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An Integrated Trust Assisted Energy Efficient Greedy Data Aggregation for Wireless Sensor Networks - 2020

An Integrated Trust Assisted Energy Efficient Greedy Data Aggregation for Wireless Sensor Networks

Research Area:  Wireless Sensor Networks


Wireless sensor networks (WSN) gathers information pertaining to sensitive data. As because the sensor nodes in WSN are remote and unattended for a longer period of time, vulnerability to intrusions and attacks are also found to be higher, hence making the medium insecure. However, increasing reliability also results in an increase in energy consumption significantly. Though several methods and mechanisms designed for solving the above said security and energy issues, many of these experiences notable computational, communication, and storage requirements that frequently cannot be contended by resource-constrained sensor nodes. Therefore, in this work, a threefold homogeneous method is introduced that supervises secure neighbor selection, energy-efficient routing and data aggregation with the greedy approach that provides multi-objective purpose called, Trust Assisted Global and Greedy Congestion-aware Data Aggregation for (TAG–GCDA) secured WSN with reduced energy consumption and improved reliability. The method enhances global aggregation precision with finite restrictions in neighbor reliability and aggregation. The threefold process of the TAG–GCDA method ensures trusted neighbor selection based on correlative divergence, energy conservation using the global cost (i.e. predictable trust and unpredictable trust) and greedy congestion control for seamless transmission. These processes intend to reduce the energy usage of the sensors to increase the network lifetime with lesser control and communication overhead. The trade-off between energy and security is obtained so as to advance efficient energy consumption with a higher packet delivery ratio.


Author(s) Name:  K. P. Uvarajan,C. Gowri Shankar

Journal name:  Wireless Personal Communications

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11277-020-07394-z

Volume Information:  volume 114, pages 813–833 (2020)