648 research outputs found
A Crosslayer Routing Protocol (XLRP) for Wireless Sensor Networks
The advent of wireless sensor networks with emphasis on the information being routed, rather than routing information has redefined networking from that of conventional wireless networked systems. Demanding that need for contnt based routing techniques and development of low cost network modules, built to operate in large numbers in a networked fashion with limited resources and capabilities. The unique characteristics of wireless sensor networks have the applicability and effectiveness of conventional algorithms defined for wireless ad-hoc networks, leading to the design and development of protocols specific to wireless sensor network. Many network layer protocols have been proposed for wireless sensor networks, identifying and addressing factors influencing network layer design, this thesis defines a cross layer routing protocol (XLRP) for sensor networks. The submitted work is suggestive of a network layer design with knowledge of application layer information and efficient utilization of physical layer capabilities onboard the sensor modules. Network layer decisions are made based on the quantity of information (size of the data) that needs to be routed and accordingly transmitter power leels are switched as an energy efficient routing strategy. The proposed routing protocol switches radio states based on the received signal strength (RSSI) acquiring only relevant information and piggybacks information in data packets for reduced controlled information exchange. The proposed algorithm has been implemented in Network Simulator (NS2) and the effectiveness of the protocol has been proved in comparison with diffusion paradigm
A priority-based multi-path routing protocol for sensor networks
Master'sMASTER OF ENGINEERIN
Physical parameter-aware Networks-on-Chip design
PhD ThesisNetworks-on-Chip (NoCs) have been proposed as a scalable, reliable
and power-efficient communication fabric for chip multiprocessors
(CMPs) and multiprocessor systems-on-chip (MPSoCs). NoCs determine
both the performance and the reliability of such systems, with a
significant power demand that is expected to increase due to developments
in both technology and architecture. In terms of architecture, an
important trend in many-core systems architecture is to increase the
number of cores on a chip while reducing their individual complexity.
This trend increases communication power relative to computation
power. Moreover, technology-wise, power-hungry wires are dominating
logic as power consumers as technology scales down. For these
reasons, the design of future very large scale integration (VLSI) systems
is moving from being computation-centric to communication-centric.
On the other hand, chip’s physical parameters integrity, especially
power and thermal integrity, is crucial for reliable VLSI systems. However,
guaranteeing this integrity is becoming increasingly difficult with
the higher scale of integration due to increased power density and operating
frequencies that result in continuously increasing temperature
and voltage drops in the chip. This is a challenge that may prevent
further shrinking of devices. Thus, tackling the challenge of power
and thermal integrity of future many-core systems at only one level
of abstraction, the chip and package design for example, is no longer
sufficient to ensure the integrity of physical parameters. New designtime
and run-time strategies may need to work together at different
levels of abstraction, such as package, application, network, to provide
the required physical parameter integrity for these large systems. This
necessitates strategies that work at the level of the on-chip network
with its rising power budget.
This thesis proposes models, techniques and architectures to improve
power and thermal integrity of Network-on-Chip (NoC)-based
many-core systems. The thesis is composed of two major parts: i)
minimization and modelling of power supply variations to improve
power integrity; and ii) dynamic thermal adaptation to improve thermal
integrity. This thesis makes four major contributions. The first is
a computational model of on-chip power supply variations in NoCs.
The proposed model embeds a power delivery model, an NoC activity
simulator and a power model. The model is verified with SPICE simulation
and employed to analyse power supply variations in synthetic
and real NoC workloads. Novel observations regarding power supply
noise correlation with different traffic patterns and routing algorithms
are found. The second is a new application mapping strategy aiming
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to minimize power supply noise in NoCs. This is achieved by defining
a new metric, switching activity density, and employing a force-based
objective function that results in minimizing switching density. Significant
reductions in power supply noise (PSN) are achieved with a low
energy penalty. This reduction in PSN also results in a better link timing
accuracy. The third contribution is a new dynamic thermal-adaptive
routing strategy to effectively diffuse heat from the NoC-based threedimensional
(3D) CMPs, using a dynamic programming (DP)-based distributed
control architecture. Moreover, a new approach for efficient extension
of two-dimensional (2D) partially-adaptive routing algorithms
to 3D is presented. This approach improves three-dimensional networkon-
chip (3D NoC) routing adaptivity while ensuring deadlock-freeness.
Finally, the proposed thermal-adaptive routing is implemented in
field-programmable gate array (FPGA), and implementation challenges,
for both thermal sensing and the dynamic control architecture are addressed.
The proposed routing implementation is evaluated in terms
of both functionality and performance.
The methodologies and architectures proposed in this thesis open a
new direction for improving the power and thermal integrity of future
NoC-based 2D and 3D many-core architectures
Cross-layer energy optimisation of routing protocols in wireless sensor networks
Recent technological developments in embedded systems have led to the emergence of a new class of networks, known asWireless Sensor Networks (WSNs), where individual nodes cooperate wirelessly with each other with the goal of sensing and interacting with the environment.Many routing protocols have been developed tomeet the unique and challenging characteristics of WSNs (notably very limited power resources to sustain an expected lifetime of perhaps
years, and the restricted computation, storage and communication capabilities of nodes that are nonetheless required to support large networks and diverse applications). No standards for routing have been developed yet for WSNs, nor has any protocol gained a dominant position among the research community.
Routing has a significant influence on the overall WSN lifetime, and providing an energy efficient routing protocol remains an open problem. This thesis addresses
the issue of designing WSN routing methods that feature energy efficiency. A common time reference across nodes is required in mostWSN applications. It is needed, for example, to time-stamp sensor samples and for duty cycling of nodes. Alsomany routing protocols require that nodes communicate according to some predefined schedule. However, independent distribution of the time information, without considering the routing algorithm schedule or network topology may lead to a failure of the synchronisation protocol. This was confirmed empirically, and was shown to result in loss of connectivity. This can be avoided by
integrating the synchronisation service into the network layer with a so-called cross-layer approach. This approach introduces interactions between the layers of a conventional layered network stack, so that the routing layer may share information with other layers. I explore whether energy efficiency can be enhanced through the use of cross-layer optimisations and present three novel cross-layer routing algorithms. The first protocol, designed for hierarchical, cluster based networks
and called CLEAR (Cross Layer Efficient Architecture for Routing), uses the routing algorithm to distribute time information which can be used for efficient duty cycling of nodes. The second method - called RISS (Routing Integrated
Synchronization Service) - integrates time synchronization into the network layer and is designed to work well in flat, non-hierarchical network topologies. The third method - called SCALE (Smart Clustering Adapted LEACH) - addresses
the influence of the intra-cluster topology on the energy dissipation of nodes. I also investigate the impact of the hop distance on network lifetime and propose a method of determining the optimal location of the relay node (the node through which data is routed in a two-hop network). I also address the problem of predicting the transition region (the zone separating the region where all packets
can be received and that where no data can be received) and I describe a way of preventing the forwarding of packets through relays belonging in this transition region.
I implemented and tested the performance of these solutions in simulations and also deployed these routing techniques on sensor nodes using TinyOS. I compared the average power consumption of the nodes and the precision of time synchronization with the corresponding parameters of a number of existing algorithms. All proposed schemes extend the network lifetime and due to their lightweight architecture they are very efficient on WSN nodes with constrained resources. Hence it is recommended that a cross-layer approach should be a feature of any routing algorithm for WSNs
Enabling knowledge-defined networks : deep reinforcement learning, graph neural networks and network analytics
Significant breakthroughs in the last decade in the Machine Learning (ML) field have ushered in a new era of Artificial Intelligence (AI). Particularly, recent advances in Deep Learning (DL) have enabled to develop a new breed of modeling and optimization tools with a plethora of applications in different fields like natural language processing, or computer vision.
In this context, the Knowledge-Defined Networking (KDN) paradigm highlights the lack of adoption of AI techniques in computer networks and – as a result – proposes a novel architecture that relies on Software-Defined Networking (SDN) and modern network analytics techniques to facilitate the deployment of ML-based solutions for efficient network operation.
This dissertation aims to be a step forward in the realization of Knowledge-Defined Networks. In particular, we focus on the application of AI techniques to control and optimize networks more efficiently and automatically. To this end, we identify two components within the KDN context whose development may be crucial to achieve self-operating networks in the future: (i) the automatic control module, and (ii) the network analytics platform.
The first part of this thesis is devoted to the construction of efficient automatic control modules. First, we explore the application of Deep Reinforcement Learning (DRL) algorithms to optimize the routing configuration in networks. DRL has recently demonstrated an outstanding capability to solve efficiently decision-making problems in other fields. However, first DRL-based attempts to optimize routing in networks have failed to achieve good results, often under-performing traditional heuristics. In contrast to previous DRL-based solutions, we propose a more elaborate network representation that facilitates DRL agents to learn efficient routing strategies. Our evaluation results show that DRL agents using the proposed representation achieve better performance and learn faster how to route traffic in an Optical Transport Network (OTN) use case. Second, we lay the foundations on the use of Graph Neural Networks (GNN) to build ML-based network optimization tools. GNNs are a newly proposed family of DL models specifically tailored to operate and generalize over graphs of variable size and structure. In this thesis, we posit that GNNs are well suited to model the relationships between different network elements inherently represented as graphs (e.g., topology, routing). Particularly, we use a custom GNN architecture to build a routing optimization solution that – unlike previous ML-based proposals – is able to generalize well to topologies, routing configurations, and traffic never seen during the training phase.
The second part of this thesis investigates the design of practical and efficient network analytics solutions in the KDN context. Network analytics tools are crucial to provide the control plane with a rich and timely view of the network state. However this is not a trivial task considering that all this information turns typically into big data in real-world networks. In this context, we analyze the main aspects that should be considered when measuring and classifying traffic in SDN (e.g., scalability, accuracy, cost). As a result, we propose a practical solution that produces flow-level measurement reports similar to those of NetFlow/IPFIX in traditional networks. The proposed system relies only on native features of OpenFlow – currently among the most established standards in SDN – and incorporates mechanisms to maintain efficiently flow-level statistics in commodity switches and report them asynchronously to the control plane. Additionally, a system that combines ML and Deep Packet Inspection (DPI) identifies the applications that generate each traffic flow.La evolución del campo del Aprendizaje Maquina (ML) en la última década ha dado lugar a una nueva era de la Inteligencia Artificial (AI). En concreto, algunos avances en el campo del Aprendizaje Profundo (DL) han permitido desarrollar nuevas herramientas de modelado y optimización con múltiples aplicaciones en campos como el procesado de lenguaje natural, o la visión artificial.
En este contexto, el paradigma de Redes Definidas por Conocimiento (KDN) destaca
la falta de adopción de técnicas de AI en redes y, como resultado, propone una nueva
arquitectura basada en Redes Definidas por Software (SDN) y en técnicas modernas de análisis de red para facilitar el despliegue de soluciones basadas en ML.
Esta tesis pretende representar un avance en la realización de redes basadas en KDN.
En particular, investiga la aplicación de técnicas de AI para operar las redes de forma más eficiente y automática. Para ello, identificamos dos componentes en el contexto de KDN cuyo desarrollo puede resultar esencial para conseguir redes operadas autónomamente en el futuro: (i) el módulo de control automático y (ii) la plataforma de análisis de red.
La primera parte de esta tesis aborda la construcción del módulo de control automático.
En primer lugar, se explora el uso de algoritmos de Aprendizaje Profundo por Refuerzo
(DRL) para optimizar el encaminamiento de tráfico en redes. DRL ha demostrado una
capacidad sobresaliente para resolver problemas de toma de decisiones en otros campos.
Sin embargo, los primeros trabajos que han aplicado DRL a la optimización del encaminamiento en redes no han conseguido rendimientos satisfactorios. Frente a dichas soluciones previas, proponemos una representación más elaborada de la red que facilita a los agentes DRL aprender estrategias de encaminamiento eficientes. Nuestra evaluación muestra que cuando los agentes DRL utilizan la representación propuesta logran mayor rendimiento y aprenden más rápido cómo encaminar el tráfico en un caso práctico en Redes de Transporte Ópticas (OTN). En segundo lugar, se presentan las bases sobre la utilización de Redes Neuronales de Grafos (GNN) para construir herramientas de optimización de red. Las GNN constituyen una nueva familia de modelos de DL específicamente diseñados para operar y generalizar sobre grafos de tamaño y estructura variables. Esta tesis destaca la idoneidad de las GNN para modelar las relaciones entre diferentes elementos de red que se representan intrínsecamente como grafos (p. ej., topología, encaminamiento). En particular, utilizamos una arquitectura GNN específicamente diseñada para optimizar el encaminamiento de tráfico que, a diferencia de las propuestas anteriores basadas en ML, es capaz de generalizar correctamente sobre topologías, configuraciones de encaminamiento y tráfico nunca vistos durante el entrenamiento La segunda parte de esta tesis investiga el diseño de herramientas de análisis de red eficientes en el contexto de KDN. El análisis de red resulta esencial para proporcionar al plano de control una visión completa y actualizada del estado de la red. No obstante, esto no es una tarea trivial considerando que esta información representa una cantidad masiva de datos en despliegues de red reales. Esta parte de la tesis analiza los principales aspectos a considerar a la hora de medir y clasificar el tráfico en SDN (p. ej., escalabilidad, exactitud, coste). Como resultado, se propone una solución práctica que genera informes de medidas de tráfico a nivel de flujo similares a los de NetFlow/IPFIX en redes tradicionales. El sistema propuesto utiliza sólo funciones soportadas por OpenFlow, actualmente uno de los estándares más consolidados en SDN, y permite mantener de forma eficiente estadísticas de tráfico en conmutadores con características básicas y enviarlas de forma asíncrona hacia el plano de control. Asimismo, un sistema que combina ML e Inspección Profunda de Paquetes (DPI) identifica las aplicaciones que generan cada flujo de tráfico.Postprint (published version
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