Evaluation of Classifier Complexity for Delay Tolerant Network Routing

The growing popularity of small cost effective satellites (SmallSats, CubeSats, etc.) creates the potential for a variety of new science applications involving multiple nodes functioning together or independently to achieve a task, such as swarms and constellations. As this technology develops and is deployed for missions in Low Earth Orbit and beyond, the use of delay tolerant networking (DTN) techniques may improve communication capabilities within the network. In this paper, a network hierarchy is developed from heterogeneous networks of SmallSats, surface vehicles, relay satellites and ground stations which form an integrated network. There is a tradeoff between complexity, flexibility, and scalability of user defined schedules versus autonomous routing as the number of nodes in the network increases. To address these issues, this work proposes a machine learning classifier based on DTN routing metrics. A framework is developed which will allow for the use of several categories of machine learning algorithms (decision tree, random forest and deep learning) to be applied to a dataset of historical network statistics, which allows for the evaluation of algorithm complexity versus performance to be explored. We develop the emulation of a hierarchical network, consisting of tens of nodes which form a cognitive network architecture. CORE (Common Open Research Emulator) is used to emulate the network using bundle protocol and DTN IP neighbor discovery

耐障害仮想ネットワークの構築法に関する研究

Tohoku University加藤寧課

Trustworthiness Mechanisms for Long-Distance Networks in Internet of Things

Aquesta tesi té com a objectiu aconseguir un intercanvi de dades fiable en un entorn hostil millorant-ne la confiabilitat mitjançant el disseny d'un model complet que tingui en compte les diferents capes de confiabilitat i mitjançant la implementació de les contramesures associades al model. La tesi se centra en el cas d'ús del projecte SHETLAND-NET, amb l'objectiu de desplegar una arquitectura d'Internet de les coses (IoT) híbrida amb comunicacions LoRa i d'ona ionosfèrica d'incidència gairebé vertical (NVIS) per oferir un servei de telemetria per al monitoratge del “permafrost” a l'Antàrtida. Per complir els objectius de la tesi, en primer lloc, es fa una revisió de l'estat de l'art en confiabilitat per proposar una definició i l'abast del terme de confiança. Partint d'aquí, es dissenya un model de confiabilitat de quatre capes, on cada capa es caracteritza pel seu abast, mètrica per a la quantificació de la confiabilitat, contramesures per a la millora de la confiabilitat i les interdependències amb les altres capes. Aquest model permet el mesurament i l'avaluació de la confiabilitat del cas d'ús a l'Antàrtida. Donades les condicions hostils i les limitacions de la tecnologia utilitzada en aquest cas d’ús, es valida el model i s’avalua el servei de telemetria a través de simulacions en Riverbed Modeler. Per obtenir valors anticipats de la confiabilitat esperada, l'arquitectura proposada es modela per avaluar els resultats amb diferents configuracions previ al seu desplegament en proves de camp. L'arquitectura proposada passa per tres principals iteracions de millora de la confiabilitat. A la primera iteració, s'explora l'ús de mecanismes de consens i gestió de la confiança social per aprofitar la redundància de sensors. En la segona iteració, s’avalua l’ús de protocols de transport moderns per al cas d’ús antàrtic. L’última iteració d’aquesta tesi avalua l’ús d’una arquitectura de xarxa tolerant al retard (DTN) utilitzant el Bundle Protocol (BP) per millorar la confiabilitat del sistema. Finalment, es presenta una prova de concepte (PoC) amb maquinari real que es va desplegar a la campanya antàrtica 2021-2022, descrivint les proves de camp funcionals realitzades a l'Antàrtida i Catalunya.Esta tesis tiene como objetivo lograr un intercambio de datos confiable en un entorno hostil mejorando su confiabilidad mediante el diseño de un modelo completo que tenga en cuenta las diferentes capas de confiabilidad y mediante la implementación de las contramedidas asociadas al modelo. La tesis se centra en el caso de uso del proyecto SHETLAND-NET, con el objetivo de desplegar una arquitectura de Internet de las cosas (IoT) híbrida con comunicaciones LoRa y de onda ionosférica de incidencia casi vertical (NVIS) para ofrecer un servicio de telemetría para el monitoreo del “permafrost” en la Antártida. Para cumplir con los objetivos de la tesis, en primer lugar, se realiza una revisión del estado del arte en confiabilidad para proponer una definición y alcance del término confiabilidad. Partiendo de aquí, se diseña un modelo de confiabilidad de cuatro capas, donde cada capa se caracteriza por su alcance, métrica para la cuantificación de la confiabilidad, contramedidas para la mejora de la confiabilidad y las interdependencias con las otras capas. Este modelo permite la medición y evaluación de la confiabilidad del caso de uso en la Antártida. Dadas las condiciones hostiles y las limitaciones de la tecnología utilizada en este caso de uso, se valida el modelo y se evalúa el servicio de telemetría a través de simulaciones en Riverbed Modeler. Para obtener valores anticipados de la confiabilidad esperada, la arquitectura propuesta es modelada para evaluar los resultados con diferentes configuraciones previo a su despliegue en pruebas de campo. La arquitectura propuesta pasa por tres iteraciones principales de mejora de la confiabilidad. En la primera iteración, se explora el uso de mecanismos de consenso y gestión de la confianza social para aprovechar la redundancia de sensores. En la segunda iteración, se evalúa el uso de protocolos de transporte modernos para el caso de uso antártico. La última iteración de esta tesis evalúa el uso de una arquitectura de red tolerante al retardo (DTN) utilizando el Bundle Protocol (BP) para mejorar la confiabilidad del sistema. Finalmente, se presenta una prueba de concepto (PoC) con hardware real que se desplegó en la campaña antártica 2021-2022, describiendo las pruebas de campo funcionales realizadas en la Antártida y Cataluña.This thesis aims at achieving reliable data exchange over a harsh environment by improving its trustworthiness through the design of a complete model that takes into account the different layers of trustworthiness and through the implementation of the model’s associated countermeasures. The thesis focuses on the use case of the SHETLAND-NET project, aiming to deploy a hybrid Internet of Things (IoT) architecture with LoRa and Near Vertical Incidence Skywave (NVIS) communications to offer a telemetry service for permafrost monitoring in Antarctica. To accomplish the thesis objectives, first, a review of the state of the art in trustworthiness is carried out to propose a definition and scope of the trustworthiness term. From these, a four-layer trustworthiness model is designed, with each layer characterized by its scope, metric for trustworthiness accountability, countermeasures for trustworthiness improvement, and the interdependencies with the other layers. This model enables trustworthiness accountability and assessment of the Antarctic use case. Given the harsh conditions and the limitations of the use technology in this use case, the model is validated and the telemetry service is evaluated through simulations in Riverbed Modeler. To obtain anticipated values of the expected trustworthiness, the proposal has been modeled to evaluate the performance with different configurations prior to its deployment in the field. The proposed architecture goes through three major iterations of trustworthiness improvement. In the first iteration, using social trust management and consensus mechanisms is explored to take advantage of sensor redundancy. In the second iteration, the use of modern transport protocols is evaluated for the Antarctic use case. The final iteration of this thesis assesses using a Delay Tolerant Network (DTN) architecture using the Bundle Protocol (BP) to improve the system’s trustworthiness. Finally, a Proof of Concept (PoC) with real hardware that was deployed in the 2021-2022 Antarctic campaign is presented, describing the functional tests performed in Antarctica and Catalonia

A Message Transfer Framework for Enhanced Reliability in Delay-and Disruption-Tolerant Networks

Many infrastructure-less networks require quick, ad hoc deployment and the ability to deliver messages even if no instantaneous end-to-end path can be found. Such networks include large-scale disaster recovery networks, mobile sensor networks for ecological monitoring, ocean sensor networks, people networks, vehicular networks and projects for connectivity in developing regions such as TIER (Technology and Infrastructure for Emerging Regions). These types of networks can be realized with delay-and disruption-tolerant network (DTN) technology. Generally, messages in DTNs are transferred hop-by-hop toward the destination in an overlay above the transport layer called the ''bundle layer''. Unlike mobile ad hoc networks (MANETs), DTNs can tolerate disruption on end-to-end paths by taking advantage of temporal links emerging between nodes as nodes move in the network. Intermediate nodes store messages before forwarding opportunities become available. A series of encounters (i.e., coming within mutual transmission range) among different nodes will eventually deliver the message to the desired destination. The message delivery performance (such as delivery ratio and delay) in a DTN highly depends on time elapsed between encounters (inter-contact time) and the time two nodes remain in each others communication range once a contact is established (contact-duration). As messages are forwarded opportunistically among nodes, it is important to have sufficient contact opportunities in the network for faster, more reliable delivery of messages. In this thesis, we propose a simple yet efficient method for increasing DTN performance by increasing the contact duration of encountered nodes (i.e., mobile devices). Our proposed sticky transfer framework and protocol enable nodes in DTNs to collect neighbors' information, evaluate their movement patterns and amounts of data to transfer in order to make decisions of whether to ''stick'' with a neighbor to complete the necessary data transfers. Nodes intelligently negotiate sticky transfer parameters such as stick duration, mobility speed and movement directions based on user preferences and collected information. The sticky transfer framework can be combined with any DTN routing protocol to improve its performance. Our simulation results show that the proposed framework can improve the message delivery ratio by up to 38% and the end-to-end message transfer delay by up to 36%

Towards a Framework for DHT Distributed Computing

Distributed Hash Tables (DHTs) are protocols and frameworks used by peer-to-peer (P2P) systems. They are used as the organizational backbone for many P2P file-sharing systems due to their scalability, fault-tolerance, and load-balancing properties. These same properties are highly desirable in a distributed computing environment, especially one that wants to use heterogeneous components. We show that DHTs can be used not only as the framework to build a P2P file-sharing service, but as a P2P distributed computing platform. We propose creating a P2P distributed computing framework using distributed hash tables, based on our prototype system ChordReduce. This framework would make it simple and efficient for developers to create their own distributed computing applications. Unlike Hadoop and similar MapReduce frameworks, our framework can be used both in both the context of a datacenter or as part of a P2P computing platform. This opens up new possibilities for building platforms to distributed computing problems. One advantage our system will have is an autonomous load-balancing mechanism. Nodes will be able to independently acquire work from other nodes in the network, rather than sitting idle. More powerful nodes in the network will be able use the mechanism to acquire more work, exploiting the heterogeneity of the network. By utilizing the load-balancing algorithm, a datacenter could easily leverage additional P2P resources at runtime on an as needed basis. Our framework will allow MapReduce-like or distributed machine learning platforms to be easily deployed in a greater variety of contexts

Characterization and Applications of Temporal Random Walks on Opportunistic Networks

Opportunistic networks are a special case of DTN that exploit systematically the mobility of nodes. When nodes contacts occur, routing protocols can exploit them to forward messages. In the absence of stable end-to-end paths, spatio-temporal paths are created spontaneously. Opportunistic networks are suitable for communications in pervasive environments that are saturated by other devices. The ability to self-organize using the local interactions among nodes, added to mobility, leads to a shift from legacy packet-based communications towards a message-based communication paradigm. Usually, routing is done by means of message replication in order to increase the probability of message delivery. Instead, we study the useof Temporal Random Walks (TRW) on opportunistic networks as a simple method to deliver messages. TRW can adapt itself to the self-organizing evolution of opportunistic networks. A TRW can be seen as the passing of a token among nodes on the spatio-temporal paths. Since the token passing is an atomic operation, we can see it as forwarding one simple message among nodes. We study the drop ratio for message forwarding considering finite buffers. We then explore the idea of token-sharing as a routing mechanism. Instead of using contacts as mere opportunities to transfer messages, we use them to forward the token over time. The evolution of the token is ruled by the TRW process. Finally, we use the TRW to monitor opportunistic networks. We present the limits and convergence of monitoring the interact time between participating nodes

SNAP : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework for Emerging Wireless Application Systems

The evolution of Cyber-Physical Systems (CPSs) has given rise to an emergent class of CPSs defined by ad-hoc wireless connectivity, mobility, and resource constraints in computation, memory, communications, and battery power. These systems are expected to fulfill essential roles in critical infrastructure sectors. Vehicular Ad-Hoc Network (VANET) and a swarm of Unmanned Aerial Vehicles (UAV swarm) are examples of such systems. The significant utility of these systems, coupled with their economic viability, is a crucial indicator of their anticipated growth in the future. Typically, the tasks assigned to these systems have strict Quality-of-Service (QoS) requirements and require sensing, perception, and analysis of a substantial amount of data. To fulfill these QoS requirements, the system requires network connectivity, data dissemination, and data analysis methods that can operate well within a system\u27s limitations. Traditional Internet protocols and methods for network connectivity and data dissemination are typically designed for well-engineering cyber systems and do not comprehensively support this new breed of emerging systems. The imminent growth of these CPSs presents an opportunity to develop broadly applicable methods that can meet the stated system requirements for a diverse range of systems and integrate these systems with the Internet. These methods could potentially be standardized to achieve interoperability among various systems of the future. This work presents a solution that can fulfill the communication and data dissemination requirements of a broad class of emergent CPSs. The two main contributions of this work are the Application System (APPSYS) system abstraction, and a complementary communications framework called the Software-Defined NAmed-data enabled Publish-Subscribe (SNAP) communication framework. An APPSYS is a new breed of Internet application representing the mobile and resource-constrained CPSs supporting data-intensive and QoS-sensitive safety-critical tasks, referred to as the APPSYS\u27s mission. The functioning of the APPSYS is closely aligned with the needs of the mission. The standard APPSYS architecture is distributed and partitions the system into multiple clusters where each cluster is a hierarchical sub-network. The SNAP communication framework within the APPSYS utilized principles of Information-Centric Networking (ICN) through the publish-subscribe communication paradigm. It further extends the role of brokers within the publish-subscribe paradigm to create a distributed software-defined control plane. The SNAP framework leverages the APPSYS design characteristics to provide flexible and robust communication and dynamic and distributed control-plane decision-making that successfully allows the APPSYS to meet the communication requirements of data-oriented and QoS-sensitive missions. In this work, we present the design, implementation, and performance evaluation of an APPSYS through an exemplar UAV swarm APPSYS. We evaluate the benefits offered by the APPSYS design and the SNAP communication framework in meeting the dynamically changed requirements of a data-intensive and QoS-sensitive Coordinated Search and Tracking (CSAT) mission operating in a UAV swarm APPSYS on the battlefield. Results from the performance evaluation demonstrate that the UAV swarm APPSYS successfully monitors and mitigates network impairment impacting a mission\u27s QoS to support the mission\u27s QoS requirements

Application of Machine Learning Techniques to Delay Tolerant Network Routing

This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics