Cross-layer design of multi-hop wireless networks

MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

深層強化学習による東京湾フェリーの避航方法について

東京海洋大学修士学位論文 2022年度(2022年9月) 海運ロジスティクス 修士 第3881号指導教員: 田丸人意全文公表年月日: 2022-12-22東京海洋大学202

Napredna tehnička rješenja za kontrolu onečišćenja pomorskog sektora u Jadranskom moru

The major environmental problems within the maritime sector are atmospheric pollution due to the extensive use of fossil fuels in ship power systems, as well as seawater pollution from various sources (e.g. oil spills, microplastic, acidification, etc.). Due to their negative effect on the environment, human health and marine ecosystem, they should be carefully controlled. The studies on environmental problems of the maritime sector are more focused on atmospheric pollution, mainly thanks to the Paris Agreement. The ships are mostly powered by conventional power systems (diesel engines), and their negative effect on the environment would be lower with the implementation of some measures for emission reduction. In this paper, the outcomes of the research into the advanced technical measures for maritime pollution control are summarized, where the emphasis is put on ro-ro passenger ships engaged in the Croatian short-sea shipping sector. The results of the performed Life-Cycle Assessment (LCA) and Life-Cycle Cost Assessment (LCCA) suggested that conventional power systems should be modernized by ship electrification. This solution is represented as the most cost-effective and the most environmentally friendly solution regarding air pollution reduction. Furthermore, regarding seawater protection, several aspects of the advanced early warning system, that is being developed by the authors, are discussed. Finally, in line with the global trends in the maritime sector, a review on increasing the degree of autonomy of ships is given, as one of the most important topics for the near future.Glavni ekološki problemi u pomorskom sektoru su onečišćenje atmosfere zbog upotrebe fosilnih goriva u brodskom energetskom sustavu te onečišćenje morske vode iz različitih izvora (npr. izlijevanje nafte, mikroplastika, zakiseljavanje itd.). Zbog njihovog negativnog utjecaja na okoliš, ljudsko zdravlje i morski ekosustav, treba ih pažljivo kontrolirati. Studije o ekološkim problemima pomorskog sektora više su usredotočene na onečišćenje atmosfere, što je uglavnom posljedica Pariškog sporazuma prema kojem bi svaki sektor trebao doprinijeti smanjenju stakleničkih plinova. Brodovi su uglavnom pogonjeni konvencionalnim energetskim sustavima s dizelskih motorima, čiji bi negativan učinak na okoliš bio manji provedbom nekih mjera za smanjenje emisija. U ovom radu prikazana su istraživanja o naprednim tehničkim mjerama za kontrolu pomorskog onečišćenja, a rezultati su ilustrirani na ro-ro putničkim brodovima hrvatske priobalne plovidbe. Analize cjeloživotnih emisija i troškova potiču modernizaciju konvencionalnih energetskih sustava elektrifikacijom broda. Ovo je rješenje predstavljeno kao najisplativije i ekološki najprihvatljivije. Uz spomenute mehanizme za smanjenje onečišćenja zraka, u radu je dan osvrt na određena rješenja za nadzor i očuvanje morske vode s naglaskom na sustav ranog upozoravanja razvijen od strane autora u okviru kompetitivnog međunarodnog projekta. Naposljetku, u skladu sa svjetskim trendovima u pomorskom sektoru, dan je osvrt na povećanje stupnja autonomnosti brodova, kao jedne od važnijih tema u bližoj budućnosti

Path planning and collision avoidance for autonomous surface vehicles I: a review

Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

A Review on Applications of Machine Learning in Shipping Sustainability

The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges

Satisfaction of Istanbul Citizens with Urban Public Transportation

Transportation is one of the most challenging urban subsystems to transform in an environmentally friendly and futuristic way. Many city dwellers use a variety of modes of transportation. An efficient and sustainable urban transportation system must include many modes of transportation for a single trip. Intermodal combinations are essential for urban transportation efficiency. Public transportation and commuting are essential elements of multimodal travel. In urban areas, a mix of bicycles, vehicles, and public transportation is prevalent, while in rural areas, car, and public transportation are more prevalent. By examining the characteristics that lead customers to prefer water transportation over Metrobus and Marmaray, we hope to gain a better understanding of how the Asian and European sides of Istanbul are traversed. The number of participants in the "Maritime Transportation Satisfaction Survey" was 2,343. During this period, a model was built using the survey item "frequency of use" (dependent variable). Numerous survey examines and evaluation methodologies were utilized to determine the effectiveness of this strategy. The study examines the intermodal travel motivations and the evaluation of transportation options by multimodal users. For a successful urban transportation system, urban planning must take into account multimodal travel behavior and user expectations. There are initiatives to improve water transportation in Istanbul. Conventional maritime transportation is inadequate from start to finish. An integrated route optimization method is needed to increase the efficiency of maritime transportation. We believe that by strengthening maritime transportation links will increase water consumption. Before the coronavirus pandemic, 2,343 maritime carriers were evaluated on March 8, 2020. (Different surveys were conducted among the passengers of City Lines, Private Motors, Metrobus, and Marmaray to compare their choices and reasons.) SPSS will be used for data analysis. Multivariate Statistical Analysis relies on Multinomial Logistic Regression and Discriminant Analysis models, both of which use the K-fold and Leave-one-out criteria to decide which attributes are valid in the regression model and which are valid in the discriminant approach. The Hosmer-Lemeshow test criteria yielded a p-value greater than 0.05 for MLR characteristics

Controlling the mobility and enhancing the performance of multiple message ferries in delay tolerant networks

In einem drahtlosen Netzwerk mit isolierten und stationären Knoten können Adhoc und verzögerungstolerante Netzwerk Routing-Protokolle nicht verwendet werden. Message Ferry Netzwerke sind die Lösung für diese Fälle, in denen ein (oder mehrere) Message Ferry Knoten den store-carry-forward Mechanismus verwendet und zwischen den Knoten reist, um Nachrichten auszutauschen. In diesem Fall erfahren die Nachrichten für gewöhnlich eine lange Verzögerung. Um die Performance der Message Ferry Netzwerke zu verbessern, kann die Mobilität der Message Ferry Knoten gesteuert werden. In dieser Doktorarbeit werden zwei Strategien zur Steuerung der Mobilität der Message Ferry Knoten studiert. Die Strategien sind das on-the-fly Entscheidungsverfahren in Ferry Knoten und die offline Wegplanung für Ferry Knoten. Für die on-the-fly Strategie untersucht diese Arbeit Decision-maker in Ferry Knoten, der die Entscheidung auf Grundlage der lokalen Observation eines Ferry Knoten trifft. Zur Koordinierung mehrerer Ferry Knoten, die keine globale Kenntnis über das Netzwerk haben, wird eine indirekte Signalisierung zwischen Ferry Knoten vorgeschlagen. Zur Kooperation der Ferry Knoten für die Zustellung der Nachrichten werden einige Ansätze zum Nachrichtenaustausch zwischen Ferry Knoten vorgeschlagen, in denen der Decision-maker eines Ferry Knotens seine Information mit dem verzögerungstoleranten Router des Ferry Knoten teilt, um die Effizienz des Nachrichtenaustauschs zwischen Ferry Knoten zu verbessern. Umfangreiche Simulationsstudien werden zur Untersuchung der vorgeschlagenen Ansätze und des Einflusses verschiedener Nachrichtenverkehrsszenarien vorgenommen. Außerdem werden verschiedene Szenarien mit unterschiedlicher Anzahl von Ferry Knoten, verschiedener Geschwindigkeit der Ferry Knoten und verschiedener Ansätze zum Nachrichtenaustausch zwischen Ferry Knoten studiert. Zur Evaluierung der offline Wegplanungsstrategie wird das Problem als Multiple Traveling Salesmen Problem (mTSP) modelliert und ein genetischer Algorithmus zur Approximation der Lösung verwendet. Es werden verschiedene Netzwerkarchitekturen zur Pfadplanung der Ferry Knoten vorgestellt und studiert. Schließlich werden die Strategien zur Steuerung der Mobilität der Ferry Knoten verglichen. Die Ergebnisse zeigen, dass die Performance der Strategien in Bezug auf die Ende-zu-Ende-Verzögerung von dem Szenario des Nachrichtenverkehrs abhängt. In Szenarien, wie Nachrichtenverkehr in Sensor-Netzwerken, in denen ein Knoten die Nachrichten zu allen anderen Knoten sendet oder von allen anderen Knoten empfängt, zeigt die offline Wegplanung, basierend auf der mTSP Lösung, bessere Performance als die on-the-fly Strategie. Andererseits ist die on-the-fly Stratgie eine bessere Wahl in Szenarien wie Nachrichtenaustausch zwischen Rettungskräften während einer Katastrophe, in denen alle drahtlose Knoten die Nachrichten austauschen müssen. Zudem ist die on-the-fly Strategie flexibler, robuster als offline Wegplanung und benötigt keine Initialisierungszeit.In a wireless network with isolated and stationary nodes, ad hoc and delay tolerant routing approaches fail to deliver messages. Message ferry networks are the solution for such networks where one or multiple mobile nodes, i.e. message ferry, apply the store-carry-forward mechanism and travel between nodes to exchange their messages. Messages usually experience a long delivery delay in this type of network. To improve the performance of message ferry networks, the mobility of ferries can be controlled. In this thesis, two main strategies to control mobility of multiple message ferries are studied. The strategies are the on-the-fly mobility decision making in ferries and the offline path planning for ferries. To apply the on-the-fly strategy, this work proposes a decision maker in ferries which makes mobility decisions based on the local observations of ferries. To coordinate multiple ferries, which have no global view from the network, an indirect signaling of ferries is proposed. For cooperation of ferries in message delivery, message forwarding and replication schemes are proposed where the mobility decision maker shares its information with the delay tolerant router of ferries to improve the efficiency of message exchange between ferries. An extensive simulation study is performed to investigate the performance of the proposed schemes and the impact of different traffic scenarios in a network. Moreover, different scenarios with different number of ferries, different speed of ferries and different message exchange approaches between ferries are studied. To study the offline path planning strategy, the problem is modeled as multiple traveling salesmen problem (mTSP) and a genetic algorithm is applied to approximate the solution. Different network architectures are proposed and studied where the path of ferries are planned in advance. Finally, the strategies to control the mobility of ferries are compared. The results show that the performance of each strategy, in terms of the average end-to-end delay of messages, depends on the traffic scenario in a network. In traffic scenarios same as the traffic in sensor networks, where only a single node generates messages to all nodes or receives messages from all node, the offline path planning based on mTSP solution performs better than the on-the-fly decision making. On the other hand, in traffic scenarios same as the traffic in disaster scenarios, where all nodes in a network may send and receive messages, the on-the-fly decision making provides a better performance. Moreover, the on-thy-fly decision making is always more flexible, more robust and does not need any initialization time

Machine learning in sustainable ship design and operation: a review

The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building a ship, through optimisation of ship operations. In recent years, ship efficiency improvements using Machine Learning (ML) methods are quickly progressing, facilitated by available data from remote sensing, experiments and high-fidelity simulations. The data have been successfully applied to extract intricate empirical rules that can reduce emissions thereby helping achieve green shipping. This article presents an overview of applying ML techniques to enhance ships’ sustainability. The work covers the ML fundamentals and applications in relevant areas: ship design, operational performance, and voyage planning. Suitable ML approaches are analysed and compared on a scenario basis, with their space for improvements also discussed. Meanwhile, a reminder is given that ML has many inherent uncertainties and hence should be used with caution