D. Jyothi Preshiya ¹, C. D. Suriyakala ²

Affiliations
1 Department of Electronic and Communication Engineering, Sathyabama University, Chennai - 600 119, Tamil Nadu, IN
2 Department of Electronic and Communication Engineering, Sree Narayana Gurukulam College of Engineering, Ernakulam - 682 311, Kerala, IN

Abstract

To what extend a wireless communication gadget can chip away at a single charge of a rechargeable battery obtains the importance. Capacity-wise, disposable batteries are superior to the rechargeable batteries. There are numerous ways utilized with which the battery life can be augmented. At the same time, identifying the cause of battery energy utilization boosts the researchers to design and develop protocols, methods and techniques to minimize the consumption of energy by which lifetime of the network could be extended. This paper investigates the sources and factors that lessen the network lifetime. The scope of this paper has been narrowed down to Delay Tolerant Network since this field of research has attracted a great deal of attention by the researchers mostly due to its challenging characteristic qualities. No promising connectivity, high mobility, indefinite node mobility patterns, energy and storage exhaustion comprise a few of the potential issues that one may confront in a DTN environment which in turn eat-off bandwidth and energy for the data delivery from the Source to the Destination.

Keywords

Ad Hoc Network, Delay Tolerant Network, Energy Efficiency, MAC Layer, Network Layer, Network Life time

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Tata Sudhakar ¹, C. D. Suriyakala ², Arathy R. Nair ³

Affiliations
1 Sathyabama University, Sholinganallur, Chennai – 600119, Tamil Nadu, IN
2 Toc H Institute of Science and Technology, Arakkunnam, Ernakulam – 682 313, Kerala, IN
3 National Institute of Ocean Technology, Pallikaranai, Chennai – 600100, Tamil Nadu, IN

Abstract

Objective: The long coastline and proximity to tsunamigenic zones mandate the requirement of an effective Tsunami forecasting system for India to minimize loss of life and property. Methods/Statistical Methods/ Statistical Analysis: In this paper, we discussed a tsunami forecasting model for Bay of Bengal using Artificial Neural Network (ANN) and network of eight Tsunami stations. The ANN algorithm at each station characterizes the tsunami detected, while velocity and direction are obtained from the specified arrangement of buoys. The effectiveness of the ANN algorithm in characterizing a tsunami is discussed using actual time-series data. Findings: It is observed that modification to the ANN algorithm in7 can characterize a tsunami effectively, in terms of its amplitude and period. Analysis of the methodology is carried out using simulated data for obtaining the direction and velocity of tsunami. Improvements/Applications: Presently, the Indian Tsunami Warning System (ITWS) issue warnings based on the possible scenario selection from its exhaustive event database on the basis of inputs collected from various sources. Applications: This methodology could augment capabilities of ITWS by providing additional inputs on peak amplitude, direction and velocity of a detected tsunami for the proper scenario selection. This in turn helps in disseminating more reliable warnings.

Keywords

Artificial Neural Network for Tsunami, Bottom Pressure Recorders, Characterization of Tsunami, Sumatra Earthquake on 12th September 2017, Tsunami Direction and Velocity, Tsunami Warning.

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