Route discovery with constant memory in oriented planar geometric networks

We address the problem of discovering routes in strongly connected planar geometric networks with directed links. Motivated by the necessity for establishing communication in wireless ad hoc networks in which the only information available to a vertex is its immediate neighborhood, we are considering routing algorithms that use the neighborhood information of a vertex for routing with constant memory only. We solve the problem for three types of directed planar geometric networks: Eulerian (in which every vertex has the same number of incoming and outgoing edges), Outerplanar (in which a single face contains all vertices of the network), and Strongly Face Connected, a new class of geometric networks that we define in the article, consisting of several faces, each face being a strongly connected outerplanar graph

Constant memory routing in quasi-planar and quasi-polyhedral graphs

AbstractWe address the problem of online route discovery for a class of graphs that can be embedded either in two or in three-dimensional space. In two dimensions we propose the class of quasi-planar graphs and in three dimensions the class of quasi-polyhedral graphs. In the former case such graphs are geometrically embedded in R2 and have an underlying backbone that is planar with convex faces; however within each face arbitrary edges (with arbitrary crossings) are allowed. In the latter case, these graphs are geometrically embedded in R3 and consist of a backbone of convex polyhedra and arbitrary edges within each polyhedron. In both cases we provide a routing algorithm that guarantees delivery. Our algorithms need only “remember” the source and destination nodes and one (respectively, two) reference nodes used to store information about the underlying face (respectively, polyhedron) currently being traversed. The existence of the backbone is used only in proofs of correctness of the routing algorithm; the particular choice is irrelevant and does not affect the behaviour of the algorithm

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Surveying Position Based Routing Protocols for Wireless Sensor and Ad-hoc Networks

A focus of the scientific community is to design network oriented position-based routing protocols and this has resulted in a very high number of algorithms, different in approach and performance and each suited only to particular applications. However, though numerous, very few position-based algorithms have actually been adopted for commercial purposes. This article is a survey of almost 50 position-based routing protocols and it comes as an aid in the implementation of this type of routing in various applications which may need to consider the advantages and pitfalls of position-based routing. An emphasis is made on geographic routing, whose notion is clarified as a more restrictive and more efficient type of position-based routing. The protocols are therefore divided into geographic and non-geographic routing protocols and each is characterized according to a number of network design issues and presented in a comparative manner from multiple points of view. The main requirements of current general applications are also studied and, depending on these, the survey proposes a number of protocols for use in particular application areas. This aims to help both researchers and potential users assess and choose the protocol best suited to their interest

Greedy routing and virtual coordinates for future networks

At the core of the Internet, routers are continuously struggling with ever-growing routing and forwarding tables. Although hardware advances do accommodate such a growth, we anticipate new requirements e.g. in data-oriented networking where each content piece has to be referenced instead of hosts, such that current approaches relying on global information will not be viable anymore, no matter the hardware progress. In this thesis, we investigate greedy routing methods that can achieve similar routing performance as today but use much less resources and which rely on local information only. To this end, we add specially crafted name spaces to the network in which virtual coordinates represent the addressable entities. Our scheme enables participating routers to make forwarding decisions using only neighbourhood information, as the overarching pseudo-geometric name space structure already organizes and incorporates "vicinity" at a global level. A first challenge to the application of greedy routing on virtual coordinates to future networks is that of "routing dead-ends" that are local minima due to the difficulty of consistent coordinates attribution. In this context, we propose a routing recovery scheme based on a multi-resolution embedding of the network in low-dimensional Euclidean spaces. The recovery is performed by routing greedily on a blurrier view of the network. The different network detail-levels are obtained though the embedding of clustering-levels of the graph. When compared with higher-dimensional embeddings of a given network, our method shows a significant diminution of routing failures for similar header and control-state sizes. A second challenge to the application of virtual coordinates and greedy routing to future networks is the support of "customer-provider" as well as "peering" relationships between participants, resulting in a differentiated services environment. Although an application of greedy routing within such a setting would combine two very common fields of today's networking literature, such a scenario has, surprisingly, not been studied so far. In this context we propose two approaches to address this scenario. In a first approach we implement a path-vector protocol similar to that of BGP on top of a greedy embedding of the network. This allows each node to build a spatial map associated with each of its neighbours indicating the accessible regions. Routing is then performed through the use of a decision-tree classifier taking the destination coordinates as input. When applied on a real-world dataset (the CAIDA 2004 AS graph) we demonstrate an up to 40% compression ratio of the routing control information at the network's core as well as a computationally efficient decision process comparable to methods such as binary trees and tries. In a second approach, we take inspiration from consensus-finding in social sciences and transform the three-dimensional distance data structure (where the third dimension encodes the service differentiation) into a two-dimensional matrix on which classical embedding tools can be used. This transformation is achieved by agreeing on a set of constraints on the inter-node distances guaranteeing an administratively-correct greedy routing. The computed distances are also enhanced to encode multipath support. We demonstrate a good greedy routing performance as well as an above 90% satisfaction of multipath constraints when relying on the non-embedded obtained distances on synthetic datasets. As various embeddings of the consensus distances do not fully exploit their multipath potential, the use of compression techniques such as transform coding to approximate the obtained distance allows for better routing performances

Position-based routing algorithms for three-dimensional ad hoc networks

In position-based routing algorithms, the nodes use the geographical information to make routing decisions. Recent research in this field addresses such routing algorithms in two-dimensional (2 D ) space. However, in real applications, the nodes may be distributed in three-dimensional (3 D ) space. Transition from 2 D to 3 D is not always easy, since many problems in 3 D are significantly harder than their 2 D counterparts. This dissertation focuses on providing a reliable and efficient position-based routing algorithms with the associated pre-processing algorithms for various 3 D ad hoc networks. In the first part of this thesis, we propose a generalization of the Yao graph where the cones used are adaptively centered on the nearest set of neighbors for each node, thus creating a directed or undirected spanning subgraph of a given unit disk graph (UDG). We show that these locally constructed spanning subgraphs are strongly connected, have bounded out-degree, are t -spanners with bounded stretch factor, contain the Euclidean minimum spanning tree as a subgraph, and are orientation-invariant. Then we propose the first local, constant time algorithm that constructs an independent dominating set and connected dominating set of a Unit Disk Graph in a 3 D environment. We present a truncated octahedral tiling system of the space to assign to each node a class number depending on the position of the node within the tiling system. Then, based on the tiling system, we present our local algorithms for constructing the dominating sets. The new algorithms have a constant time complexity and have approximation bounds that are completely independent of the size of the network. In the second part of this thesis, we implement 3 D versions of many current 2 D position-based routing algorithms in addition to creating many new algorithms that are specially designed for a 3 D environment. We show experimentally that these new routing algorithms can achieve nearly guaranteed delivery while discovering routes significantly closer in length to a shortest path. Because many existing position-based routing algorithms for ad hoc and sensor networks use the maximum transmission power of the nodes to discover neighbors, which is a very power-consuming process. We propose several localized power-aware 3 D position-based routing algorithms that increase the lifetime of a network by maximizing the average lifetime of its nodes. These new algorithms use the idea of replacing the constant transmission power of a node with an adjusted transmission power during two stages. The simulation results show a significant improvement in the overall network lifetime over the current power-aware routing algorithm

A cyber-physical system for dynamic building evacuation

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201

Stochastic cycle selection in active flow networks.

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.This is the accepted manuscript. It is currently embargoed pending publication