EFFICIENT GREEDY-FACE-GREEDY GEOGRAPHIC ROUTING PROTOCOLS IN MOBILE AD HOC AND SENSOR NETWORKS

This thesis describes and develops two planarization algorithms for geographic routing and a geographic routing protocol for mobile ad hoc and sensor networks. As all nodes are mobile and there is no fixed infrastructure, the design of routing protocols is one of the most challenging issues in mobile ad hoc and sensor networks. In recent years, greedyface- greedy (GFG) geographic routing protocols have been widely used, which need nodes to construct planar graphs as the underlying graphs for face routing. Two kinds of planarization algorithms have been developed, idealized and realistic planarization algorithms, respectively. The idealized planarization algorithms make the ideal assumption that the original network graph is a unit-disk graph (UDG). On the other hand, the realistic planarization algorithms do not need the original network to be a UDG. We propose an idealized planarization algorithm, which constructs an Edge Constrained Localized Delaunay graph (ECLDel). Compared to the existing planarized localized Delaunay graph [42], the construction of an ECLDel graph is far simpler, which reduces the communication cost and saves the network bandwidth. We propose a Pre-Processed Cross Link Detection Protocol (PPCLDP), which generates a planar spanning subgraph of the original network graph in realistic environments with obstacles. The proposed PPCLDP outperforms the existing Cross Link Detection Protocol [32] with much lower communication cost and better convergence time. In GFG routing protocols, greedy routing may fail at concave nodes, in which case, face routing is applied to recover from the greedy routing failure. This may cause extra hops in routing in networks containing voids. We propose a Hill-Area-Restricted (HAR) routing protocol, which avoids the extra hops taken in the original GFG routing. Compared to the existing Node Elevation Ad hoc Routing [4], the proposed HAR guarantees the packet delivery and decreases the communication cost greatly

MAP: Medial Axis Based Geometric Routing in Sensor Networks

One of the challenging tasks in the deployment of dense wireless networks (like sensor networks) is in devising a routing scheme for node to node communication. Important consideration includes scalability, routing complexity, the length of the communication paths and the load sharing of the routes. In this paper, we show that a compact and expressive abstraction of network connectivity by the medial axis enables efficient and localized routing. We propose MAP, a Medial Axis based naming and routing Protocol that does not require locations, makes routing decisions locally, and achieves good load balancing. In its preprocessing phase, MAP constructs the medial axis of the sensor field, defined as the set of nodes with at least two closest boundary nodes. The medial axis of the network captures both the complex geometry and non-trivial topology of the sensor field. It can be represented compactly by a graph whose size is comparable with the complexity of the geometric features (e.g., the number of holes). Each node is then given a name related to its position with respect to the medial axis. The routing scheme is derived through local decisions based on the names of the source and destination nodes and guarantees delivery with reasonable and natural routes. We show by both theoretical analysis and simulations that our medial axis based geometric routing scheme is scalable, produces short routes, achieves excellent load balancing, and is very robust to variations in the network model

The Four Principles of Geographic Routing

Geographic routing consists in using the position information of nodes to assist in the routing process, and has been a widely studied subject in sensor networks. One of the outstanding challenges facing geographic routing has been its applicability. Authors either make some broad assumptions on an idealized version of wireless networks which are often unverifiable, or they use costly methods to planarize the communication graph. The overarching questions that drive us are the following. When, and how should we use geographic routing? Is there a criterion to tell whether a communication network is fit for geographic routing? When exactly does geographic routing make sense? In this paper we formulate the four principles that define geographic routing and explore their topological consequences. Given a localized communication network, we then define and compute its geographic eccentricity, which measures its fitness for geographic routing. Finally we propose a distributed algorithm that either enables geographic routing on the network or proves that its geographic eccentricity is too high.Comment: This manuscript on geographic routing incoporates team feedback and expanded experiment

Planarisation de graphes dans les réseaux ad-hoc

We propose a radically new family of geometric graphs, i.e., Hypocomb, Reduced Hypocomb and Local Hypocomb. The first two are extracted from a complete graph; the last is extracted from a Unit Disk Graph (UDG). We analytically study their properties including connectivity, planarity and degree bound. All these graphs are connected (provided the original graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first strictly-localized, degree-bounded planar graph computed using merely 1-hop neighbor position information. We present a construction algorithm for these graphs and analyze its time complexity. Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results on their average degree and their impact on FACE routing. We discuss their potential applications and pinpoint some interesting open problems for future research

A Novel Family of Geometric Planar Graphs for Wireless Ad Hoc Networks

International audienceWe propose a radically new family of geometric graphs, i.e., Hypocomb, Reduced Hypocomb and Local Hypocomb. The first two are extracted from a complete graph; the last is extracted from a Unit Disk Graph (UDG). We analytically study their properties including connectivity, planarity and degree bound. All these graphs are connected (provided the original graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first strictly-localized, degree-bounded planar graph computed using merely 1-hop neighbor position information. We present a construction algorithm for these graphs and analyze its time complexity. Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results on their average degree and their impact on FACE routing. We discuss their potential applications and some open problems

Hypocomb: Bounded-degree Localized Geometric Planar Graphs for Wireless Ad Hoc Networks

International audienceWe propose a radically new family of geometric graphs, i.e., Hypocomb, Reduced Hypocomb and Local Hypocomb. T he first two are extracted from a complete graph; the last is extracted from a Unit Disk Graph (UDG). We analytically study their properties including connectivity, planarity and degree bound. All these graphs are connected (provided the original graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first strictly-localized, degree-bounded planar graph computed using merely I-hop neighbor position information. We present a construction algorithm for these graphs and analyze its time complexity. Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results on their average degree and their impact on FACE routing. We discuss their potential applications and some open problems for future research

The use of computational geometry techniques to resolve the issues of coverage and connectivity in wireless sensor networks

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.Web of Science2218art. no. 700