Semaphore Based Data Aggregation and Similarity Findings for Underwater Wireless Sensor Networks

Semaphore Based Data Aggregation and Similarity Findings for Underwater Wireless Sensor Networks

Ruby D (Research Scholar, Periyar Maniammai Institute of Science & Technology, Vallam, India) and Jeyachidra J (Associate Professor, Periyar Maniammai Institute of Science & Technology, Vallam, India)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJGHPC.2019070104

Abstract

A critical factor of underwater sensor networks (UWSN) is to maintain energy consumption at minimum, as immediate battery replacement is difficult. This is achieved by reducing duplication of data with similarity functions. The construction of optimal clustering is to avoid data loss. In this article, similarity function-based data aggregation with a Semaphore process is applied to UWSN to retain the energy level at an advantage. Sensor nodes (SNs) are clustered in a Date Palm Tree approach. The Minkowski Distance model is used in Data Aggregation Nodes (DANs) to check similar measures of readings collected from cluster members. The Semaphore concept is executed in all DANs and cluster heads (CHs) to enhance network life and regulate excessive exploitation of energy levels of the SN, DANs, and CHs. The message queue (MQ) can be used to allow the packets transferred from the DANs to the cluster heads (CHs). The proposed algorithm SBDA with similarity measures would result in better link quality, reduction in redundancy, data delay, and would control the consumption of energy.
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1. Introduction

The extensive research study with sensor networks on various aspects and the recent novel ideas have dipped into the submarine area of Underwater Wireless Sensor Networks (UWSNs). UWSNs differ from normal sensors with respect to the deployment of the sensor, signal processing, communication energy consumption, and data transfer. Common problems with UWSN are the utilization of energy, network failure, and collisions raised at the time of collecting and forwarding data from the source to the destination. If a group of sensor nodes is deployed without a suitable Cluster Head (CH) selection scheme, then the sensor nodes automatically collect the readings from the environment and transmit them to the sink node, but the sink node cannot get the effective and relevant information from the Sensor Nodes (SNs) using this method. Hence, something more is to be done to enhance the effectiveness of the sensor system.

Using a suitable cluster head selection algorithm with a topology-based approach reduces the power consumption derived by Huang et al. (2010). Sometimes, in UWSN, the position of the sensor nodes get unintentionally altered due to a natural disaster, wind, marine animal movements, human intervention, etc., narrated by Sadanand Yadev et al. (2017). Also, duplication of data may be received by the cluster head or sink node without a proper data aggregation algorithm. Hence, similarities-based data aggregation may be used to minimize the redundancy level of the duplication. Transforming the readings from the cluster head to the sink node by frame or channels will lead to a delay of transforming the packet, because the sink node receives packets one by one, as per the arrangement in the frame or channels. Mostly, the grid-based clustering is constructed whenever the frame or channel is used for communication purposes.

A distributed compressed sensing network with grid-based clustering may reduce the communication cost mentioned by researcher Oh et al. (2013). The collisions occur in the intra-cluster and inter-cluster communication approaches during the time of transactions and, hence, choosing the cluster head is very tricky. The UWSNs deployed in underwater consume more energy during its communications due to acoustic problems. All these factors have created a lot of pressure on the demand for innovative ideas to improve the lifetime of the network, reduce the loss of information, and reduce communication cost and optimum utilization of energy.

In the process of evolution, sensor nodes are deployed underwater, such as the Date Palm Tree approach, which is based on distance, node density, and energy. The entire system is sectored into three-layered approaches, the top layer is the sink node, the middle layer is the cluster heads (CHs), and the bottom layer is the data aggregation nodes (DANs). The distance for deploying the nodes has been calculated using the Minkowski Distance model for a particular node, between the neighboring nodes. This is one way to reduce redundant data received by the sensor. A Semaphore based data aggregation (SBDA) algorithm is used to stabilize the process to attain the deduplicated data. It consists of three states, -1, 0, and 1, which are implemented in the data aggregation nodes (DANs) and cluster heads (CHs). A Similarity checking algorithm (SCA) is used to identify similar data collected by the data aggregation nodes.

This work helps to overcome the main problems of the UWSNs with each level, i.e. the lower level to reduce redundancy, the middle level to maintain the energy level at a minimum and network lifetime, and the top level to receive the deduplicate packet with minimum delay. This new approach to improve the outcomes of the previous techniques in terms of energy, delay, and delivery ratio would result in optimizing the operating parameters.

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