In the last decade, a new computing platform, Wireless Sensor Networks (WSN), has emerged. This platform is an interconnected and distributed transducer network of tiny, inexpensive sensors, and it has emerged as one of the essentials of contemporary ubiquitous computing. A WSN's many tiny sensor nodes work together to perform one or more tasks, normally involving some type of monitoring, tracking and controlling. One of the main goals of WSNs is to sense physical environments and detect events

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... occurring in the field of interest. Detection of events or materials of interest can be done by processing and analysing sensory information obtained by sensor nodes. Pattern recognition is one of the most useful and commonly utilised machine learning techniques in the literature for event detection in WSNs, especially when dealing with complex events. However, pattern recognition is highly affected by the limited resources offered by WSNs, including limited energy and limited computational, communicational and memory resources. In addition to limited resources, WSNs face other challenges in event detection, related to the dynamic nature of the environments in which WSNs are usually deployed. For example, a memorised pattern in a WSN pattern recognition scheme could appear in a different form, such as size dilation or location change, in the field of interest, or the WSN network's topology or sensor node locations might change, meaning the information memorised within the network will have different relations and distribution. The pattern recognition scheme also requires the capability of handling noisy patterns in order to maintain a high accuracy level. These noisy patterns are mainly the result of the monitoring environment and the limited lifetime of sensor nodes. Another issue associated with the nature of WSNs is the restricted number of training instances available, as events generally occur in some form of randomness. Therefore, designing a pattern recognition scheme for event detection in WSNs is a matter of a tra [...]