Next Article in Journal
Efficient Three-Step Class of Eighth-Order Multiple Root Solvers and Their Dynamics
Next Article in Special Issue
Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors
Previous Article in Journal
A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested
Previous Article in Special Issue
Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network
Article Menu

Export Article

Open AccessArticle

Neighbor Discovery Optimization for Big Data Analysis in Low-Power, Low-Cost Communication Networks

1
Department of Computer Science & Engineering, Gangneung-Wonju National University, Wonju 26403, Korea
2
Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(7), 836; https://doi.org/10.3390/sym11070836
Received: 28 April 2019 / Revised: 20 June 2019 / Accepted: 24 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data 2019)
  |  
PDF [2011 KB, uploaded 1 July 2019]
  |  

Abstract

Big data analysis generally consists of the gathering and processing of raw data and producing meaningful information from this data. These days, large collections of sensors, smart phones, and electronic devices are all connected in the network. One of the primary features of these devices is low-power consumption and low cost. Power consumption is one of the important research concerns in low-power, low-cost communication networks such as sensor networks. A primary feature of sensor networks is a distributed and autonomous system. Therefore, all network devices in this type of network maintain the network connectivity by themselves using limited energy resources. When they are deployed in the area of interest, the first step for neighbor discovery involves the identification of neighboring nodes for connection and communication. Most wireless sensors utilize a power-saving mechanism by powering on the system if it is off, and vice versa. The neighbor discovery process becomes a power-consuming task if two neighboring nodes do not know when their partner wakes up and sleeps. In this paper, we consider the optimization of the neighbor discovery to reduce the power consumption in wireless sensor networks and propose an energy-efficient neighbor discovery scheme by adapting symmetric block designs, combining block designs, and utilizing the concept of activating nodes based on the multiples of a specific number. The performance evaluation demonstrates that the proposed neighbor discovery algorithm outperforms other competitive approaches by analyzing the wasted awakening slots numerically. View Full-Text
Keywords: neighbor discovery; optimization of neighbor discovery; wireless sensor network; asymmetric duty cycle; low-power; low-cost communication network neighbor discovery; optimization of neighbor discovery; wireless sensor network; asymmetric duty cycle; low-power; low-cost communication network
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Choi, S.; Yi, G. Neighbor Discovery Optimization for Big Data Analysis in Low-Power, Low-Cost Communication Networks. Symmetry 2019, 11, 836.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top