38,604 research outputs found
Robust Sequential Detection in Distributed Sensor Networks
We consider the problem of sequential binary hypothesis testing with a
distributed sensor network in a non-Gaussian noise environment. To this end, we
present a general formulation of the Consensus + Innovations Sequential
Probability Ratio Test (CISPRT). Furthermore, we introduce two different
concepts for robustifying the CISPRT and propose four different algorithms,
namely, the Least-Favorable-Density-CISPRT, the Median-CISPRT, the M-CISPRT,
and the Myriad-CISPRT. Subsequently, we analyze their suitability for different
binary hypothesis tests before verifying and evaluating their performance in a
shift-in-mean and a shift-in-variance scenario.Comment: 13 pages, 5 figure
Multi-Target Tracking in Distributed Sensor Networks using Particle PHD Filters
Multi-target tracking is an important problem in civilian and military
applications. This paper investigates multi-target tracking in distributed
sensor networks. Data association, which arises particularly in multi-object
scenarios, can be tackled by various solutions. We consider sequential Monte
Carlo implementations of the Probability Hypothesis Density (PHD) filter based
on random finite sets. This approach circumvents the data association issue by
jointly estimating all targets in the region of interest. To this end, we
develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized
version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their
performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA)
metric, benchmarked against a distributed extension of the Posterior
Cram\'er-Rao Lower Bound (PCRLB), and compared to the performance of an
existing distributed PHD Particle Filter. Furthermore, the robustness of the
proposed tracking algorithms against outliers and their performance with
respect to different amounts of clutter is investigated.Comment: 27 pages, 6 figure
Distributed Change Detection via Average Consensus over Networks
Distributed change-point detection has been a fundamental problem when
performing real-time monitoring using sensor-networks. We propose a distributed
detection algorithm, where each sensor only exchanges CUSUM statistic with
their neighbors based on the average consensus scheme, and an alarm is raised
when local consensus statistic exceeds a pre-specified global threshold. We
provide theoretical performance bounds showing that the performance of the
fully distributed scheme can match the centralized algorithms under some mild
conditions. Numerical experiments demonstrate the good performance of the
algorithm especially in detecting asynchronous changes.Comment: 15 pages, 8 figure
Detection techniques of selective forwarding attacks in wireless sensor networks: a survey
The wireless sensor network has become a hot research area due its wide range
of application in military and civilian domain, but as it uses wireless media
for communication these are easily prone to security attacks. There are number
of attacks on wireless sensor networks like black hole attack, sink hole
attack, Sybil attack, selective forwarding attacks etc. in this paper we will
concentrate on selective forwarding attacks In selective forwarding attacks,
malicious nodes behave like normal nodes and selectively drop packets. The
selection of dropping nodes may be random. Identifying such attacks is very
difficult and sometimes impossible. In this paper we have listed up some
detection techniques, which have been proposed by different researcher in
recent years, there we also have tabular representation of qualitative analysis
of detection techniquesComment: 6 Page
- …