Connected Spatial Networks over Random Points and a Route-Length Statistic

We review mathematically tractable models for connected networks on random points in the plane, emphasizing the class of proximity graphs which deserves to be better known to applied probabilists and statisticians. We introduce and motivate a particular statistic $R$ measuring shortness of routes in a network. We illustrate, via Monte Carlo in part, the trade-off between normalized network length and $R$ in a one-parameter family of proximity graphs. How close this family comes to the optimal trade-off over all possible networks remains an intriguing open question. The paper is a write-up of a talk developed by the first author during 2007--2009.Comment: Published in at http://dx.doi.org/10.1214/10-STS335 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimal Base Station Placement: A Stochastic Method Using Interference Gradient In Downlink Case

In this paper, we study the optimal placement and optimal number of base stations added to an existing wireless data network through the interference gradient method. This proposed method considers a sub-region of the existing wireless data network, hereafter called region of interest. In this region, the provider wants to increase the network coverage and the users throughput. In this aim, the provider needs to determine the optimal number of base stations to be added and their optimal placement. The proposed approach is based on the Delaunay triangulation of the region of interest and the gradient descent method in each triangle to compute the minimum interference locations. We quantify the increase of coverage and throughput.Comment: This work has been presented in the 5th International ICST Conference on Performance Evaluation Methodologies and Tools (Valuetools 2011

Impact of Obstacles on the Degree of Mobile Ad Hoc Connection Graphs

What is the impact of obstacles on the graphs of connections between stations in Mobile Ad hoc Networks? In order to answer, at least partially, this question, the first step is to define both an environment with obstacles and a mobility model for the stations in such an environment. The present paper focuses on a new way of considering the mobility within environments with obstacles, while keeping the core ideas of the well-known Random WayPoint mobility model (a.k.a RWP). Based on a mesh-partitioning of the space, we propose a new model called RSP-O-G for which we compute the spatial distribution of stations and analyse how the presence of obstacles impacts this distribution compared to the distribution when no obstacles are present. Coupled with a simple model of radio propagation, and according to the density of stations in the environment, we study the mean degree of the connection graphs corresponding to such mobile ad hoc networks

Interference reduced routing for sensor networks

Construction of interference reduced routes is an all-important problem in sensor network. We propose a model for extracting a small size backbone network from a given background network. The extracted network possesses the property of reduced static interference. A backbone structure, constructed on the top of a planar sensor network can be used to route message with lower interference. We propose two centralized algorithms for constructing the backbone network. The first algorithm is based on the spanning tree construction of inner holes of sensor network. The second algorithm builds the backbone network by using the Delaunay triangulation of the center of gravity of holes in the network, which runs in O(n2) time. We also present a distributed localized implementation of the proposed algorithm by using the quasi Voronoi diagram and medial axis formed by the distribution of network holes. We describe an experimental investigation of the proposed algorithm. The results of the simulation show that the routing guided by the proposed backbone network is effective in reducing interference

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