Ghufran Isam Drewil; Riyadh Jabbar Albahadili
Abstract
Air pollution is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. This problem results from the abundance of automobiles, industrial production, and combustion of transportation and electricity generation petroleum products. Therefore, ...
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Air pollution is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. This problem results from the abundance of automobiles, industrial production, and combustion of transportation and electricity generation petroleum products. Therefore, forecasting air pollution is necessary. In this paper, a system is proposed to monitor the level of air pollution by integrating the Internet of Things(IoT) with Wireless Sensor Networks (WSN), where pollution levels are observed in three areas in Baghdad using different types of sensors connected with ESP32 (It is the name of the chip developed by Espressif Systems) to detect Particulate Matter (PM2.5 and PM10), Nitrogen Oxides (NOx), Carbon monoxide (CO) as well as temperature and humidity to monitor indoor and outdoor air quality. Observed results are monitored by ThingSpeak, an open- source IoT platform. Success has been achieved using the ESP32 microcontroller, as the project is low-cost and uncomplicated, and pollutant measurement is accurate compared to the natural proportions of pollutants. Data display is easy and it can be monitored easily. This encourages the improvement of the model and its use in other monitoring systems.
Methaq Khamees Faraj; Ahmed Al-Saadi; Riyadh Jabbar Albahadili
Volume 20, Issue 3 , July 2020, , Page 65-74
Abstract
The number of devices connected to networks and the internet such as the Internet of Things, machine to machine, social media or speech traffic, etc., are rapidly increased that results in a huge amount of traffic. This leads to congestion that increases packet loss and reduces system performance. Therefore, ...
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The number of devices connected to networks and the internet such as the Internet of Things, machine to machine, social media or speech traffic, etc., are rapidly increased that results in a huge amount of traffic. This leads to congestion that increases packet loss and reduces system performance. Therefore, a single server cannot handle this traffic and need to use some approaches to optimize network performance. The use of a load balancer to distribute network traffic among multiple servers could minimize the load on a single server, provide availability, scalability, and enhance network performance. A load balancer in a traditional network is a dedicated hardware device that is expensive, close vender, and non-programmable. A load balancer contains few algorithms that network engineers cannot change or create a new one. In contrast, Software Defined Network (SDN) that utilizes load balancer is programmable (hardware independent) and more agilely.The objective of this investigation is to implement the Least packet load algorithm, which is used in the traditional load balancer, using an SDN-controller Python Network Operating system (POX) in order to distribute load among servers. Moreover, it discusses some research opportunities that this work introduces to improve load balancing in SDN. This work is validated through extensive simulations and emulations that compare the proposed algorithm with four of the most widely cited schemes. The results indicate that the proposed algorithm improved network performance and achieve up to 21% increase to system throughput compared to other benchmark approaches.
