Geographic and Battery-Aware Kubernetes Scheduling for IoT Sensing

In Internet of Things (IoT) applications, measurements may be requested at specified points and assigned to worker nodes whose locations and reported battery states differ. For such workloads, GeoBattery was implemented as an optional Kubernetes Scheduling Framework plugin. Target coordinates are read from pod annotations, while fresh ordinal battery reports are obtained from node conditions. Eligible nodes are then ranked by a configurable combination of Euclidean distance and reported battery state, following the standard feasibility checks. The plugin was evaluated against generated workloads in a Go simulator without a running cluster. Proximity, battery, and combined policies were compared over 1000 timesteps per run. A default profile was examined separately, and sensitivity was studied by changing one factor at a time. Under combined scoring, intermediate distance and completion outcomes were obtained relative to the policies using either input alone, with small differences in modeled lifetime during observation. These findings are confined to the generated workloads and stated battery assumptions. Physical energy use, battery endurance, and sensing quality remain to be measured on devices, and scheduler behavior requires evaluation in a live cluster.