SNOOPING BASED SPATIAL, TEMPORAL CORRELATION WITH ADVANCED MULTIPLE DATA PREDICTION INTERFACE (SSAMI)

In the past few years, Wireless Sensor Network (WSN)has garnered considerable interest from the research on data redundancy. Nodal energy and bandwidth can be significantly preserved by reducing the spatio-temporal data redundancy. To reduce the redundancy of sensor data in spatial correlation, an e...

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Bibliographic Details
Published inNeuroQuantology Vol. 20; no. 14; p. 2598
Main Authors Abraham Amal Raj B, Sain, Mahaveer Kumar, Yadav, Dharmveer
Format Journal Article
LanguageEnglish
Published Bornova Izmir NeuroQuantology 01.01.2022
Subjects
Clustering
Correlation
Data collection
Data transmission
Energy
Energy consumption
Espionage
Nodes
Redundancy
Sensors
Similarity
Spatiotemporal data
Wireless sensor networks
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Summary:In the past few years, Wireless Sensor Network (WSN)has garnered considerable interest from the research on data redundancy. Nodal energy and bandwidth can be significantly preserved by reducing the spatio-temporal data redundancy. To reduce the redundancy of sensor data in spatial correlation, an exploitation of combined spatial, temporal correlation Advance Multiple Data Prediction Interface (SAMI) is introduced. Among sensory data, the projected work will use the Snooping based SAMI termed as SSAMI, which depends on their data collection using TDMA, data clustering, and similarity sub clustering. Furthermore, the nodes are assigned to the time slots based on their energy. When the data does not change in a specific time instant, the nodes are kept in the snooping process by observing the data trend similarity. The node does not sense the data during this time. As a result, energy needed for data sensing is conserved. Thus, SSAMI work helps the network save a considerable amount of energy. The time slots for data transmission by the sensor nodes are assigned in snooping-based methods based on the nodes' remaining energy. Therefore, the data is transmitted through the highest residual node. Additionally, data transmission only takes place during a time slot if there is a considerable data variance. Otherwise, only the spying mode is maintained for the nodes. As a result, SSAMI drastically reduces the number of transmissions, which in turn lowers energy usage as seen in the findings.
ISSN:1303-5150
DOI:10.48047/NQ.2022.20.14.NQ77235