## 2 Related Work
### Wireless Sensor Node Clustering Strategies  
Clustering in wireless sensor networks is a matter of interest not only as a method of data aggregation but also as a backdrop to support other wireless sensor network protocols such as routing and localization. The approach is used to organize fixed sensor nodes, distributed over a wide geographic area, into smaller groups, or clusters, that are responsible for specific regions in the tracking zone. The first problem that researchers must tackle when considering wireless sensor node clustering, is how these clusters are defined. [PERSON] _et al._[10] show the importance of this decision by comparing of the characteristics and trade-offs of different organizational types. They propose a multi-agent system design to explore geographic coalitions (_i.e._, size and shape of the sector) and functional differentiation, and perform experiments to determine the effect of sector size on communication load, load disparity between agents, average communication distance, and quality of tracking. Their results support the use of agents for these systems, but show that further work is required to fully understanding the effects of organizational trade-offs. Recent research on WSN clustering strategies has focused primarily on improving the efficiency of the overall network. For example, the desire to reduce the time and cost associated with replacingbatteries in sensors has motivated research on partitioning the WSN in ways that minimize overall energy consumption. [PERSON] and [PERSON] [11] propose a distributed randomized clustering algorithm for homogeneous wireless sensor networks: _i.e._, networks where every wireless node can become a cluster head with a probability that minimizes the total energy spent in the network. [PERSON] _et al._[12] present an energy-efficient dynamic clustering technique to route sensed information from the field sensors to a remote base station in large-scale sensor networks. In a network of homogeneous wireless sensor nodes, a probabilistic approach is employed to determine the cluster heads. [PERSON] _et al._[13] propose a distributed cluster formation technique to increase security in wireless sensor networks. The proposed distributed protocol first divides the nodes into mutually disjoint cliques. Then, all the normal nodes in each clique agree on the same clique memberships. Finally, while external attackers can be prevented from participating in the cluster formation process, inside attackers that do not follow the protocol semantics can be identified and removed from the network. Research has also focused on efficient geographical partitioning of sensor nodes around cluster heads. One particular method of interest that has been used to define clusters is the Voronoi diagram [14]. This method divides a set of points into regions, or cells, that each contains all points that are closest to a specified \"seed\" location. In the context of Figure 1, seed locations correspond to sink nodes and points correspond to anchor nodes. [PERSON] _et al._[15] propose a dynamic clustering technique based on Voronoi diagrams to track acoustic targets in wireless sensor networks. Given a heterogeneous wireless sensor network where wireless nodes possess different capabilities, the authors envision a hierarchical cluster that assumes the role of cluster head for the nodes with higher capabilities that are sparsely distributed in the network field. Voronoi diagrams are used by the cluster heads to muster an appropriate number of sensor nodes and dynamically form the clusters, and also to ensure only one cluster remains active at each time to track the mobile target. [PERSON] _et al._[16] adopt a range-based approach to localize the wireless sensor networks. As such, using average size of a hop, hops are mapped to distance units. To overcome the scalability limits of a DV-hop technique that is imposed by the large communication cost, the authors propose an approach based on Voronoi diagrams.