Wireless sensor networks (WSNs) are integrated
as a pillar of collaborative Internet of Things (IoT)
technologies for the creation of pervasive smart environments.
Generally, IoT end nodes (or WSN sensors) can be
mobile or static. In this kind of hybrid WSNs, mobile sinks
move to predetermined sink locations to gather data
sensed by static sensors. Scheduling mobile sinks energyefficiently
while prolonging the network lifetime is a
challenge. To remedy this issue, we propose a three-phase
energy-balanced heuristic. Specifically, the network region
is first divided into grid cells with the same geo-graphical
size. These grid cells are assigned to clusters through an
algorithm inspired by the k-dimensional tree algorithm,
such that the energy consumption of each clus-ter is
similar when gathering data. These clusters are adjusted
by (de)allocating grid cells contained in these clusters,
while considering the energy consumption of sink
movement. Consequently, the energy to be consumed in
each cluster is approximately balanced considering the
energy consumption of both data gathering and sink movement.
Experimental evaluation shows that this technique
can generate an optimal grid cell division within a limited
time of iterations and prolong the network lifetime.