This study presents novel approaches to the allocation of resources in Internet-of-Things sensor network (IoTSN) systems applied to water-quality monitoring for optimal and more sustainable utilization of resources. To tackle the long-standing energy scarcity issue that currently plagues sensor network (SN) systems, energy harvesting is explored and exploited to maximize its untapped potential to develop asuccessive wireless power sensor network (WPSN) system embedded with a scheduling algorithm, and operate as a non-orthogonal multiple access (NOMA) system. Similarly, quality of service parameters are crucial design considerations for network efficiency,and energy efficiency (EE) is considered here. Consequently, an EE optimization problem is formulated for the successiveWPSN system and solved by exploiting the problem structure and through a meta-heuristic algorithm. The new system is validated through the numerical simulation results presented in this work by thoroughly analyzing, evaluating and comparing the proposed meta-heuristic based WPSN system with the baseline state-of-the-art WPSN systems that combined a meta-heuristic algorithm, two additional meta-heuristic algorithms including genetic algorithm (GA) and ant-colony optimization (ACO) algorithm as well as a non-meta-heuristic algorithm – specifically an iterative based Dinkelbach algorithm.The experimental outcomes show that the proposed system significantly outperforms the contemporary WPSN systems in terms of EE performance gains.