2,959 research outputs found
GCP: Gossip-based Code Propagation for Large-scale Mobile Wireless Sensor Networks
Wireless sensor networks (WSN) have recently received an increasing interest.
They are now expected to be deployed for long periods of time, thus requiring
software updates. Updating the software code automatically on a huge number of
sensors is a tremendous task, as ''by hand'' updates can obviously not be
considered, especially when all participating sensors are embedded on mobile
entities. In this paper, we investigate an approach to automatically update
software in mobile sensor-based application when no localization mechanism is
available. We leverage the peer-to-peer cooperation paradigm to achieve a good
trade-off between reliability and scalability of code propagation. More
specifically, we present the design and evaluation of GCP ({\emph Gossip-based
Code Propagation}), a distributed software update algorithm for mobile wireless
sensor networks. GCP relies on two different mechanisms (piggy-backing and
forwarding control) to improve significantly the load balance without
sacrificing on the propagation speed. We compare GCP against traditional
dissemination approaches. Simulation results based on both synthetic and
realistic workloads show that GCP achieves a good convergence speed while
balancing the load evenly between sensors
Mobile Conductance in Sparse Networks and Mobility-Connectivity Tradeoff
In this paper, our recently proposed mobile-conductance based analytical
framework is extended to the sparse settings, thus offering a unified tool for
analyzing information spreading in mobile networks. A penalty factor is
identified for information spreading in sparse networks as compared to the
connected scenario, which is then intuitively interpreted and verified by
simulations. With the analytical results obtained, the mobility-connectivity
tradeoff is quantitatively analyzed to determine how much mobility may be
exploited to make up for network connectivity deficiency.Comment: Accepted to ISIT 201
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
On the Role of Mobility for Multi-message Gossip
We consider information dissemination in a large -user wireless network in
which users wish to share a unique message with all other users. Each of
the users only has knowledge of its own contents and state information;
this corresponds to a one-sided push-only scenario. The goal is to disseminate
all messages efficiently, hopefully achieving an order-optimal spreading rate
over unicast wireless random networks. First, we show that a random-push
strategy -- where a user sends its own or a received packet at random -- is
order-wise suboptimal in a random geometric graph: specifically,
times slower than optimal spreading. It is known that this
gap can be closed if each user has "full" mobility, since this effectively
creates a complete graph. We instead consider velocity-constrained mobility
where at each time slot the user moves locally using a discrete random walk
with velocity that is much lower than full mobility. We propose a simple
two-stage dissemination strategy that alternates between individual message
flooding ("self promotion") and random gossiping. We prove that this scheme
achieves a close to optimal spreading rate (within only a logarithmic gap) as
long as the velocity is at least . The key
insight is that the mixing property introduced by the partial mobility helps
users to spread in space within a relatively short period compared to the
optimal spreading time, which macroscopically mimics message dissemination over
a complete graph.Comment: accepted to IEEE Transactions on Information Theory, 201
EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks
This paper describes EZ-AG, a structure-free protocol for duplicate
insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a
token that performs a self-repelling random walk in the network and aggregates
information from nodes when they are visited for the first time. A
self-repelling random walk of a token on a graph is one in which at each step,
the token moves to a neighbor that has been visited least often. While
self-repelling random walks visit all nodes in the network much faster than
plain random walks, they tend to slow down when most of the nodes are already
visited. In this paper, we show that a single step push phase at each node can
significantly speed up the aggregation and eliminate this slow down. By doing
so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of
overhead, EZ-AG outperforms existing structure-free data aggregation by a
factor of at least log(N) and achieves the lower bound for aggregation message
overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3
simulations in networks ranging from 100 to 4000 nodes under different mobility
models and node speeds. We also describe a hierarchical extension for EZ-AG
that can produce multi-resolution aggregates at each node using only O(NlogN)
messages, which is a poly-logarithmic factor improvement over existing
techniques
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