Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions

We investigate three related and important problems connected to machine learning: approximating a submodular function everywhere, learning a submodular function (in a PAC-like setting [53]), and constrained minimization of submodular functions. We show that the complexity of all three problems depends on the 'curvature' of the submodular function, and provide lower and upper bounds that refine and improve previous results [3, 16, 18, 52]. Our proof techniques are fairly generic. We either use a black-box transformation of the function (for approximation and learning), or a transformation of algorithms to use an appropriate surrogate function (for minimization). Curiously, curvature has been known to influence approximations for submodular maximization [7, 55], but its effect on minimization, approximation and learning has hitherto been open. We complete this picture, and also support our theoretical claims by empirical results.Comment: 21 pages. A shorter version appeared in Advances of NIPS-201

Minimum energy broadcast on rectangular grid wireless networks

The minimum energy broadcast problem is to assign a transmission range to each node in an ad hoc wireless network to construct a spanning tree rooted at a given source node such that any non-root node resides within the transmission range of its parent. The objective is to minimize the total energy consumption, i.e., the sum of the δth powers of a transmission range (δ<1). In this paper, we consider the case that δ=2, and that nodes are located on a 2-dimensional rectangular grid. We prove that the minimum energy consumption for an n-node k×l-grid with n=kl and k≤l is at most nπ+O(n k0.68) and at least nπ+Ω(nk)-O(k). Our bounds close the previously known gap of upper and lower bounds for square grids. Moreover, our lower bound is n3-O(1) for 3≤k≤18, which matches a naive upper bound within a constant term for k≡0(mod3). © 2011 Elsevier B.V. All rights reserved

Energy Trade-offs for end-to-end Communications in Urban Vehicular Networks exploiting an Hyperfractal Model

International audienceWe present results on the trade-offs between the end-to-end communication delay and energy spent for completing a transmission in vehicular communications in urban settings. This study exploits our innovative model called " hyperfractal " that captures the self-similarity of the topology and vehicle locations in cities. We enrich the model by incorporating roadside infrastructure. We use analytical tools to derive theoretical bounds for the end-to-end communication hop count under two different energy constraints: either total accumulated energy, or maximum energy per node. More precisely, we prove that the hop count is bounded by O(n 1−α /(d m −1)) where α 2 is the precise hyperfractal dimension. This proves that for both constraints the energy decreases as we allow to chose among paths of larger length. In fact the asymptotic limit of the energy becomes significantly small when the number of nodes becomes asymptotically large. A lower bound on the network throughput capacity with constraints on path energy is also given. The results are confirmed through exhaustive simulations using different hyperfractal dimensions and path loss coefficients

Energy-efficient broadcasting with cooperative transmissions in wireless sensor networks

[[abstract]]Broadcasting is a method that allows the distributed nodes in a wireless sensor network to share its data efficiently among each other. Due to the limited energy supplies of a sensor node, energy efficiency has become a crucial issue in the design of broadcasting protocols. In this paper, we analyze the energy savings provided by a cooperative form of broadcast, called the Opportunistic Large Arrays (OLA), and compare it to the performance of conventional multi-hop networks where no cooperation is utilized for transmission. The cooperation in OLA allows the receivers to utilize for detection the accumulation of signal energy provided by the transmitters that are relaying the same symbol. In this work, we derive the optimal energy allocation policy that minimizes the total energy cost of the OLA network subject to the SNR (or BER) requirements at all receivers. Even though the cooperative broadcast protocol provides significant energy savings, we prove that the optimum assignment for cooperative networks is an NP-complete problem and, thus, requires high computational complexity in general. We then introduce several suboptimal yet scalable solutions and show the significant energy-savings that one can obtain even with the approximate solutions.[[fileno]]2030137030017[[department]]電機工程學

On the Approximability of Range Assignment Problems

We consider combinatorial optimization problems motivated by the following scenario. We are given a set of radio stations which can all send and receive data via wireless communication. Each radio station can be assigned an individual range up to which it transmits data. Given a certain connectivity requirement, the optimization task is to find a configuration of ranges (or range assignment) of minimal total power consumption providing the required network property. A problem of this kind is called range assignment problem. Three important problems of this type are examined in this thesis. First, we choose a quite abstract approach, allowing arbitrary distance functions without geometrical interpretation. We give the first thorough structural analysis of these problems in different setups. Our results identify easy cases as well as hard ones in terms of complexity as well as various levels of approximability for the individual problems. They also reveal interesting differences between the three problems themselves. We then turn to geometrical instances, on which there already exists a line of research regarding complexity and approximability in the literature. We contribute to this research by designing new reductions which are more simple and versatile than the ones used before, and produce new and better results. Using our reductions we can solve open problems posed in prior work. In the last chapter, we turn to approximation algorithms. We give a tight analysis of a well-known approximation algorithm for two of the problems as a function of the input size. A thorough analysis of two natural greedy paradigms is given, with tight results in the general and many special cases. We conclude with the design and analysis of a new approximation algorithm for one problem, and identify the first approximation scheme for some special geometric instances

Swarm intelligence techniques for optimization and management tasks insensor networks

The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted in each case. We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state, where communications are not possible and energy consumption is low; and the active state, where communication result in a higher energy consumption. In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net). The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too close in time, groups of males that are located nearby each other desynchronize their calls. Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and show its performance on a larger set of benchmark instances. The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm, which also compares quite favorably against centralized heuristics from the literature.Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales. En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado. Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net). La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos. Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores. El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas. Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas