Effective Medium Access Control for Underwater Acoustic Sensor Networks

This work is concerned with the design, analysis and development of effective Medium Access Control (MAC) protocols for Underwater Acoustic Sensor Networks (UASNs). The use of acoustic waves underwater places time-variant channel constraints on the functionality of MAC protocols. The contrast between traffic characteristics of the wide-ranging applications of UASNs makes it hard to design a single MAC protocol that can be adaptive to various applications. This thesis proposes MAC solutions that can meet the environmental and non-environmental challenges posed underwater. Scheduling-based schemes are the most common MAC solutions for UASNs, but scheduling is also challenging in such a dynamic environment. The preferable way of synchronisation underwater is the use of a global scheduler, guard intervals and exchange of timing signals. To this end, single-hop topologies suit UASN applications very well. The Combined Free and Demand Assignment Multiple Access (CFDAMA) is a centralised, scheduling-based MAC protocol demonstrating simplicity and adaptability to the time-variant channel and traffic characteristics. It is shown to minimise end-to-end delay, maximise channel utilisation and maintain fairness amongst nodes. This thesis primarily introduces two novel robust MAC solutions for UASNs, namely CFDAMA with Systematic Round Robin and CFDAMA without clock synchronisation (CFDAMA-NoClock). The former scheme is more suitable for large-scale and widely-spread UASNs, whereas the latter is a more feasible MAC solution when synchronisation amongst node clocks cannot be attained. Both analytical and comprehensive event-driven Riverbed simulations of underwater scenarios selected based on realistic sensor deployments show that the two protocols make it possible to load the channel up to higher levels of its capacity with controlled delay performance superior to that achievable with the traditional CFDAMA schemes. The new scheduling features make the CFDAMA-NoClock scheme a very feasible networking solution for robust and efficient UASN deployments in the real world

Analysis of MAC Strategies for Underwater Acoustic Networks

En esta tesis presentamos los protocolos MAC diseñados para redes acústicas subacuáticas, clasificándolos en amplias categorías, proporcionando técnicas de medición de rendimiento y análisis comparativo para seleccionar el mejor algoritmo MAC para aplicaciones específicas. Floor Acquisition Multiple Access (FAMA) es un protocolo MAC que se propuso para redes acústicas submarinas como medio para resolver los problemas de terminales ocultos y expuestos. Una versión modificada, Slotted FAMA, tenía como objetivo proporcionar ahorros de energía mediante el uso de ranuras de tiempo, eliminando así la necesidad de paquetes de control excesivamente largos en FAMA. Sin embargo, se ha observado que, debido al alto retraso de propagación en estas redes, el coste de perder un ACK es muy alto y tiene un impacto significativo en el rendimiento. Los mecanismos MultiACK y EarlyACK han sido analizados para el protocolo MACA, para mejorar su eficiencia. El mecanismo MultiACK aumenta la probabilidad de recibir al menos un paquete ACK al responder con un tren de paquetes ACK, mientras que el mecanismo EarlyACK evita la repetición de todo el ciclo de contención y transmisión de datos RTS / CTS enviando un ACK temprano. En esta investigación se presenta un análisis matemático de las dos variantes, los mecanismos MultiACK y EarlyACK, en Slotted FAMA. La investigación incluye las expresiones analíticas modificadas así como los resultados numéricos. Las simulaciones se llevaron a cabo utilizando ns-3. Los resultados han sido probados y validados utilizando Excel y MATLAB. La evaluación del rendimiento de S-FAMA con dos variantes mostró un factor de mejora del 65,05% en la probabilidad de recibir un ACK correctamente utilizando el mecanismo MultiACK y del 60,58% en la prevención de la repetición del ciclo completo, con EarlyACK. El impacto de este factor de mejora en el retardo, el tamaño del paquete de datos y el rendimiento también se analiza. La energía de transmisión desperdiciada y consumida en los mecanismos MultiACK y EarlyACK se analizan y comparan con S-FAMA. El rendimiento se ha evaluado, alcanzando una mejora en ambos casos, en comparación con S-FAMA. Estos mecanismos tendrán una utilidad práctica en caso de pérdida de ACK, al ahorrar energía y tiempo en períodos críticos. Fecha de lectura de Tesis Doctoral: 28 septiembre 2018.Esta tesis presenta una investigación sobre los protocolos MAC utilizados en la comunicación subacuática para explorar el mundo submarino. Los protocolos MAC ayudan en el acceso al medio compartido y la recopilación de datos de los océanos, para monitorizar el clima y la contaminación, la prevención de catástrofes, la navegación asistida, la vigilancia estratégica y la exploración de los recursos minerales. Esta investigación beneficiará a sectores como las industrias militares, de petróleo y gas, pesquerías, compañías de instrumentación subacuática, organismos de investigación, etc. El protocolo MAC afecta la vida útil de las redes inalámbricas de sensores. La eficiencia energética de las redes acústicas submarinas se ve gravemente afectada por las propiedades típicas de la propagación de las ondas acústicas. Los largos retrasos de propagación y las colisiones de paquetes de datos dificultan la transmisión de los paquetes de datos, que contienen información útil para que los usuarios realicen tareas de supervisión colectivas. El objetivo de este estudio es proponer nuevos mecanismos para protocolos MAC diseñados para funcionar en redes acústicas submarinas, con el propósito de mejorar su rendimiento. Para alcanzar ese objetivo es necesario realizar un análisis comparativo de los protocolos existentes. Lo que además sienta un procedimiento metodológicamente correcto para realizar esa comparación. Como la comunicación subacuática depende de ondas acústicas, en el diseño de los protocolos de MAC submarinos surgen varios desafíos como latencia prolongada, ancho de banda limitado, largas demoras en la propagación, grandes tasas de error de bit, pérdidas momentáneas en las conexiones, severo efecto multicamino y desvanecimientos. Los protocolos MAC terrestres, si se implementan directamente, funcionarán de manera ineficiente

Energy Efficient Reconfigurable MAC Protocol for Underwater Acoustic Sensor Network

In a multi-hop Underwater Acoustic Sensor Network~(UWASN) the challenges of the medium access control~(MAC) are different than that of single hop fully connected network. Existing MAC solutions try to solve the challenges of MAC control by channel reservation or contention elimination techniques. These techniques heavily depend on the transmissions of control packets that result in large overhead specially in terms of energy consumptions. In this thesis, a multi-hop enabled energy efficient MAC protocol for UWASN is proposed by exploiting a novel 2-phase contention resolution technique that minimizes the usage of control packets by utilizing short duration tones.~A probabilistic model of the proposed protocol is also developed to analyze the performance of the protocol analytically. A network simulation framework has been designed to simulate MAC and a physical layer for UWASN.~The proposed MAC protocol has been evaluated through quantitative analysis and simulation. By evaluating this proposed protocol through quantitative analysis and simulation, this research found that the proposed protocol outperforms in terms of energy efficiency, channel utilization and end-to-end delay. The proposed protocol achieves stable throughput and a maximum 30\% of theoretical maximum channel utilization in high traffic load in comparison to the existing protocol that becomes unstable and does not perform well. Additionally, the proposed protocol achieves better energy efficiency and lower end-to-end delay

Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation

El concepto de entorno inteligente concibe un mundo donde los diferentes tipos de dispositivos inteligentes colaboran para conseguir un objetivo común. En este concepto, inteligencia hace referencia a la habilidad de adquirir conocimiento y aplicarlo de forma autónoma para conseguir el objetivo común, mientras que entorno hace referencia al mundo físico que nos rodea. Por tanto, un entorno inteligente se puede definir como aquel que adquiere conocimiento de su entorno y aplicándolo permite mejorar la experiencia de sus habitantes. La computación ubicua o generalizada permitirá que este concepto de entorno inteligente se haga realidad. Normalmente, el término de computación ubicua hace referencia al uso de dispositivos distribuidos por el mundo físico, pequeños y de bajo precio, que pueden comunicarse entre ellos y resolver un problema de forma colaborativa. Cuando esta comunicación se lleva a cabo de forma inalámbrica, estos dispositivos forman una red de sensores inalámbrica o en inglés, Wireless Sensor Network (WSN). Estas redes están atrayendo cada vez más atención debido al amplio espectro de aplicaciones que tienen, des de soluciones para el ámbito militar hasta aplicaciones para el gran consumo. Esta tesis se centra en las redes de sensores inalámbricas y subacuáticas o en inglés, Underwater Wireless Sensor Networks (UWSN). Estas redes, a pesar de compartir los mismos principios que las WSN, tienen un medio de transmisión diferente que cambia su forma de comunicación de ondas de radio a ondas acústicas. Este cambio hace que ambas redes sean diferentes en muchos aspectos como el retardo de propagación, el ancho de banda disponible, el consumo de energía, etc. De hecho, las señales acústicas tienen una velocidad de propagación cinco órdenes de magnitud menor que las señales de radio. Por tanto, muchos algoritmos y protocolos necesitan adaptarse o incluso rediseñarse. Como el despliegue de este tipo de redes puede ser bastante complicado y caro, se debe planificar de forma precisa el hardware y los algoritmos que se necesitan. Con esta finalidad, las simulaciones pueden resultar una forma muy conveniente de probar todas las variables necesarias antes del despliegue de la aplicación. A pesar de eso, un nivel de precisión adecuado que permita extraer resultados y conclusiones confiables, solamente se puede conseguir utilizando modelos precisos y parámetros reales. Esta tesis propone un ecosistema para UWSN basado en herramientas libres y de código abierto. Este ecosistema se compone de un modelo de recolección de energía y unmodelo de unmódemde bajo coste y bajo consumo con un sistema de activación remota que, junto con otros modelos ya implementados en las herramientas, permite la realización de simulaciones precisas con datos ambientales del tiempo y de las condiciones marinas del lugar donde la aplicación objeto de estudio va a desplegarse. Seguidamente, este ecosistema se utiliza con éxito en el estudio y evaluación de diferentes protocolos de transmisión aplicados a una aplicación real de monitorización de una piscifactoría en la costa del mar Mediterráneo, que es parte de un proyecto de investigación español (CICYT CTM2011-2961-C02-01). Finalmente, utilizando el modelo de recolección de energía, esta plataforma de simulación se utiliza para medir los requisitos de energía de la aplicación y extraer las necesidades de hardware mínimas.Climent Bayarri, JS. (2014). Contribution to Research on Underwater Sensor Networks Architectures by Means of Simulation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3532

Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

D13.3 Overall assessment of selected techniques on energy- and bandwidth-efficient communications

Deliverable D13.3 del projecte europeu NEWCOM#The report presents the outcome of the Joint Research Activities (JRA) of WP1.3 in the last year of the Newcom# project. The activities focus on the investigation of bandwidth and energy efficient techniques for current and emerging wireless systems. The JRAs are categorized in three Tasks: (i) the first deals with techniques for power efficiency and minimization at the transceiver and network level; (ii) the second deals with the handling of interference by appropriate low interference transmission techniques; (iii) the third is concentrated on Radio Resource Management (RRM) and Interference Management (IM) in selected scenarios, including HetNets and multi-tier networks.Peer ReviewedPostprint (published version

Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical Programming: Modeling and Optimization

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation time, and departure time. Thus, the contribution of this dissertation is in the two areas of fairness and evacuation. The first part of the contribution of this dissertation is in the field of fairness. The objective in MMF is to allocate resources fairly among multiple demands given limited resources while utilizing the resources for higher efficiency. Fairness and efficiency are contradicting objectives, so they are translated into a bi-objective mathematical programming model and solved using the ϵ-constraint method, introduced by Vira and Haimes (1983). Although the solution is an approximation to the MMF, the model produces quality solutions, when ϵ is properly selected, in less computational time compared to the progressive-filling algorithm (PFA). In addition, a new algorithm is developed in this research called the θ progressive-filling algorithm that finds the MMF in resource allocation for general problems and works on problems with the nonconvex structure problems. The second part of the contribution is in evacuation modeling. The common dynamic evacuation models lack a piece of essential information for achieving fairness, which is the time each evacuee or group of evacuees spend in the network. Most evacuation models compute the total time for all evacuees to move from the endangered zone to the safe destination. Lack of information about the users of the transportation network is the motivation to develop a new optimization model that reports more information about the users of the network. The model finds the travel time, evacuation time, departure time, and the route selected for each group of evacuees. Given that the travel time function is a non-linear convex function of the traffic volume, the function is linearized through a piecewise linear approximation. The developed model is a mixed-integer linear programming (MILP) model with high complexity. Hence, the model is not capable of solving large scale problems. The complexity of the model was reduced by introducing a linear programming (LP) version of the full model. The complexity is significantly reduced while maintaining the exact output. In addition, the new θ-progressive-filling algorithm was implemented on the evacuation model to find a fair and efficient evacuation plan. The algorithm is also used to identify the optimal routes in the transportation network. Moreover, the robustness of the evacuation model was tested against demand uncertainty to observe the model behavior when the demand is uncertain. Finally, the robustness of the model is tested when the traffic flow is uncontrolled. In this case, the model's only decision is to distribute the evacuees on routes and has no control over the departure time

Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective

Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-MultipleAccess (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication