Restructuring binomial trees for delay-aware and energy-efficient data aggregation in wireless sensor networks

Energy conservation is a fundamental problem in wireless sensor networks that has attracted a great attention in recent years, as each sensor node is battery powered. In this paper, we consider an excessive energy consumption scenario like data transmission between sensor node and far off destination, and present distributed control data aggregation and energy efficiency scheme. Our scheme maintains the strengths of existing algorithms like Restructuring Binomial Trees (RBT) scheme and improves the energy conservation performance in wireless sensor networks. When a node is inserted or deleted in the network, tree rotations are used in a number of tree data structures such as AVL trees and red-black trees. These trees keep the tree balanced by using the rotation method that does not affect the surrounding. The proposed scheme utilizes the rotation mechanism for rebuilding a binomial tree, in which the sensor nodes swap the role partly and then reduce tree cost while keeping the tree structure. Swapping is performed between the parent and child nodes. The proposed scheme maintains the minimum data aggregation delay and increases the energy efficiency of the sensor nodes. Simulation results show that, the cost of building trees by DADC and DEDA is decreased by 45.84% and 14.28% on average respectively, by using the proposed scheme.

[1]  Hyunseung Choo,et al.  Delay-minimized Energy-efficient Data Aggregation in Wireless Sensor Networks , 2012, 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[2]  Ibrahim Korpeoglu,et al.  Power efficient data gathering and aggregation in wireless sensor networks , 2003, SGMD.

[3]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[4]  Poonam Sinha,et al.  Clustering Protocols in Wireless Sensor Networks : A Survey 1 st , 2013 .

[5]  Chi-Tsun Cheng,et al.  A Delay-Aware Data Collection Network Structure for Wireless Sensor Networks , 2011, IEEE Sensors Journal.

[6]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[7]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[8]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.