Application of Self-Managing System in Greenhouse with Wireless Sensor Network

Khudhur, Dhuha Dheyaa; Croock, Muayad Sadik

doi:10.33103/uot.ijccce.21.1.5

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Wi-Fi functionality is provided by the ESP8266 chip on the NodeMCU board. Using the TCP/IP protocol, the ESP8266 from Espressif is a cheap WiFi chip [25]. The LEDs represent the (client) sensors, which in turn transmit data from the environment to the NodeMCU board (server), which transmits it to the gateway.…”

Section: E In-house Prototyping Of Internet-of-things Devices Using R...mentioning

confidence: 99%

Design and Implementation of Security Gatway for IoT Devices Security

2023

IJCCCE

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As the Internet of Things (IoT) is growing in popularity globally, which has resulted in a rise in cyber threats, experts are focusing more on its security. The majority of IoT security research to date has concentrated on huge devices, while small IoT devices have received comparably little attention. Our primary purpose is, therefore, to research how to ensure the operation of IoT devices that are small. The security gateway is a Security Settings on the Gateway for RaspberryPi built gateway that may link Internet of Things devices to their private network, safeguarding IoT devices from exposure to external networks. In addition, a variety of Security Settings on the Gateway for RaspberryPi security settings are installed, including fiel2ban and a Security Settings on the Gateway for RaspberryPi firewall, in order to avoid brute force and dictionary attacks. This article also studies the communication between Internet of Things (IoT) devices utilizing various secure communications, including Secure Shell (SSH), and analyzes their performance in a variety of circumstances. The gateway's experimental evaluation reveals that the proposed framework can secure tiny IoT devices. Index Terms— Internet of Things (IoT), Security IoT, Security Gateway, Security Settings on the Gateway for RaspberryPi, ESP8266.

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Section: E In-house Prototyping Of Internet-of-things Devices Using R...mentioning

confidence: 99%

Design and Implementation of Security Gatway for IoT Devices Security

2023

IJCCCE

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“…They can supervise external conditions (such as temperature, humidity, wind … etc.) for alarm systems [1] or greenhouse applications [2] and periodically transmit the recorded data to the base station. The sensor nodes have batteries as the energy sources and their batteries usually cannot be replaced after a deployment.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

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In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

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LSDStrategy: A Lightweight Software-Driven Strategy for Addressing Big Data Variety of Multimedia Streaming

Khudhur

Jeiad

2022

IEEE Access

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Many devices, users, and applications stream an irregular amount of varied data every second. This rapid generation of data continues at an enormous rate, constructing the big data that increase the need for solutions, despite resource constraints, to analyze and manipulate data. Current methods allocate cloud resources according to the characteristics of the data. Resource allocation requires a comprehensive view of the workload requirements. However, the data characteristics in big data streams are uncertain due to the random nature of data generation. Choosing and allocating the right resources to this stream is challenging. With the variety of big data streams, the stochastic nature of the stream led to unpredictable requirements and specifications. The critical issue is forecasting the workload to avoid the over-provisioning and under-provisioning of resources. Such forecasting needs an adequate dataset to describe the history logs of the incoming workload. A fast release for such a dataset provides a high chance of deploying forecasting at the right time. This paper addresses this issue with a novel strategy named LSDStrategy that analyzes the received multimedia stream based on its binary content using machine learning techniques with artificial and real datasets. LSDStrategy utilizes an evaluating voting technique to select the optimum classifier to trade off accuracy and prediction time as metrics. Multi-classifiers that have been built and tested include Decision Tree (DT), K-Nearest Neighbor (K-NN), and Random Forest (RF) over multi-content-based features. Experiments evaluated the performance of the adopted models and the selected features. According to experimental analysis, the DT approach provides a consistent performance for both artificial and realworld datasets for 85% and 81.3%, respectively. We deploy and evaluate the LSDStrategy efficiency on a regular specification server through a set of experiments using a synthetic stream. The experiments prove the LSDStrategy agility and adaptivity in identifying the multimedia-based workload type utilizing small chunks of load.

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Application of Self-Managing System in Greenhouse with Wireless Sensor Network

Cited by 3 publications

References 7 publications

Design and Implementation of Security Gatway for IoT Devices Security

Design and Implementation of Security Gatway for IoT Devices Security

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

LSDStrategy: A Lightweight Software-Driven Strategy for Addressing Big Data Variety of Multimedia Streaming

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