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Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health,…
In the era of big data and Internet of things, massive sensor data are gathered with Internet of things. Quantity of data captured by sensor networks are considered to contain highly useful and valuable information. However, for a variety…
Insitu sensors and Wireless Sensor Networks (WSNs) have become more and more popular in the last decade, due to their potential to be used in various applications of many different fields. As of today, WSNs are pretty much used by any…
Decisions in agriculture are frequently based on weather. With an increase in the availability and affordability of off-the-shelf weather stations, farmers able to acquire localised weather information. However, with uncertainty in the…
As the costs of sensors and associated IT infrastructure decreases - as exemplified by the Internet of Things - increasing volumes of observational data are becoming available for use by environmental scientists. However, as the number of…
Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of…
The use of in-situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real-time data for science, the data are prone to technical anomalies that can undermine the…
In this paper we present an application of techniques from statistical signal processing to the problem of event detection in wireless sensor networks used for environmental monitoring. The proposed approach uses the well-established…
Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports…
Streaming data can arise from a variety of contexts. Important use cases are continuous sensor measurements such as temperature, light or radiation values. In the process, streaming data may also contain data errors that should be cleaned…
Anomalies (unusual patterns) in time-series data give essential, and often actionable information in critical situations. Examples can be found in such fields as healthcare, intrusion detection, finance, security and flight safety. In this…
Edge computing enabled smart greenhouse is a representative application of Internet of Things technology, which can monitor the environmental information in real time and employ the information to contribute to intelligent decision-making.…
Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning. However, tracking real-world…
Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, social networks, or internet traffic networks are best…
As climate change poses new and more unpredictable challenges to society, insurance is an essential avenue to protect against loss caused by extreme events. Traditional insurance risk models employ statistical analyses that are inaccurate…
This paper proposes a Bayesian modeling approach to address the problem of online fault-tolerant dynamic event region detection in wireless sensor networks. In our model every network node is associated with a virtual community and a trust…
The problem of quickest growing dynamic anomaly detection in sensor networks is studied. Initially, the observations at the sensors, which are sampled sequentially by the decision maker, are generated according to a pre-change distribution.…
Analysis of water and environmental data is an important aspect of many intelligent water and environmental system applications where inference from such analysis plays a significant role in decision making. Quite often these data that are…
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time…
Existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. Thus, all sensor data have to be uploaded to the…