MoC-TSCH: multi-objective MILP-based TSCH mobility optimization for network coverage and connectivity in IIoT

The Industrial Internet of Things (IIoT) increasingly integrates heterogeneous sensing platforms, including mobile agents such as robots and drones, to enable real-time monitoring and control in dynamic environments. Although Time-Slotted Channel Hopping (TSCH) protocols offer deterministic scheduling and energy efficiency, their limited adaptability to mobility and dynamic topologies restricts their effectiveness in mobile IIoT scenarios. To overcome these limitations, this paper introduces MoC-TSCH, a mobility-aware TSCH framework enhanced with multi-objective Mixed-Integer Linear Programming. MoC-TSCH jointly optimizing initial static node placement at the boundary and mobile node trajectory planning, while dynamically adjusting timeslot and channel allocations in response to changing network conditions. Simulation results indicate that MoC-TSCH improves performance across spatial, temporal, and reliability metrics under evaluated scenarios. Coverage increases from 40% to over 88%, average joining time drops from 3.7 s to 2.4 s, and reliability reaches 92%. End-to-end latency is reduced from 128 ms to 89 ms. Estimated energy consumption, calculated using a supply voltage of 3.3 V, decreases from approximately 0.43 J (130 mAs) to 0.25 J (76 mAs). Handover analysis indicate adaptive behavior, with MoC-TSCH achieving higher handover rates than standard TSCH under the tested scenarios. Compared to MTSH, MoC-TSCH coordinates static and mobile nodes under a multi-objective optimization, yielding clearer gains in coverage, connectivity, join time, reliability consistency, and delay in dynamic IIoT scenarios. To validate these findings, a custom indoor testbed was deployed using mobile and static OpenMote B nodes, Nano33BLE sensors, and a TurtleBot 4 Lite platform. The results suggest that MoC-TSCH exhibits improve reliability and reduced delay relative to baseline TSCH in the evaluated IIoT scenarios.