7,712 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
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
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
Validating an integer non-linear program optimization model of a wireless sensor network using agent-based simulation
Deploying wireless sensor networks (WSN) along a barrier line to provide surveillance against illegal intruders is a fundamental sensor-allocation problem. To maximize the detection probability of intruders with a limited number of sensors, we propose an integer non-linear program optimization model which considers multiple types of sensors and targets, probabilistic detection functions and sensor-reliability issues. An agent-based simulation (ABS) model is used to validate the analytic results and evaluate the performance of the WSN under more realistic conditions, such as intruders moving along random paths. Our experiment shows that the results from the optimization model are consistent with the results from the ABS model. This increases our confidence in the ABS model and allows us to conduct a further experiment using moving intruders, which is more realistic, but it is challenging to find an analytic solution. This experiment shows the complementary benefits of using optimization and ABS models
Probabilistic model for Intrusion Detection in Wireless Sensor Network
Intrusion detection in Wireless Sensor Network (WSN) is important through the view of security in WSN. Sensor Deployment Strategy gives an extent to security in WSNs. This paper compares the probability of intrusion detection in both the Poisson as well as Gaussian deployment strategies. It focuses on maximizing intrusion detection probability by assuming the combination of these two deployment strategies and it gives theoretical proposal with respect to intrusion detection
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