1,097 research outputs found

    Modelling bus bunching along a common line corridor considering passenger arrival time and transfer choice under stochastic travel time

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    This study examines bus bunching along a common-line corridor, considering crucial factors underexplored in existing literature, such as stochastic travel times, passenger arrival patterns, and passenger transfer behaviours. We first develop a bus motion model that captures the interaction between bus trajectories and passenger movement. Then we formulate a reliability-based passenger arrival time choice and a transfer choice model to characterise passengers’ behaviours. Afterwards, the bus motion model and the passenger choice models are integrated, and a Method of Successive Averages type iterative algorithm is developed to obtain stable passenger arrival patterns and transfer choices. Numerical experiments are carried out on a hypothetical network followed by a case with real-world data. Our findings demonstrate that a high transfer demand could amplify the propagation of bus bunching across lines along the common-line corridor. Meanwhile, a 50% increase in transfer demand leads to a 24%–30% rise in headway fluctuation. Furthermore, our results suggest that non-uniform passenger accumulation patterns can restore headway regularity as a result of coordinated passenger movement and bus motions, thus alleviating the persistent deterioration in bus bunching

    Study on performance modeling and assurance of cross/permissionless/permissioned chains

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    This research addresses and resolves the performance modeling and assurance issues across the full spectrum of blockchain protocols, from permissionless (Chapter II) and permissioned (Chapter III) to cross-chain (Chapter IV). In Chapter II, a queueing model for permissionless blockchains and validations is proposed with respect to specific yet practical characteristics of the blockchains such as Bitcoin and Ethereum, primarily in terms of the block size and its waiting time. A set of variables considered in this model lists the network traffic intensity, the maximum number of transactions in a block, the block time, and the transaction arrival rate, to mention a few. Numerical simulations are conducted, and the efficacy of the proposed model is validated in a quantitative yet practical manner versus Bitcoin and Ethereum. In Chapter III, a set of queueing models for permissioned blockchain, which is considered an emerging technology for a trustworthy decentralized network, is proposed. Hyperledger Fabric is a well-defined permissioned blockchain. It is constructed by various types of nodes, such as the nodes for endorsement, ordering, and commitment, to realize the decentralized nature of trustworthy network operations. Each type of node is characterized in terms of transaction/block queue size and waiting time, and the transaction/block arrival rates and the transaction/block service rates are considered for simulation purposes. It is taken into account how the arrival rates and the service rates co-influence the performance and how the number of channels impact the performance in order to ultimately facilitate a more dynamic way of optimization. The efficacy of the proposed models is demonstrated by the extensive numerical simulations and analyses. In Chapter IV, a cross-chain communication protocol and a m/Cox/1 queueing model-based performance model are proposed. Cross-chain communication considers two distinct types of transactions, such as an atomic swap and an inter-ledger asset transfer. They are controlled by different types of communication mechanisms, namely, Hashed Time Lock Contract (HTLC) based on a pre-image-based technique, and inter-ledger asset transfer, based on an asynchronous verification technique. In the performance model, a Poisson arrival process is assumed, and the two services for pre-commit, verify and commit are assumed to be exponential distributions. Lastly, the selection ratio of a communication protocol between HTLC and the inter-ledger asset transfer is assumed. Extensive numerical simulations are conducted to study the performance impact of changing the parameters, such as arrival rate, service rate, and the ratio of communication protocol. In this research, the proposed models provide a comprehensive yet fundamental basis to assure and ultimately optimize the design of blockchain technology-based applications in specific terms of performance

    Causal Sampling, Compressing, and Channel Coding of Streaming Data

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    With the emergence of the Internet of Things, communication systems, such as those employed in distributed control and tracking scenarios, are becoming increasingly dynamic, interactive, and delay-sensitive. The data in such real-time systems arrive at the encoder progressively in a streaming fashion. An intriguing question is: what codes can transmit streaming data with both high reliability and low latency? Classical non-causal (block) encoding schemes can transmit data reliably but under the assumption that the encoder knows the entire data block before the transmission. While this is a realistic assumption in delay-tolerant systems, it is ill-suited to real-time systems due to the delay introduced by collecting data into a block. This thesis studies causal encoding: the encoder transmits information based on the causally received data while the data is still streaming in and immediately incorporates the newly received data into a continuing transmission on the fly. This thesis investigates causal encoding of streaming data in three scenarios: causal sampling, causal lossy compressing, and causal joint source-channel coding (JSCC). In the causal sampling scenario, a sampler observes a continuous-time source process and causally decides when to transmit real-valued samples of it under a constraint on the average number of samples per second; an estimator uses the causally received samples to approximate the source process in real time. We propose a causal sampling policy that achieves the best tradeoff between the sampling frequency and the end-to-end real-time estimation distortion for a class of continuous Markov processes. In the causal lossy compressing scenario, the sampling frequency constraint in the causal sampling scenario is replaced by a rate constraint on the average number of bits per second. We propose a causal code that achieves the best causal distortion-rate tradeoff for the same class of processes. In the causal JSCC scenario, the noiseless channel and the continuous-time process in the previous scenarios are replaced by a discrete memoryless channel with feedback and a sequence of streaming symbols, respectively. We propose a causal joint sourcechannel code that achieves the maximum exponentially decaying rate of the error probability compatible with a given rate. Remarkably, the fundamental limits in the causal lossy compressing and the causal JSCC scenarios achieved by our causal codes are no worse than those achieved by the best non-causal codes. In addition to deriving the fundamental limits and presenting the causal codes that achieve the limits, we also show that our codes apply to control systems, are resilient to system deficiencies such as channel delay and noise, and have low complexities.</p

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Model-Predictive Control in Communication Networks

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    This dissertation consists of 8 papers, separated into 3 groups. The first 3 papers show, how model-predictive control can be applied to queueing networks and contain a detailed proof of throughput optimality. Additionally, numerous network examples are discussed, and a connection between the stability properties of assembly queues and random walks on quotient spaces is established. The next two papers develop algorithms, with which robust forecasts of delay can be obtained in queueing networks. To that end, a notion of robustness is proposed, and the network control policy is designed to meet this goal. For the last 3 papers, focus is shifted towards Age-of-Information. Two main contributions are the derivation of the distribution of the Age-of-Information values in networks with clocked working cycles and an algorithm for the exact numerical evaluation of the Age-of-Information state-space in a similar set-up

    Private 5G and its Suitability for Industrial Networking

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    5G was and is still surrounded by many promises and buzzwords, such as the famous 1 ms, real-time, and Ultra-Reliable and Low-Latency Communications (URLLC). This was partly intended to get the attention of vertical industries to become new customers for mobile networks, which shall be deployed in their factories. With the allowance of federal agencies, companies deployed their own private 5G networks to test new use cases enabled by 5G. But what has been missing, apart from all the marketing, is the knowledge of what 5G can really do? Private 5G networks are envisioned to enable new use cases with strict latency requirements, such as robot control. This work has examined in great detail the capabilities of the current 5G Release 15 as private network, and in particular its suitability with regard to time-critical communications. For that, a testbed was designed to measure One-Way Delays (OWDs) and Round-Trip Times (RTTs) with high accuracy. The measurements were conducted in 5G Non-Standalone (NSA) and Standalone (SA) net-works and are the first published results. The evaluation revealed results that were not obvious or identified by previous work. For example, a strong impact of the packet rate on the resulting OWD and RTT was found. It was also found that typically 95% of the SA downlink end-to-end packet delays are in the range of 4 ms to 10 ms, indicating a fairly wide spread of packet delays, with the Inter-Packet Delay Variation (IPDV) between consecutive packets distributed in the millisecond range. Surprisingly, it also seems to matter for the RTT from which direction, i.e. Downlink (DL) or Uplink (UL), a round-trip communication was initiated. Another important factor plays especially the Inter-Arrival Time (IAT) of packets on the RTT distribution. These examples from the results found demonstrate the need to critically examine 5G and any successors in terms of their real-time capabilities. In addition to the end-to-end OWD and RTT, the delays caused by 4G and 5G Core processing has been investigated as well. Current state-of-the-art 4G and 5G Core implementations exhibit long-tailed delay distributions. To overcome such limitations, modern packet processing have been evaluated in terms of their respective tail-latency. The hardware-based solution was able to process packets with deterministic delay, but the software-based solutions also achieved soft real-time results. These results allow the selection of the right technology for use cases depending on their tail-latency requirements. In summary, many insights into the suitability of 5G for time-critical communications were gained from the study of the current 5G Release 15. The measurement framework, analysis methods, and results will inform the further development and refinement of private 5G campus networks for industrial use cases
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