부산대학교 도서관

This paper poses new challenges, especially when designing routing protocols to improve the quality of service (QoS) criteria and the lifetime of large-scale wireless sensor networks (LS-WSNs) and high sensor node density WSNs (HSND-WSN). Some of these challenging problems for routing protocols are minimizing the distance between a base station (BS) and the cluster head (CH), latency in the transmission phase, power consumption in the clustering phase, and determining the precise SN location without error. Thus, for a dynamic WSN, it is important to support an intelligent mobile data collector (MDC) to continue data propagation despite the inevitable changes in the WSN topology. Considering these challenges, we propose a novel intelligent routing protocol based on the improved recursive distance vector-hop (IR-DV-Hop) localization algorithm to determine the accurate location of SNs without errors in LS-WSNs and determine the CHs and optimal route traversed by the MDC for energy efficiency, latency, and reliability. Specifically, the proposed mobile data collector-improved recursive distance vector-hop (MDC-IR-DV-Hop) protocol uses the MDC as an intermediate between the CH and BS to enhance the QoS of WSNs, reduce delays while collecting data, and improve the transmission phase of the routing protocol. Simulation results prove the performance of the proposed protocol compared with the energy-aware cluster based multi-hop (EACBM), tree clustering algorithm with mobile data collector (TCMDC), mobile data collector maximum residual energy low energy adaptive clustering hierarchy (MDC maximum residual energy leach), MDC minimum distance leach, mobile data collector-K-means (MDC-K), mobile data collector-traveling salesman problem-low energy adaptive clustering hierarchy-K-means (MDC-TSP-LEACH-K), a three-layer hierarchical architecture for optimized routing protocol based on clustering and the Khalimsky topology for mobile WSN (AMWSN), and node density-based clustering and mobile collection (NDCM) protocols. In addition, this article proposes a comparative analysis of the best clustering, CH election, localization and MDC by discussing and describing their functionalities, advantages, disadvantages and their main performance parameters.