Wireless Sensor Networks (WSNs) are integrated as a pillar of collaborative 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 pre-determined 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 firstly divided into grid cells with the same geographical 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 cluster 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