16,649 research outputs found
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
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
Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks
Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network
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