38 research outputs found
Betweenness centrality in dense random geometric networks
Random geometric networks consist of 1) a set of nodes embedded randomly in a
bounded domain and 2) links formed
probabilistically according to a function of mutual Euclidean separation. We
quantify how often all paths in the network characterisable as topologically
`shortest' contain a given node (betweenness centrality), deriving an
expression in terms of a known integral whenever 1) the network boundary is the
perimeter of a disk and 2) the network is extremely dense. Our method shows how
similar formulas can be obtained for any convex geometry. Numerical
corroboration is provided, as well as a discussion of our formula's potential
use for cluster head election and boundary detection in densely deployed
wireless ad hoc networks.Comment: 6 pages, 3 figure
BOUNDARY DETECTION ALGORITHMS IN WIRELESS SENSOR NETWORKS: A SURVEY
Wireless sensor networks (WSNs) comprise a large number of sensor nodes, which are spread out within a region and communicate using wireless links. In some WSN applications, recognizing boundary nodes is important for topology discovery, geographic routing and tracking. In this paper, we study the problem of recognizing the boundary nodes of a WSN. We firstly identify the factors that influence the design of algorithms for boundary detection. Then, we classify the existing work in boundary detection, which is vital for target tracking to detect when the targets enter or leave the sensor field
Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks
We propose two novel algorithms for distributed and location-free boundary
recognition in wireless sensor networks. Both approaches enable a node to
decide autonomously whether it is a boundary node, based solely on connectivity
information of a small neighborhood. This makes our algorithms highly
applicable for dynamic networks where nodes can move or become inoperative.
We compare our algorithms qualitatively and quantitatively with several
previous approaches. In extensive simulations, we consider various models and
scenarios. Although our algorithms use less information than most other
approaches, they produce significantly better results. They are very robust
against variations in node degree and do not rely on simplified assumptions of
the communication model. Moreover, they are much easier to implement on real
sensor nodes than most existing approaches.Comment: extended version of accepted submission to SEA 201
Literature Review on Hole Detection and Healing in Wireless Sensor Network
Abstract The emerging technology of wireless sensor network (WSN
Fine-grained boundary recognition in wireless ad hoc and sensor networks by topological methods
Location-free boundary recognition is crucial and critical for many fundamental network functionalities in wireless ad hoc and sensor networks. Previous designs, often coarse-grained, fail to accurately locate boundaries, especially when small holes exist. To address this issue, we propose a fine-grained boundary recognition approach using connectivity information only. This algorithm accurately discovers inner and outer boundary cycles without using location information. To the best of our knowledge, this is the first design being able to determinately locate all hole boundaries no matter how small the holes are. Also, this distributed algorithm does not rely on high node density. We formally prove the correctness of our design, and evaluate its effectiveness through extensive simulations. Categories and Subject Descriptor