4,643 research outputs found
New Models for the Correlation in Sensor Data
In this paper, we propose two new models of spatial correlations in sensor
data in a data-gathering sensor network. A particular property of these models
is that if a sensor node knows in \textit{how many} bits it needs to transmit
its data, then it also knows \textit{which} bits of its data it needs to
transmit.Comment: 3 pages, 2 figure
Worst-Case Interactive Communication and Enhancing Sensor Network Lifetime
We are concerned with the problem of maximizing the worst-case lifetime of a
data-gathering wireless sensor network consisting of a set of sensor nodes
directly communicating with a base-station.We propose to solve this problem by
modeling sensor node and base-station communication as the interactive
communication between multiple correlated informants (sensor nodes) and a
recipient (base-station). We provide practical and scalable interactive
communication protocols for data gathering in sensor networks and demonstrate
their efficiency compared to traditional approaches.
In this paper, we first develop a formalism to address the problem of
worst-case interactive communication between a set of multiple correlated
informants and a recipient. We realize that there can be different objectives
to achieve in such a communication scenario and compute the optimal number of
messages and bits exchanged to realize these objectives. Then, we propose to
adapt these results in the context of single-hop data-gathering sensor
networks. Finally, based on this proposed formalism, we propose a clustering
based communication protocol for large sensor networks and demonstrate its
superiority over a traditional clustering protocol.Comment: Minor revision: fixed some typos and reorganized some portions. 12
pages, 3 figure
Energy Conscious Interactive Communication for Sensor Networks
In this work, we are concerned with maximizing the lifetime of a cluster of
sensors engaged in single-hop communication with a base-station. In a
data-gathering network, the spatio-temporal correlation in sensor data induces
data-redundancy. Also, the interaction between two communicating parties is
well-known to reduce the communication complexity. This paper proposes a
formalism that exploits these two opportunities to reduce the number of bits
transmitted by a sensor node in a cluster, hence enhancing its lifetime. We
argue that our approach has several inherent advantages in scenarios where the
sensor nodes are acutely energy and computing-power constrained, but the
base-station is not so. This provides us an opportunity to develop
communication protocols, where most of the computing and communication is done
by the base-station.
The proposed framework casts the sensor nodes and base-station communication
problem as the problem of multiple informants with correlated information
communicating with a recipient and attempts to extend extant work on
interactive communication between an informant-recipient pair to such
scenarios. Our work makes four major contributions. Firstly, we explicitly show
that in such scenarios interaction can help in reducing the communication
complexity. Secondly, we show that the order in which the informants
communicate with the recipient may determine the communication complexity.
Thirdly, we provide the framework to compute the -message communication
complexity in such scenarios. Lastly, we prove that in a typical sensor network
scenario, the proposed formalism significantly reduces the communication and
computational complexities.Comment: 6 pages, 1 figure. Minor revision: fixed a couple of typo
Secure Analog Network Coding in Layered Networks
We consider a class of Gaussian layered networks where a source communicates
with a destination through intermediate relay layers with nodes in each
layer in the presence of a single eavesdropper which can overhear the
transmissions of the nodes in any one layer. The problem of maximum secrecy
rate achievable with analog network coding for a unicast communication over
such layered wireless relay networks with directed links is considered. A relay
node performing analog network coding scales and forwards the signals received
at its input. The key contribution of this work is a lemma that provides the
globally optimal set of scaling factors for the nodes that maximizes the
end-to-end secrecy rate for a class of layered networks. We also show that in
the high-SNR regime, ANC achieves secrecy rates within a constant gap of the
cutset upper bound on the secrecy capacity. To the best of our knowledge, this
work offers the first characterization of the performance of secure ANC in
multi-layered networks in the presence of an eavesdropper.Comment: 13 pages, 5 figures. arXiv admin note: substantial text overlap with
arXiv:1607.0018
Coding, Scheduling, and Cooperation in Wireless Sensor Networks
We consider a single-hop data gathering sensor cluster consisting of a set of
sensors that need to transmit data periodically to a base-station. We are
interested in maximizing the lifetime of this network. Even though the setting
of our problem is very simple, it turns out that the solution is far from easy.
The complexity arises from several competing system-level opportunities
available to reduce the energy consumed in radio transmission. First, sensor
data is spatially and temporally correlated. Recent advances in distributed
source-coding allow us to take advantage of these correlations to reduce the
number of transmitted bits, with concomitant savings in energy. Second, it is
also well-known that channel-coding can be used to reduce transmission energy
by increasing transmission time. Finally, sensor nodes are cooperative, unlike
nodes in an ad hoc network that are often modeled as competitive, allowing us
to take full advantage of the first two opportunities for the purpose of
maximizing cluster lifetime. In this paper, we pose the problem of maximizing
lifetime as a max-min optimization problem subject to the constraint of
successful data collection and limited energy supply at each node. By
introducing the notion of instantaneous decoding, we are able to simplify this
optimization problem into a joint scheduling and time allocation problem. We
show that even with our ample simplification, the problem remains NP-hard. We
provide some algorithms, heuristics and insight for various scenarios. Our
chief contribution is to illustrate both the challenges and gains provided by
joint source-channel coding and scheduling.Comment: 10 pages, 1 figur
Worst-case Compressibility of Discrete and Finite Distributions
In the worst-case distributed source coding (DSC) problem of [1], the smaller
cardinality of the support-set describing the correlation in informant data,
may neither imply that fewer informant bits are required nor that fewer
informants need to be queried, to finish the data-gathering at the sink. It is
important to formally address these observations for two reasons: first, to
develop good worst-case information measures and second, to perform meaningful
worst-case information-theoretic analysis of various distributed data-gathering
problems. Towards this goal, we introduce the notions of bit-compressibility
and informant-compressibility of support-sets. We consider DSC and distributed
function computation problems and provide results on computing the bit- and
informant- compressibilities regions of the support-sets as a function of their
cardinality.Comment: 5 pages, 3 figure
Worst-case Asymmetric Distributed Source Coding
We consider a worst-case asymmetric distributed source coding problem where
an information sink communicates with correlated information sources to
gather their data. A data-vector
is derived from a discrete and finite joint probability distribution and component is revealed to the
source, . We consider an asymmetric communication scenario where
only the sink is assumed to know distribution . We are interested
in computing the minimum number of bits that the sources must send, in the
worst-case, to enable the sink to losslessly learn any revealed to
the sources.
We propose a novel information measure called information ambiguity to
perform the worst-case information-theoretic analysis and prove its various
properties. Then, we provide interactive communication protocols to solve the
above problem in two different communication scenarios. We also investigate the
role of block-coding in the worst-case analysis of distributed compression
problem and prove that it offers almost no compression advantage compared to
the scenarios where this problem is addressed, as in this paper, with only a
single instance of data-vector.Comment: 22 pages, 10 figure
Network Simplification for Secure AF Relaying
We consider a class of Gaussian layered networks where a source communicates
with a destination through L intermediate relay layers with N nodes in each
layer in the presence of a single eavesdropper which can overhear the
transmissions of the nodes in the last layer. For such networks we address the
question: what fraction of maximum secure achievable rate can be maintained if
only a fraction of available relay nodes are used in each layer? In particular,
we provide upper bounds on additive and multiplicative gaps between the optimal
secure AF when all N relays in each layer are used and when only k, 1 <= k < N,
relays are used in each layer. We show that asymptotically (in source power),
the additive gap increases at most logarithmically with ratio N/k and L, and
the corresponding multiplicative gap increases at most quadratically with ratio
N/k and L. To the best of our knowledge, this work offers the first
characterization of the performance of network simplification in layered
amplify-and-forward relay networks in the presence of an eavesdropper.Comment: 14 pages, 1 figure. arXiv admin note: text overlap with
arXiv:1204.215
Energy- and Spectral- Efficiency Tradeoff for D2D-Multicasts in Underlay Cellular Networks
Underlay in-band device-to-device (D2D) multicast communication, where the
same content is disseminated via direct links in a group, has the potential to
improve the spectral and energy efficiencies of cellular networks. However,
most of the existing approaches for this problem only address either spectral
efficiency (SE) or energy efficiency (EE). We study the tradeoff between SE and
EE in a single cell D2D integrated cellular network, where multiple D2D
multicast groups (MGs) may share the uplink channel with multiple cellular
users (CUs). We formulate the EE maximization problem with constraint on SE and
maximum available transmission power. A power allocation algorithm is proposed
to solve this problem and its efficacy is demonstrated via extensive numerical
simulations. The tradeoff between SE and EE as a function of density of D2D
MGs, and maximum transmission power of a MG is characterized.Comment: 8 pages, 2 figure
Distributed Function Computation in Asymmetric Communication Scenarios
We consider the distributed function computation problem in asymmetric
communication scenarios, where the sink computes some deterministic function of
the data split among N correlated informants. The distributed function
computation problem is addressed as a generalization of distributed source
coding (DSC) problem. We are mainly interested in minimizing the number of
informant bits required, in the worst-case, to allow the sink to exactly
compute the function. We provide a constructive solution for this in terms of
an interactive communication protocol and prove its optimality. The proposed
protocol also allows us to compute the worst-case achievable rate-region for
the computation of any function. We define two classes of functions: lossy and
lossless. We show that, in general, the lossy functions can be computed at the
sink with fewer number of informant bits than the DSC problem, while
computation of the lossless functions requires as many informant bits as the
DSC problem.Comment: 10 pages, 6 figures, 2 table
- β¦