23,458 research outputs found
A Grid-Based Distributed Event Detection Scheme for Wireless Sensor Networks
This paper presents a grid-based distributed event detection scheme for wireless sensor networks. The network is divided into square-shaped grids of predefined grid size, where sensor nodes in each grid form a cluster with a cluster head. Event detection at each grid alone based on the readings of its member nodes is limited in event detection performance, especially for a small event region compared to the grid size. To improve the performance, each grid is further divided into 2 × 2 sub-grids of equal size. The decision on an event is made by finding a square region of 2 × 2 sub-grids, not necessarily in the same grid, that passed a predefined threshold. This process is conducted at each cluster head in a distributed manner by inter-cluster communications. Event detection is initiated when a cluster head receives an alarm from its member nodes. The cluster-head communicates with its neighboring cluster heads to exchange the number of nodes reporting an alarm. The threshold for event detection can be dynamically adjusted to reflect the number of sensor nodes in a grid and event size, if known. High event detection accuracy is achieved with a relatively low threshold without sacrificing false alarm rate by filtering most errors due to transient faults and isolating nodes with permanent faults. Experimental results show that the proposed scheme can achieve high detection accuracy, while maintaining low false alarm rate
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Outlier Detection Techniques For Wireless Sensor Networks: A Survey
In the field of wireless sensor networks, measurements that
significantly deviate from the normal pattern of sensed data are
considered as outliers. The potential sources of outliers include
noise and errors, events, and malicious attacks on the network.
Traditional outlier detection techniques are not directly
applicable to wireless sensor networks due to the multivariate
nature of sensor data and specific requirements and limitations of
the wireless sensor networks. This survey provides a comprehensive
overview of existing outlier detection techniques specifically
developed for the wireless sensor networks. Additionally, it
presents a technique-based taxonomy and a decision tree to be used
as a guideline to select a technique suitable for the application
at hand based on characteristics such as data type, outlier type,
outlier degree
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks
Sensor networks aim at monitoring their surroundings for event detection and
object tracking. But, due to failure, or death of sensors, false signal can be
transmitted. In this paper, we consider the problems of distributed fault
detection in wireless sensor network (WSN). In particular, we consider how to
take decision regarding fault detection in a noisy environment as a result of
false detection or false response of event by some sensors, where the sensors
are placed at the center of regular hexagons and the event can occur at only
one hexagon. We propose fault detection schemes that explicitly introduce the
error probabilities into the optimal event detection process. We introduce two
types of detection probabilities, one for the center node, where the event
occurs and the other one for the adjacent nodes. This second type of detection
probability is new in sensor network literature. We develop schemes under the
model selection procedure, multiple model selection procedure and use the
concept of Bayesian model averaging to identify a set of likely fault sensors
and obtain an average predictive error.Comment: 14 page
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