GHAIHAB HASSAN ADDAY
@faculty.uobasrah.edu.iq
Department of Computer Science, College of Computer Science and Information Technology
Department of Computer Science, College of Computer Science and Information Technology, University of Basrah, Basrah 61004, Iraq.
GHAIHAB HASSAN ADDAY was born in Basrah City, Iraq, in 1981. He received a B.S. degree in computer science from the University of Basrah, Basrah, Iraq, in 2008 and an M.S. in routing in wireless networks from the University of Basrah, Basrah, Iraq, in 2012.
From 20012 to 20014, he was a Lecturer Assistant in the Computer Science Department, College of Science, University of Basrah, Basrah, Iraq. Since 2014, he has been a Lecturer in the Computer Science Department, College of Computer Science and Information Technology, University of Basrah, Basrah, Iraq. From 2016 to 2019, he was Secretary of the College Council, College of Computer Science and Information Technology University of Basrah, Basrah, Iraq. From 2019 to 2020, he was an Assistant Dean for the Administrative and Financial Affairs College of Computer Science and Information Technology, University of Basrah, Basrah, Iraq.
Ghaihab is currently pursuing a Ph.D. in Wireless Sensor Networks from the University Putra Malaysia (UPM)
EDUCATION
MSc. in Computer Science
2010 - 2012
University of Basrah, Basrah, Iraq.
BSc. In Computer Science
2004 - 2008
University of Basrah, Basrah, Iraq.
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Networks and Communications, Hardware and Architecture, Computer Science Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ghaihab Hassan Adday, Shamala K. Subramaniam, Zuriati Ahmad Zukarnain, and Normalia Samian
Institute of Electrical and Electronics Engineers (IEEE)
Wireless Sensor Networks (WSNs) serve as the backbone for the Internet of Things (IoT) and smart cities, enabling the gathering of essential data and vital information. The widespread deployment of sensors created new scenarios that presented new domains for various applications. Due to their numerous advantages, WSN’s simulators are still gaining considerable attention as the primary method for testing and evaluating new protocols and approaches for WSN. The scientific community has developed various simulators, some tailored explicitly for WSNs and others for general use. In addition, an old but renovated direction has been enormously grown recently, representing the researcher’s attention to building its own simulator. However, many researchers struggle to select the most appropriate tool for performance analysis, which requires extensive research into available options. Choosing a suitable simulator to meet the necessary simulation criteria necessitates exhaustive research into the available options. The published studies on WSN’s simulators have limitations, such as the limited number of simulators under examination, ignoring the essential functions of the simulators, and inadequate performance criteria for precise comparison. To get beyond these restrictions in previous studies and offer a fresh study that compares the simulation tools comprehensively, an analysis of thirty-three different simulators was conducted based on a new taxonomy. Furthermore, this study examines the advantages and constraints of each simulator regarding many specific academic areas in WSNs. Moreover, the study presented a detailed comparison among the performance analysis tools according to many famous performance metrics.
Ghaihab Hassan Adday, Shamala K. Subramaniam, Zuriati Ahmad Zukarnain, and Normalia Samian
Institute of Electrical and Electronics Engineers (IEEE)
Event-driven Wireless Sensor Networks (WSNs) consist of thousands of tiny nodes. Sensor nodes are prone to faults because of their fragility and the fact that they are typically placed in harsh environments. Erroneous readings pose a high risk in many situations and affect the network’s reliability, necessitating a solution to distinguish between true and faulty events. In response to this challenge, this work proposes the Friendship Degree and Tenth Man Strategy (FD-TMS) method for true event detection in WSNs. This new method can differentiate between erroneous readings and true events in a distributed manner. The FD idea has previously been used to solve security problems, while military intelligence operations have inspired the TMS and have never been used in WSNs. The FD-TMS consists of two stages. In the first stage, it employs a majority voting approach considering the friendship degree among voters. Voting among only trustworthiness nodes with high FD values will effectively differentiate true events and incorrect measurements. The second stage will validate the voting process through a novel perspective based on the TMS. TMS will check the voters’ replies based on the event’s location. The proposed method will delete erroneous readings, while only the true event reports will be reported. FD-TMS was comprehensively assessed in a simulation environment utilizing a performance analysis tool constructed on Java. The results were compared to the baseline algorithm, highlighting key parameters like false alarms and event detection accuracy. The simulation results demonstrated the proposed approach significantly enhanced the performance of the baseline works.
Ghaihab Hassan Adday, Shamala K. Subramaniam, Zuriati Ahmad Zukarnain, and Normalia Samian
MDPI AG
The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates. The Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks (WSNs), is an important subsection component of the IR 4.0. WSNs are a good demonstration of an ambient intelligence vision, in which the environment becomes intelligent and aware of its surroundings. WSN has unique features which create its own distinct network attributes and is deployed widely for critical real-time applications that require stringent prerequisites when dealing with faults to ensure the avoidance and tolerance management of catastrophic outcomes. Thus, the respective underlying Fault Tolerance (FT) structure is a critical requirement that needs to be considered when designing any algorithm in WSNs. Moreover, with the exponential evolution of IoT systems, substantial enhancements of current FT mechanisms will ensure that the system constantly provides high network reliability and integrity. Fault tolerance structures contain three fundamental stages: error detection, error diagnosis, and error recovery. The emergence of analytics and the depth of harnessing it has led to the development of new fault-tolerant structures and strategies based on artificial intelligence and cloud-based. This survey provides an elaborate classification and analysis of fault tolerance structures and their essential components and categorizes errors from several perspectives. Subsequently, an extensive analysis of existing fault tolerance techniques based on eight constraints is presented. Many prior studies have provided classifications for fault tolerance systems. However, this research has enhanced these reviews by proposing an extensively enhanced categorization that depends on the new and additional metrics which include the number of sensor nodes engaged, the overall fault-tolerant approach performance, and the placement of the principal algorithm responsible for eliminating network errors. A new taxonomy of comparison that also extensively reviews previous surveys and state-of-the-art scientific articles based on different factors is discussed and provides the basis for the proposed open issues.