RETRACTED: Multifactor optimized mobile sink data collection framework for hybrid wireless sensor network‐long term evolution‐assisted Internet of Things network

Abstract

SummaryThe wireless sensor network‐assisted Internet of Things convergence 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. However, providing a scalable and effective solution with consideration for multicriteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network with long‐term evolution‐assisted architecture. The issue of maximizing lifetime and providing multifactor 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 multicriteria optimal clustering, optimal path scheduling, and network adaptive traffic class‐based data scheduling. This hybridization extends the network's lifespan and improves the QoS regarding packet transmission 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 with existing methodologies.