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All Journal International Journal of Electrical and Computer Engineering
Alani, Sameer
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Computer Science & IT Electrical & Electronics Engineering

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IoT based implemented comparison analysis of two well-known network platforms for smart home automation Alani, Sameer; Mahmood, Sarmad Nozad; Attaallah, Sarah Zaeead; Mhmood, Haneen Sameer; Khudhur, Zeena Abdulsattar; Dhannoon, Azzam Amer
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp442-450

Abstract

The developments of the internet of things (IoT) technologies fascinated the universe and provided great opportunities to introduce these innovations in smart house networks. Smart home automation is highly required these days. Smart home automation is a collection of electronic devices connected to monitor and control in the market home appliance remotely. However, it is still needed to design a friendly and reliable system since the system mainly depends on the devices used and the environment of the network. NETPI and BLYNK are IoT frameworks used for hardware-agnostic with smartphones, websites, private clouds, system security, data mining, and deep learning. The results confirmed that NETPI provides flexibility to deal with several NODEMCU controllers in a single control framework. The proposed system shows its applicability in monitoring and controlling home appliances remotely.
Secured node detection technique based on artificial neural network for wireless sensor network Hasan, Bassam; Alani, Sameer; Saad, Mohammed Ayad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp536-544

Abstract

The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network.
Co-Authors Attaallah, Sarah Zaeead Dhannoon, Azzam Amer Hasan, Bassam Khudhur, Zeena Abdulsattar Mahmood, Sarmad Nozad Mhmood, Haneen Sameer Saad, Mohammed Ayad