5,291 research outputs found
Towards a Queueing-Based Framework for In-Network Function Computation
We seek to develop network algorithms for function computation in sensor
networks. Specifically, we want dynamic joint aggregation, routing, and
scheduling algorithms that have analytically provable performance benefits due
to in-network computation as compared to simple data forwarding. To this end,
we define a class of functions, the Fully-Multiplexible functions, which
includes several functions such as parity, MAX, and k th -order statistics. For
such functions we exactly characterize the maximum achievable refresh rate of
the network in terms of an underlying graph primitive, the min-mincut. In
acyclic wireline networks, we show that the maximum refresh rate is achievable
by a simple algorithm that is dynamic, distributed, and only dependent on local
information. In the case of wireless networks, we provide a MaxWeight-like
algorithm with dynamic flow splitting, which is shown to be throughput-optimal
Networked Computing in Wireless Sensor Networks for Structural Health Monitoring
This paper studies the problem of distributed computation over a network of
wireless sensors. While this problem applies to many emerging applications, to
keep our discussion concrete we will focus on sensor networks used for
structural health monitoring. Within this context, the heaviest computation is
to determine the singular value decomposition (SVD) to extract mode shapes
(eigenvectors) of a structure. Compared to collecting raw vibration data and
performing SVD at a central location, computing SVD within the network can
result in significantly lower energy consumption and delay. Using recent
results on decomposing SVD, a well-known centralized operation, into
components, we seek to determine a near-optimal communication structure that
enables the distribution of this computation and the reassembly of the final
results, with the objective of minimizing energy consumption subject to a
computational delay constraint. We show that this reduces to a generalized
clustering problem; a cluster forms a unit on which a component of the overall
computation is performed. We establish that this problem is NP-hard. By
relaxing the delay constraint, we derive a lower bound to this problem. We then
propose an integer linear program (ILP) to solve the constrained problem
exactly as well as an approximate algorithm with a proven approximation ratio.
We further present a distributed version of the approximate algorithm. We
present both simulation and experimentation results to demonstrate the
effectiveness of these algorithms
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Network correlated data gathering with explicit communication: NP-completeness and algorithms
We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal
Transform-based Distributed Data Gathering
A general class of unidirectional transforms is presented that can be
computed in a distributed manner along an arbitrary routing tree. Additionally,
we provide a set of conditions under which these transforms are invertible.
These transforms can be computed as data is routed towards the collection (or
sink) node in the tree and exploit data correlation between nodes in the tree.
Moreover, when used in wireless sensor networks, these transforms can also
leverage data received at nodes via broadcast wireless communications. Various
constructions of unidirectional transforms are also provided for use in data
gathering in wireless sensor networks. New wavelet transforms are also proposed
which provide significant improvements over existing unidirectional transforms
Message and time efficient multi-broadcast schemes
We consider message and time efficient broadcasting and multi-broadcasting in
wireless ad-hoc networks, where a subset of nodes, each with a unique rumor,
wish to broadcast their rumors to all destinations while minimizing the total
number of transmissions and total time until all rumors arrive to their
destination. Under centralized settings, we introduce a novel approximation
algorithm that provides almost optimal results with respect to the number of
transmissions and total time, separately. Later on, we show how to efficiently
implement this algorithm under distributed settings, where the nodes have only
local information about their surroundings. In addition, we show multiple
approximation techniques based on the network collision detection capabilities
and explain how to calibrate the algorithms' parameters to produce optimal
results for time and messages.Comment: In Proceedings FOMC 2013, arXiv:1310.459
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-smooth Data Field
Compressive Sensing (CS) has been applied successfully in a wide variety of
applications in recent years, including photography, shortwave infrared
cameras, optical system research, facial recognition, MRI, etc. In wireless
sensor networks (WSNs), significant research work has been pursued to
investigate the use of CS to reduce the amount of data communicated,
particularly in data aggregation applications and thereby improving energy
efficiency. However, most of the previous work in WSN has used CS under the
assumption that data field is smooth with negligible white Gaussian noise. In
these schemes signal sparsity is estimated globally based on the entire data
field, which is then used to determine the CS parameters. In more realistic
scenarios, where data field may have regional fluctuations or it is piecewise
smooth, existing CS based data aggregation schemes yield poor compression
efficiency. In order to take full advantage of CS in WSNs, we propose an
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS)
scheme. The proposed schemes dynamically chooses sparsity values based on
signal variations in local regions. We prove that A-HDACS enables more sensor
nodes to employ CS compared to the schemes that do not adapt to the changing
field. The simulation results also demonstrate the improvement in energy
efficiency as well as accurate signal recovery
- …