5,383 research outputs found
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 Dynamics of Vehicular Networks in Urban Environments
Vehicular Ad hoc NETworks (VANETs) have emerged as a platform to support
intelligent inter-vehicle communication and improve traffic safety and
performance. The road-constrained, high mobility of vehicles, their unbounded
power source, and the emergence of roadside wireless infrastructures make
VANETs a challenging research topic. A key to the development of protocols for
inter-vehicle communication and services lies in the knowledge of the
topological characteristics of the VANET communication graph. This paper
explores the dynamics of VANETs in urban environments and investigates the
impact of these findings in the design of VANET routing protocols. Using both
real and realistic mobility traces, we study the networking shape of VANETs
under different transmission and market penetration ranges. Given that a number
of RSUs have to be deployed for disseminating information to vehicles in an
urban area, we also study their impact on vehicular connectivity. Through
extensive simulations we investigate the performance of VANET routing protocols
by exploiting the knowledge of VANET graphs analysis.Comment: Revised our testbed with even more realistic mobility traces. Used
the location of real Wi-Fi hotspots to simulate RSUs in our study. Used a
larger, real mobility trace set, from taxis in Shanghai. Examine the
implications of our findings in the design of VANET routing protocols by
implementing in ns-3 two routing protocols (GPCR & VADD). Updated the
bibliography section with new research work
A Scale-Free Topology Construction Model for Wireless Sensor Networks
A local-area and energy-efficient (LAEE) evolution model for wireless sensor
networks is proposed. The process of topology evolution is divided into two
phases. In the first phase, nodes are distributed randomly in a fixed region.
In the second phase, according to the spatial structure of wireless sensor
networks, topology evolution starts from the sink, grows with an
energy-efficient preferential attachment rule in the new node's local-area, and
stops until all nodes are connected into network. Both analysis and simulation
results show that the degree distribution of LAEE follows the power law. This
topology construction model has better tolerance against energy depletion or
random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure
k-connectivity of Random Graphs and Random Geometric Graphs in Node Fault Model
k-connectivity of random graphs is a fundamental property indicating
reliability of multi-hop wireless sensor networks (WSN). WSNs comprising of
sensor nodes with limited power resources are modeled by random graphs with
unreliable nodes, which is known as the node fault model. In this paper, we
investigate k-connectivity of random graphs in the node fault model by
evaluating the network breakdown probability, i.e., the disconnectivity
probability of random graphs after stochastic node removals. Using the notion
of a strongly typical set, we obtain universal asymptotic upper and lower
bounds of the network breakdown probability. The bounds are applicable both to
random graphs and to random geometric graphs. We then consider three
representative random graph ensembles: the Erdos-Renyi random graph as the
simplest case, the random intersection graph for WSNs with random key
predistribution schemes, and the random geometric graph as a model of WSNs
generated by random sensor node deployment. The bounds unveil the existence of
the phase transition of the network breakdown probability for those ensembles.Comment: 6 page
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
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
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