  Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents
with multi-modal sensing capabilities that communicate over wireless networks.
WMSNs have more flexibility in terms of deployment and exploration abilities
over static sensor networks. Sensor networks have a wide range of applications
in security and surveillance systems, environmental monitoring, data gathering
for network-centric healthcare systems, monitoring seismic activities and
atmospheric events, tracking traffic congestion and air pollution levels,
localization of autonomous vehicles in intelligent transportation systems, and
detecting failures of sensing, storage, and switching components of smart
grids. The above applications require target tracking for processes and events
of interest occurring in an environment. Various methods and approaches have
been proposed in order to track one or more targets in a pre-defined area.
Usually, this turns out to be a complicated job involving higher order
mathematics coupled with artificial intelligence due to the dynamic nature of
the targets. To optimize the resources we need to have an approach that works
in a more straightforward manner while resulting in fairly satisfactory data.
In this paper we have discussed the various cases that might arise while
flocking a group of sensors to track targets in a given environment. The
approach has been developed from scratch although some basic assumptions have
been made keeping in mind some previous theories. This paper outlines a
customized approach for feasibly tracking swarms of targets in a specific area
so as to minimize the resources and optimize tracking efficiency.
