44,588 research outputs found
Not Always Sparse: Flooding Time in Partially Connected Mobile Ad Hoc Networks
In this paper we study mobile ad hoc wireless networks using the notion of
evolving connectivity graphs. In such systems, the connectivity changes over
time due to the intermittent contacts of mobile terminals. In particular, we
are interested in studying the expected flooding time when full connectivity
cannot be ensured at each point in time. Even in this case, due to finite
contact times durations, connected components may appear in the connectivity
graph. Hence, this represents the intermediate case between extreme cases of
fully mobile ad hoc networks and fully static ad hoc networks. By using a
generalization of edge-Markovian graphs, we extend the existing models based on
sparse scenarios to this intermediate case and calculate the expected flooding
time. We also propose bounds that have reduced computational complexity.
Finally, numerical results validate our models
Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks
Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067
Spatial Interference Cancelation for Mobile Ad Hoc Networks: Perfect CSI
Interference between nodes directly limits the capacity of mobile ad hoc
networks. This paper focuses on spatial interference cancelation with perfect
channel state information (CSI), and analyzes the corresponding network
capacity. Specifically, by using multiple antennas, zero-forcing beamforming is
applied at each receiver for canceling the strongest interferers. Given spatial
interference cancelation, the network transmission capacity is analyzed in this
paper, which is defined as the maximum transmitting node density under
constraints on outage and the signal-to-interference-noise ratio. Assuming the
Poisson distribution for the locations of network nodes and spatially i.i.d.
Rayleigh fading channels, mathematical tools from stochastic geometry are
applied for deriving scaling laws for transmission capacity. Specifically, for
small target outage probability, transmission capacity is proved to increase
following a power law, where the exponent is the inverse of the size of antenna
array or larger depending on the pass loss exponent. As shown by simulations,
spatial interference cancelation increases transmission capacity by an order of
magnitude or more even if only one extra antenna is added to each node.Comment: 6 pages; submitted to IEEE Globecom 200
On Space-Time Capacity Limits in Mobile and Delay Tolerant Networks
We investigate the fundamental capacity limits of space-time journeys of
information in mobile and Delay Tolerant Networks (DTNs), where information is
either transmitted or carried by mobile nodes, using store-carry-forward
routing. We define the capacity of a journey (i.e., a path in space and time,
from a source to a destination) as the maximum amount of data that can be
transferred from the source to the destination in the given journey. Combining
a stochastic model (conveying all possible journeys) and an analysis of the
durations of the nodes' encounters, we study the properties of journeys that
maximize the space-time information propagation capacity, in bit-meters per
second. More specifically, we provide theoretical lower and upper bounds on the
information propagation speed, as a function of the journey capacity. In the
particular case of random way-point-like models (i.e., when nodes move for a
distance of the order of the network domain size before changing direction), we
show that, for relatively large journey capacities, the information propagation
speed is of the same order as the mobile node speed. This implies that,
surprisingly, in sparse but large-scale mobile DTNs, the space-time information
propagation capacity in bit-meters per second remains proportional to the
mobile node speed and to the size of the transported data bundles, when the
bundles are relatively large. We also verify that all our analytical bounds are
accurate in several simulation scenarios.Comment: Part of this work will be presented in "On Space-Time Capacity Limits
in Mobile and Delay Tolerant Networks", P. Jacquet, B. Mans and G. Rodolakis,
IEEE Infocom, 201
Towards a Simple Relationship to Estimate the Capacity of Static and Mobile Wireless Networks
Extensive research has been done on studying the capacity of wireless
multi-hop networks. These efforts have led to many sophisticated and customized
analytical studies on the capacity of particular networks. While most of the
analyses are intellectually challenging, they lack universal properties that
can be extended to study the capacity of a different network. In this paper, we
sift through various capacity-impacting parameters and present a simple
relationship that can be used to estimate the capacity of both static and
mobile networks. Specifically, we show that the network capacity is determined
by the average number of simultaneous transmissions, the link capacity and the
average number of transmissions required to deliver a packet to its
destination. Our result is valid for both finite networks and asymptotically
infinite networks. We then use this result to explain and better understand the
insights of some existing results on the capacity of static networks, mobile
networks and hybrid networks and the multicast capacity. The capacity analysis
using the aforementioned relationship often becomes simpler. The relationship
can be used as a powerful tool to estimate the capacity of different networks.
Our work makes important contributions towards developing a generic methodology
for network capacity analysis that is applicable to a variety of different
scenarios.Comment: accepted to appear in IEEE Transactions on Wireless Communication
Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model
With the popularity of portable wireless devices it is important to model and
predict how information or contagions spread by natural human mobility -- for
understanding the spreading of deadly infectious diseases and for improving
delay tolerant communication schemes. Formally, we model this problem by
considering moving agents, where each agent initially carries a
\emph{distinct} bit of information. When two agents are at the same location or
in close proximity to one another, they share all their information with each
other. We would like to know the time it takes until all bits of information
reach all agents, called the \textit{flood time}, and how it depends on the way
agents move, the size and shape of the network and the number of agents moving
in the network.
We provide rigorous analysis for the \MRWP model (which takes paths with
minimum number of turns), a convenient model used previously to analyze mobile
agents, and find that with high probability the flood time is bounded by
, where agents move on an
grid. In addition to extensive simulations, we use a data set of
taxi trajectories to show that our method can successfully predict flood times
in both experimental settings and the real world.Comment: 10 pages, ACM SIGSPATIAL 2018, Seattle, U
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