62,952 research outputs found
Distributed Detection and Estimation in Wireless Sensor Networks
In this article we consider the problems of distributed detection and
estimation in wireless sensor networks. In the first part, we provide a general
framework aimed to show how an efficient design of a sensor network requires a
joint organization of in-network processing and communication. Then, we recall
the basic features of consensus algorithm, which is a basic tool to reach
globally optimal decisions through a distributed approach. The main part of the
paper starts addressing the distributed estimation problem. We show first an
entirely decentralized approach, where observations and estimations are
performed without the intervention of a fusion center. Then, we consider the
case where the estimation is performed at a fusion center, showing how to
allocate quantization bits and transmit powers in the links between the nodes
and the fusion center, in order to accommodate the requirement on the maximum
estimation variance, under a constraint on the global transmit power. We extend
the approach to the detection problem. Also in this case, we consider the
distributed approach, where every node can achieve a globally optimal decision,
and the case where the decision is taken at a central node. In the latter case,
we show how to allocate coding bits and transmit power in order to maximize the
detection probability, under constraints on the false alarm rate and the global
transmit power. Then, we generalize consensus algorithms illustrating a
distributed procedure that converges to the projection of the observation
vector onto a signal subspace. We then address the issue of energy consumption
in sensor networks, thus showing how to optimize the network topology in order
to minimize the energy necessary to achieve a global consensus. Finally, we
address the problem of matching the topology of the network to the graph
describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R.
Chellapa and S. Theodoridis, Eds., Elsevier, 201
Artificial intelligence based event detection in wireless sensor networks
Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more recent applications is on fast and reliable identification of out-of-ordinary situations and events. This new functionality of wireless sensor networks is known as event detection. Due to the fact that collecting all sensor data centrally to perform event detection is inefficient in many occasions, the new trend in event detection in wireless sensor networks is to perform detection in the network. Design of in-network event detection methods for wireless sensor networks is by no means straightforward, as it needs to efficiently cope with various challenges and concerns including unreliability, heterogeneity, adaptability, and resource constraints. In this thesis, we tackle this problem by proposing fast, accurate, in-network, and intelligent event detection methods using artificial intelligence (AI) and machine learning (ML) approaches. To this end, the main objective of this thesis is to analyze, investigate applicability, and optimize artificial intelligence (AI) and machine learning (ML) methods for efficient, distributed, local and in-network event detection in wireless sensor networks (WSNs)
Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks
Existing anomaly and intrusion detection schemes of wireless sensor networks
have mainly focused on the detection of intrusions. Once the intrusion is
detected, an alerts or claims will be generated. However, any unidentified
malicious nodes in the network could send faulty anomaly and intrusion claims
about the legitimate nodes to the other nodes. Verifying the validity of such
claims is a critical and challenging issue that is not considered in the
existing cooperative-based distributed anomaly and intrusion detection schemes
of wireless sensor networks. In this paper, we propose a validation algorithm
that addresses this problem. This algorithm utilizes the concept of
intrusion-aware reliability that helps to provide adequate reliability at a
modest communication cost. In this paper, we also provide a security resiliency
analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure
Distributed Learning in Wireless Sensor Networks
The problem of distributed or decentralized detection and estimation in
applications such as wireless sensor networks has often been considered in the
framework of parametric models, in which strong assumptions are made about a
statistical description of nature. In certain applications, such assumptions
are warranted and systems designed from these models show promise. However, in
other scenarios, prior knowledge is at best vague and translating such
knowledge into a statistical model is undesirable. Applications such as these
pave the way for a nonparametric study of distributed detection and estimation.
In this paper, we review recent work of the authors in which some elementary
models for distributed learning are considered. These models are in the spirit
of classical work in nonparametric statistics and are applicable to wireless
sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference
on Communication, Control and Computing, University of Illinois, 200
FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks
Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods
A distributed scheme to detect wormhole attacks in mobile wireless sensor networks
Due to mostly being unattended, sensor nodes become open to physical attacks such as wormhole attack, which is our focus in this paper. Various solutions are proposed for wormhole attacks in sensor networks, but only a few of them take mobility of sensor nodes into account. We propose a distributed wormhole detection scheme for mobile wireless sensor networks in which mobility of sensor nodes is utilized to estimate two network features (i.e. network node density, standard deviation in network node density) through using neighboring information in a local manner. Wormhole attack is detected via observing anomalies in the neighbor nodesâ behaviors based on the estimated network features and the neighboring information. We analyze the performance of proposed scheme via simulations. The results show that our scheme achieves a detection rate up to 100% with very small false positive rate (at most 1.5%) if the system parameters are chosen accordingly. Moreover, our solution requires neither additional hardware nor tight clock synchronization which are both costly for sensor networks
Homology-based Distributed Coverage Hole Detection in Wireless Sensor Networks
Homology theory provides new and powerful solutions to address the coverage
problems in wireless sensor networks (WSNs). They are based on algebraic
objects, such as Cech complex and Rips complex. Cech complex gives accurate
information about coverage quality but requires a precise knowledge of the
relative locations of nodes. This assumption is rather strong and hard to
implement in practical deployments. Rips complex provides an approximation of
Cech complex. It is easier to build and does not require any knowledge of nodes
location. This simplicity is at the expense of accuracy. Rips complex can not
always detect all coverage holes. It is then necessary to evaluate its
accuracy. This work proposes to use the proportion of the area of undiscovered
coverage holes as performance criteria. Investigations show that it depends on
the ratio between communication and sensing radii of a sensor. Closed-form
expressions for lower and upper bounds of the accuracy are also derived. For
those coverage holes which can be discovered by Rips complex, a homology-based
distributed algorithm is proposed to detect them. Simulation results are
consistent with the proposed analytical lower bound, with a maximum difference
of 0.5%. Upper bound performance depends on the ratio of communication and
sensing radii. Simulations also show that the algorithm can localize about 99%
coverage holes in about 99% cases
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