A distinctive difference between conventional and distributed pattern recognition is the resource consideration. In a distributed approach, the system must be capable of utilizing the available resources effectively and efficiently. A good communication model must be considered to ensure proper utilization and communication of resources between processing nodes. Distributed pattern recognition (DPR) has the ability to scale up the process as the size of the problem increases. However, the scalability depends on the resource availability in a particular computational network. Resource availability is influenced by the capacity and stability of the computational network. The network capacity in distributed applications, such as DPR, is observed in terms of the granularity of the network. Commonly, computational networks are either coarse-grained, such as a computational grid, or fine-grained, such as a wireless sensor network (WSN). The processing capacities and capabilities of these networks differ. Because the application deployment focused on a single problem domain, most existing pattern recognition schemes are nonadaptive to different network granularities. The DHGN pattern recognition scheme described in Chapter 5 was developed with adaptive network granularity consideration [4] and the algorithm can be deployed in both coarse-and fine-grained networks. In this chapter, we will look at the network granularity aspect of distributed

pattern recognition (DPR). We will demonstrate how the DHGN algorithm can be deployed in a network of different granularity, which allows for flexible recognition of different forms of Internet-scale data. In addition, we will discuss specific pattern recognition applications in coarse-grained networks.