Data Aggregation Tree Construction Strategies for Increasing Network Lifetime in EH-WSN

In WSN one of the issues is to route the data from the sensor nodes to sink node. The tree-based approach provides an efficient solution for establishing the path and in network aggregation. Tree is nonlinear structures, which have hierarchical levels in terms of parent-child combination. In this work the tree is constructed using the software agents. The proposed scheme uses multiagent system that comprises of both static and mobile agents. On every sensor node of WSN agent platform is running that coordinates the agent communication. Tree Construction Agent (TCA) is mobile agent that is generated at the sink node. TCA uses the angle of constraint for the construction of the tree. In the proposed work along the tree in network aggregation is done that saves the energy and reduces the delay. Aggregation Agent (AA) gets the routing information and visits the nodes along the path (tree) for data aggregation based the correlation of the sensor node data. Finally the aggregation agent routes the aggregated data to the sink node.

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Load-balanced data aggregation tree construction for large scale cluster monitoring system

2010 International Conference on Information, Networking and Automation (ICINA) ◽

10.1109/icina.2010.5636959 ◽

2010 ◽

Author(s):

Yuqian Xiao ◽

Mingfa Zhu ◽

Limin Xiao ◽

Li Ruan

Keyword(s):

Monitoring System ◽

Data Aggregation ◽

Large Scale ◽

Tree Construction ◽

Aggregation Tree ◽

Load Balanced

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Secure clustering-based energy efficient protocol using hybrid soft computing

Modern Physics Letters B ◽

10.1142/s0217984920502036 ◽

2020 ◽

Vol 34 (18) ◽

pp. 2050203

Author(s):

Supreet Kaur ◽

Vijay Kumar Joshi

Keyword(s):

Soft Computing ◽

Network Lifetime ◽

Data Aggregation ◽

Energy Efficient ◽

Threshold Function ◽

Efficient Manner ◽

Aggregated Data ◽

Cluster Heads ◽

Extensive Analysis ◽

Cluster Data

A hybrid protocol is proposed which utilizes secure clustering and hybrid soft computing to improve the network lifetime. Ant colony optimization (ACO) and Particle swam optimization (PSO) with crossover operator are used to design a hybrid soft computing-based inter-cluster data aggregation. Initially, cluster heads are selected based upon adaptive threshold function. Recommendation-based signatures are then assigned to every aggregated data. Then, tree-based data aggregation comes in action and collects sensing information directly from cluster heads by utilizing short distance obtained from the hybrid soft computing. The use of compressive sensing reduces the packet size which is going to be transmitted in the sensor network. Extensive analysis shows that the hybrid protocol considerably enhances network lifetime by conserving the energy in more efficient manner than other protocols at present deployed for sensor networks.

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