10,324 research outputs found
On Distributed Computation in Noisy Random Planar Networks
We consider distributed computation of functions of distributed data in
random planar networks with noisy wireless links. We present a new algorithm
for computation of the maximum value which is order optimal in the number of
transmissions and computation time.We also adapt the histogram computation
algorithm of Ying et al to make the histogram computation time optimal.Comment: 5 pages, 2 figure
Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation
Sensor networks potentially feature large numbers of nodes that can sense
their environment over time, communicate with each other over a wireless
network, and process information. They differ from data networks in that the
network as a whole may be designed for a specific application. We study the
theoretical foundations of such large scale sensor networks, addressing four
fundamental issues- connectivity, capacity, clocks and function computation.
To begin with, a sensor network must be connected so that information can
indeed be exchanged between nodes. The connectivity graph of an ad-hoc network
is modeled as a random graph and the critical range for asymptotic connectivity
is determined, as well as the critical number of neighbors that a node needs to
connect to. Next, given connectivity, we address the issue of how much data can
be transported over the sensor network. We present fundamental bounds on
capacity under several models, as well as architectural implications for how
wireless communication should be organized.
Temporal information is important both for the applications of sensor
networks as well as their operation.We present fundamental bounds on the
synchronizability of clocks in networks, and also present and analyze
algorithms for clock synchronization. Finally we turn to the issue of gathering
relevant information, that sensor networks are designed to do. One needs to
study optimal strategies for in-network aggregation of data, in order to
reliably compute a composite function of sensor measurements, as well as the
complexity of doing so. We address the issue of how such computation can be
performed efficiently in a sensor network and the algorithms for doing so, for
some classes of functions.Comment: 10 pages, 3 figures, Submitted to the Proceedings of the IEE
Latency Optimal Broadcasting in Noisy Wireless Mesh Networks
In this paper, we adopt a new noisy wireless network model introduced very
recently by Censor-Hillel et al. in [ACM PODC 2017, CHHZ17]. More specifically,
for a given noise parameter any sender has a probability of
of transmitting noise or any receiver of a single transmission in its
neighborhood has a probability of receiving noise.
In this paper, we first propose a new asymptotically latency-optimal
approximation algorithm (under faultless model) that can complete
single-message broadcasting task in time units/rounds in any
WMN of size and diameter . We then show this diameter-linear
broadcasting algorithm remains robust under the noisy wireless network model
and also improves the currently best known result in CHHZ17 by a
factor.
In this paper, we also further extend our robust single-message broadcasting
algorithm to multi-message broadcasting scenario and show it can broadcast
messages in time rounds. This new robust
multi-message broadcasting scheme is not only asymptotically optimal but also
answers affirmatively the problem left open in CHHZ17 on the existence of an
algorithm that is robust to sender and receiver faults and can broadcast
messages in time rounds.Comment: arXiv admin note: text overlap with arXiv:1705.07369 by other author
Broadcasting in Noisy Radio Networks
The widely-studied radio network model [Chlamtac and Kutten, 1985] is a
graph-based description that captures the inherent impact of collisions in
wireless communication. In this model, the strong assumption is made that node
receives a message from a neighbor if and only if exactly one of its
neighbors broadcasts.
We relax this assumption by introducing a new noisy radio network model in
which random faults occur at senders or receivers. Specifically, for a constant
noise parameter , either every sender has probability of
transmitting noise or every receiver of a single transmission in its
neighborhood has probability of receiving noise.
We first study single-message broadcast algorithms in noisy radio networks
and show that the Decay algorithm [Bar-Yehuda et al., 1992] remains robust in
the noisy model while the diameter-linear algorithm of Gasieniec et al., 2007
does not. We give a modified version of the algorithm of Gasieniec et al., 2007
that is robust to sender and receiver faults, and extend both this modified
algorithm and the Decay algorithm to robust multi-message broadcast algorithms.
We next investigate the extent to which (network) coding improves throughput
in noisy radio networks. We address the previously perplexing result of Alon et
al. 2014 that worst case coding throughput is no better than worst case routing
throughput up to constants: we show that the worst case throughput performance
of coding is, in fact, superior to that of routing -- by a
gap -- provided receiver faults are introduced. However, we show that any
coding or routing scheme for the noiseless setting can be transformed to be
robust to sender faults with only a constant throughput overhead. These
transformations imply that the results of Alon et al., 2014 carry over to noisy
radio networks with sender faults.Comment: Principles of Distributed Computing 201
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