Databases Reference
In-Depth Information
Chapter 9
Event Detection within
Fine-Grained Networks
The intensive development of wireless technologies and the increasing minia-
turization of RF devices and micro electro-mechanical systems (MEMS) have
been driving forces in the advancement of small and tiny computing devices,
such as WSN technology. These devices are inter-connected and form a com-
putational network that is capable of providing a frontline processing scheme
for applications such as event detection and remote monitoring. Because this
type of network has a large number of computing nodes that have limited
power, storage, and processing capabilities, it is referred to as a fine-grained
network.
The ability to acquire resource-awareness characteristics was discussed in
the previous chapter and is essential for the design of distributed applications,
including pattern recognition. The DHGN as a distributed pattern recognition
scheme was developed with adaptive granularity characteristics built into its
design. A distributed and parallel pattern recognition scheme for applications
in fine-grained systems was introduced by Khan and Mihailescu [2].
In a fully distributed configuration, each GN is assigned to a single compute
node, and the collaborations of inter-connected compute nodes form a DHGN
subnet. The simple bias array search computations involved for each node
make this configuration well-suited for fine-grained networks that have limited
processing and storage capabilities, such as a WSN. We will demonstrate the
robustness and scalability of the DHGN for a distributed recognition process
over a fine-grained network using a number of DPR applications.
9.1 Distributed Event Detection Scheme for Wireless
Sensor Networks
Highly complex computations, iterative learning, and large training set re-
quirements are some of the weaknesses of event detection schemes commonly
deployed in Wireless Sensor Networks (WSNs). These schemes often apply
conventional neural networks or machine learning algorithms that require ex-
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