16,670 research outputs found
Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network
Device-free localization (DFL) based on the received signal strength (RSS)
measurements of radio frequency (RF)links is the method using RSS variation due
to the presence of the target to localize the target without attaching any
device. The majority of DFL methods utilize the fact the link will experience
great attenuation when obstructed. Thus that localization accuracy depends on
the model which describes the relationship between RSS loss caused by
obstruction and the position of the target. The existing models is too rough to
explain some phenomenon observed in the experiment measurements. In this paper,
we propose a new model based on diffraction theory in which the target is
modeled as a cylinder instead of a point mass. The proposed model can will
greatly fits the experiment measurements and well explain the cases like link
crossing and walking along the link line. Because the measurement model is
nonlinear, particle filtering tracing is used to recursively give the
approximate Bayesian estimation of the position. The posterior Cramer-Rao lower
bound (PCRLB) of proposed tracking method is also derived. The results of field
experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that
the tracking error of proposed model is improved by at least 36 percent in the
single target case and 25 percent in the two targets case compared to other
models.Comment: This paper has been withdrawn by the author due to some mistake
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
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