Cluster-based comb-needle model for energy-efficient data aggregation in wireless sensor networks

Abstract

This paper deals with information discovery and aggregation in large scale wireless sensor networks applied for mission-critical applications like military reconnaissance. To support query processing based on the gathered information, an efficient and reliable information discovery mechanism is proposed for sensor networks. We extend the basic Comb-Needle Discovery Support Model [3] by including Cluster-based data aggregation mechanism, which helps minimize the communication cost. Clusterbased approach groups the sensor nodes in the sensor network. Each node of a group will send information to its Cluster Head, which then aggregates and forwards the information to the base station (Sink). We compare the performance of proposed Cluster-based Comb-Needle Model with the basic Comb-Needle Model using the parameters, namely, energy consumption and communication cost. The experimental results through Avrora Simulator on TinyOS Platform reveal that Clusterbased Comb-Needle Model for Data Aggregation helps a sensor network in conserving its energy, and thereby extending its life time.

A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things

“…In CH level, use three methods of distance functions, one-way ANOVA model, and set similarity function. The authors [ 18 ] have proposed a cluster-based data aggregation approach using the Comb-Needle model, which aims to minimize communication cost and energy consumption. The authors [ 19 ] have proposed a clustering approach in Underwater Sensor Network (UWSN) based on aggregation with Euclidean distance, aiming at data redundancy and analyzing network throughput and energy consumption.…”

Optimized clustering using soft computing approaches in wireless sensor networks: research dimensions and contributions

“…Researchers' interest in designing data aggregating techniques has grown rapidly in past few years (Al-Karaki et al 2004;Nandini and Patil 2010;Dagar and Mahajan 2013;Randhawa and Jain 2017;Dhand and Tyagi 2016;Gherbi 2015;Fang et al 2019;Shanmukhi and Ramanaiah 2015). In Al-Karaki et al (2004), author presented exact algorithms and approximate algorithms to select optimal number of aggregation points in the network.…”

“…In addition, this scheme not only minimizes communication overhead and energy utilization meanwhile it also preserves privacy in data communication. A comb needle discovery model for query processing is extended by introducing a cluster-based data combining method into it (Shanmukhi and Ramanaiah 2015). This extension results in minimizing the communication cost and energy utilization of the network, thereby enhancing the network life duration.…”

An Energy Efficient Data Aggregation for Random Sensor Networks

“…X. Liu [32] proposed Comb Needle Model to perform data aggregation based on push and pull strategies. Shanmukhi et al [33] had demonstrated Cluster based Comb Needle Model for grid networks. It has minimized communication cost and maximized the throughput.…”

“…Clustering has been added to CNM for regular network [33] to perform efficient data aggregation with energy efficiency. The basic CNM is extended to support the data aggregation in randomly deployed sensor networks [17] as shown in Figure 2.…”