Size- and lifetime-aware disjoint dominating set formation and multi-armed-bandit-based scheduling for energy-efficient IoT-WSNs in sustainable smart city applications

Abstract

The versatility and broad applicability of IoT-based heterogeneous wireless sensor networks (HWSNs) make them key enablers of sustainability objectives in sustainable smart cities (SSCs). However, their heterogeneous architecture must be carefully managed to ensure reliable and energy-efficient operation. To prolong network lifetime and avoid premature node depletion, effective energy management mechanisms are essential. One promising approach is to exploit the concept of dominating sets (DSs), whereby sensor nodes are partitioned into disjoint DSs, and only one set is activated at a time. In this work, we propose EAAS-S4C-MAB, an enhanced framework for IoT-based HWSNs in SSCs that combines skyline-based DS formation, four-case, size- and lifetime-aware scheduling, and energy-aware data collection within the active DS. In the DS formation phase, the proposed Energy-Attentive Algorithm with Skyline (EAAS) generates multiple valid disjoint DS candidates per iteration, evaluates them jointly in terms of lifetime and size, and applies a BNL_SKYLINE procedure to retain only non-dominated candidates. In the scheduling phase, the proposed S4C-MAB algorithm classifies DSs into four cases according to size and lifetime and uses a multi-armed bandit to learn the relative priority of these cases online, replacing fixed case weights with adaptive case-level preferences. These learned priorities are combined with a dynamically recomputed adjusted lifetime, along with case-dependent activation caps and cooldown intervals, to guide DS activation. During communication, the active DS nodes form a chain, and the node nearest to the sink serves as the chain leader for final data delivery. Simulation results show that EAAS-S4C-MAB achieves more balanced DS utilization, reduces the premature depletion of fragile sets, and significantly extends network lifetime compared to other baseline methods, while preserving network coverage and connectivity.