10,651 research outputs found
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
A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework
An Online Decision-Theoretic Pipeline for Responder Dispatch
The problem of dispatching emergency responders to service traffic accidents,
fire, distress calls and crimes plagues urban areas across the globe. While
such problems have been extensively looked at, most approaches are offline.
Such methodologies fail to capture the dynamically changing environments under
which critical emergency response occurs, and therefore, fail to be implemented
in practice. Any holistic approach towards creating a pipeline for effective
emergency response must also look at other challenges that it subsumes -
predicting when and where incidents happen and understanding the changing
environmental dynamics. We describe a system that collectively deals with all
these problems in an online manner, meaning that the models get updated with
streaming data sources. We highlight why such an approach is crucial to the
effectiveness of emergency response, and present an algorithmic framework that
can compute promising actions for a given decision-theoretic model for
responder dispatch. We argue that carefully crafted heuristic measures can
balance the trade-off between computational time and the quality of solutions
achieved and highlight why such an approach is more scalable and tractable than
traditional approaches. We also present an online mechanism for incident
prediction, as well as an approach based on recurrent neural networks for
learning and predicting environmental features that affect responder dispatch.
We compare our methodology with prior state-of-the-art and existing dispatch
strategies in the field, which show that our approach results in a reduction in
response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201
- …