11,221 research outputs found
The Sensing Capacity of Sensor Networks
This paper demonstrates fundamental limits of sensor networks for detection
problems where the number of hypotheses is exponentially large. Such problems
characterize many important applications including detection and classification
of targets in a geographical area using a network of sensors, and detecting
complex substances with a chemical sensor array. We refer to such applications
as largescale detection problems. Using the insight that these problems share
fundamental similarities with the problem of communicating over a noisy
channel, we define a quantity called the sensing capacity and lower bound it
for a number of sensor network models. The sensing capacity expression differs
significantly from the channel capacity due to the fact that a fixed sensor
configuration encodes all states of the environment. As a result, codewords are
dependent and non-identically distributed. The sensing capacity provides a
bound on the minimal number of sensors required to detect the state of an
environment to within a desired accuracy. The results differ significantly from
classical detection theory, and provide an ntriguing connection between sensor
networks and communications. In addition, we discuss the insight that sensing
capacity provides for the problem of sensor selection.Comment: Submitted to IEEE Transactions on Information Theory, November 200
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Sensing Capacity for Markov Random Fields
This paper computes the sensing capacity of a sensor network, with sensors of
limited range, sensing a two-dimensional Markov random field, by modeling the
sensing operation as an encoder. Sensor observations are dependent across
sensors, and the sensor network output across different states of the
environment is neither identically nor independently distributed. Using a
random coding argument, based on the theory of types, we prove a lower bound on
the sensing capacity of the network, which characterizes the ability of the
sensor network to distinguish among environments with Markov structure, to
within a desired accuracy.Comment: To appear in the proceedings of the 2005 IEEE International Symposium
on Information Theory, Adelaide, Australia, September 4-9, 200
Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors
Most studies that consider the problem of estimating the location of a point
source in wireless sensor networks assume that the source location is estimated
by a set of spatially distributed sensors, whose locations are fixed. Motivated
by the fact that the observation quality and performance of the localization
algorithm depend on the location of the sensors, which could be randomly
distributed, this paper investigates the performance of a recently proposed
energy-based source-localization algorithm under the assumption that the
sensors are positioned according to a uniform clustering process. Practical
considerations such as the existence and size of the exclusion zones around
each sensor and the source will be studied. By introducing a novel performance
measure called the estimation outage, it will be shown how parameters related
to the network geometry such as the distance between the source and the closest
sensor to it as well as the number of sensors within a region surrounding the
source affect the localization performance.Comment: 7 Pages, 5 Figures, To appear at the 2014 IEEE International
Conference on Communications (ICC'14) Workshop on Advances in Network
Localization and Navigation (ANLN), Invited Pape
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