addition we collected over 100 pre-encoded video sequences from the Web, generated the trace files, and conducted a thorough statistical evaluation. Because the pre-encoded video sequences are encoded by different users, their video settings differ in terms of codec, quality, format, and length. The advantage of user diversity for encoding is that it reflects very well the traffic situation in upcoming WLANs. Thus, the new traces are very suitable for the network performance evaluation of future WLANs.

We introduce error control algorithms for multicast delivery in 3G cellular networks. For efficiency reasons, the delivery of multicast flows is usually achieved by allocating a common channel in the forward direction. This enables multiple users to be served by a single physical resource. However, different users are affected by independent channel error processes and new techniques, different from plain ARQ, have to be found to perform error recovery. These new algorithms are needed to deliver the multicast flow in an efficient manner and to enable a reliable, performant and network operator inexpensive, multicast service. Different hybrid ARQ algorithms for the error recovery of multicast flows over common channels are proposed and their performance is evaluated both analytically and by simulation. The proposed solutions have been found to be effective and advantageous over plain ARQ techniques. Results on the achievable video quality are reported for the multicast video streaming case by considering the H.263 video coding format.

The success of 3G networks depends on the possibility to attract customers to this new technology. Therefore, new services using the spectrum in an efficient manner while satisfying the customers are needed. We introduce link layer algorithms that are suitable for multicast transmission in 3G cellular systems and we present their impact on video application in terms of play-out buffer requirements. By our approach we show that hybrid ARQ solutions can be successfully employed to perform error control in the multicast transmission case. Using these schemes, performance can be increased thereby increasing system capacity and lowering the cost per served user. In the final part of the paper, these solutions are extended for the important multicast video streaming case, where new schemes are devised to avoid the throughput inefficiencies of fully reliable error recovery algorithms.

Streaming of continuous media over wireless links is a notoriously difficult problem. This is due to the stringent quality of service (QoS) requirements of continuous media and the unreliability of wireless links. We develop a streaming protocol for the real-time delivery of prerecorded continuous media from (to) a central base station to (from) multiple wireless clients within a wireless cell. Our protocol prefetches parts of the ongoing continuous media streams into prefetch buffers in the clients (base station). Our protocol prefetches according to a join-the-shortest-queue (JSQ) policy. By exploiting rate adaptation techniques of wireless data packet protocols, the JSQ policy dynamically allocates more transmission capacity to streams with small prefetched reserves. Our protocol uses channel probing to handle the location-dependent, time-varying, and bursty errors of wireless links. We evaluate our prefetching protocol through extensive simulations with VBR MPEG and H.263 encoded video traces. Our simulations indicate that for bursty VBR video with an average rate of 64 kb/s and typical wireless communication conditions our prefetching protocol achieves client starvation probabilities on the order of 10/sup -4/ and a bandwidth efficiency of 90% with prefetch buffers of 128 kbytes.