As the wireless sensor networks (WSNs) progress with newer and more advanced technologies, so do the demands for them in a growing number of applications. Precision agricultural environment monitoring is one of the most prominent applications that require feasible wireless support systems, particularly in the protection and condition control of the crops. This paper focuses on the grid nodes arrangement of WSN, considering the wide dissemination of the plantation areas in the agriculture industry. Due to the different types of sensors used and their data size, the study on the impact of the varied packet size on the performance of the small and large network has been carried out using AODV and OLSR routing protocols. No significant differences in terms of performance can be seen as the packet size is varied. However, compared to the small network, more performance issues have occured in the large network, such as more packet loss, higher throughput degradation, higher energy consumption, worse unfairness, and more overhead production. The OEG routing protocol has been proposed to enhance the network performance by reducing the strain due to the saturated traffic. When solely compared to AODV, OEG routing protocol is able to enhance the network performance with at most 27% more packet delivery ratio, 31kbps more throughput, and 0.991J lesser energy consumed in the network.
The aim of this study is to use the Box-Jenkins method to build a flood forecast model by analysing real-time flood parameters for Pengkalan Rama, Melaka river, hereafter known as Sungai Melaka. The time series was tested for stationarity using the Augmented Dickey-Fuller (ADF) and differencing method to render a non-stationary time series stationary from 1 July 2020 at 12:00am to 30th July 2020. A utocorrelation (ACF) and partial autocorrelation (PACF) functions was measured and observed using visual observation to identify the suitable model for water level time series. The parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to find the best ARIMA model (BIC). ARIMA (2, 1, 3) was the best ARIMA model for the Pengkalan Rama, with an AIC of 5653.7004 and a BIC of 5695.209. The ARIMA (2, 1, 3) model was used to produce a lead forecast of up to 7 hours for the time series. The model's accuracy was tested by comparing the original and forecast sequences by using Pearson r and R squared. The ARIMA model appears to be adequate for Sungai Melaka, according to the findings of this study. Finally, the ARIMA model provides an appropriate short-term water level forecast with a lead forecast of up to 7 hours. As a result, the ARIMA model is undeniably ideal for river flooding.
Due to extensive pipeline dissemination in the oil and gas refinery, the nodes need to be placed in a grid formation. As such, since most oil and gas industry applications require continuous data gathering, a heavy data stream will be introduced in the network traffic, mainly when the network density is high. As a result, performance degradation and poor energy consumption will occur. Ad hoc on-demand distance vector and optimized link state routing protocol have been simulated to investigate these issues further. Due to packet congestion, the network experiences a domino effect on the performance, such as packet loss, throughput degradation, and poor energy consumption. Thus, a tailored solution is required since oil and gas industry relies heavily on sensor data to keep track of pipelines condition to prevent anomalous events from happening. The proposed algorithm has been developed to optimize the network performance by dividing the traffic into two and by reducing the flooding during route discovery. The results have shown better network performance and energy consumption can be achieved using the proposed algorithm when compared to the others.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.