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Performance of various forecasting algorithms to reduce the number of transmitted packets by sensor node in wireless sensor networks

Husni, Muhammed Ihsan (2018) Performance of various forecasting algorithms to reduce the number of transmitted packets by sensor node in wireless sensor networks. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

The development of wireless sensor networks originated in military research and the application of monitoring conflict areas. One of the main features of WSNs is the limited energy of their wireless sensor nodes. Energy consumption is an important issue in WSN. Reducing the number of transmission packet messages for sensor nodes is one of the techniques. Furthermore, forecasting is one of the most common solutions to reduce them. Therefore, the main contribution of this thesis is to study the performances of different algorithms that can reduce the number of data packets transmitted by sensor nodes within the WSN. The simulation experiments are done using MATLAB software for a variety of algorithms. The selected algorithms for the reduction algorithm in the transmissions include Move Average algorithm (MA), Autoregressive all-pole model parameters — Burg’s algorithm. (AR-B), Autoregressive all-pole model parameters — Yule-Walker algorithm (AR-YW) and an Efficient Data Collection and Dissemination Algorithm (EDCD1). The data has been extensively gathered at 500 data points. The performance comparison shows that the on the basis of reduction in the data packet transmissions from the source to the sink EDCD1 algorithm shows the maximum reduction of 92% while a minimum reduction of 23% is shown in case of MA and the reduction of AR-B and AR-YW are 58% and 56, respectively. Moreover, in term of overall performance for reduction in a number of data transmission reduction EDCD1 algorithm shows the highest ratios. Additionally, in terms of absolute error in the data at the sink, the EDCD1 algorithm shows the best performance with less average error at 2.2803 for all sensors compared to others algorithms.

Item Type: Thesis (Masters)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-300 Electrical engineering. Electronics. Nuclear engineering
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Technology
Depositing User: Sabarina Che Mat
Date Deposited: 02 Feb 2020 03:56
Last Modified: 02 Feb 2020 03:56
URI: http://eprints.uthm.edu.my/id/eprint/12068
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