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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
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An effective adjustment approach of event reliability (EAAER) with mobile sink using shortest path routing for WSN
*Navneet VishnoiCorresponding authornavneetv.computers@tmu.ac.inCollege of Computing Sciences and Information Technology Teerthanker Mahaveer UniversityCollege of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad, Uttar Pradesh, IndiaView full profile →
, Rakesh Kumar Dwivediprincipal.computers@tmu.ac.inCollege of Computing Sciences and Information Technology Teerthanker Mahaveer UniversityCollege of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad, Uttar Pradesh, IndiaView full profile →
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A crucial requirement for Wireless Sensor Networks (WSNs) is reliable data transfer. To maintain the overall reliability of the network, it is essential that the data delivered from a source node reach its intended destination without any loss or errors. In order to collect data from the sensor nodes that are deploy randomly around the sensor region, wireless sensor networks (WSNs) typically use static sinks. In the static sink-based technique, the data packets are transmitted through the network to reach the base station. The static sink is ineffective at utilizing energy as a result. This paper presents an effective adjustment approach for event reliability (EAAER) to find duplicate events using a mobile sink for homogenous wsn’s. The EAAER approach improves the routing against the base model by using shortest path routing. Simulation results show that the performance of EEAER increases battery utilization and the number of received packets while minimizing duplicate packets and packets lost during transmission compared to the base model.
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