Multi-factor optimized mobile sink data collection framework for hybrid WSN-LTE assisted IoT network

The convergence of wireless sensor network-assisted Internet of Things has diverse applications. In most applications, the sensors are battery-powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks-based data collection is used to extend the lifespan of these networks. But providing a scalable and effective solution with consideration for multi-criteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network-Long term evolution assisted architecture. The problem of maximizing lifetime and providing multi-factor quality of service is solved as a two-stage optimization problem involving clustering and data collection path scheduling. Hybrid meta-heuristics is used to solve the clustering optimization problem. Minimal Steiner tree-based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multi-criteria optimal clustering, optimal path scheduling, and network adaptive traffic class-based data scheduling. This hybridization helps to extend the lifetime and enhance the QoS regarding packet delivery within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared to existing methodologies.