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Elmustafa Sayed Ali Ahmed : Received his M.Sc. degree in electronic engineering, Telecommunication in 2012, and B.Sc. (Honor) degree in electrical engineering, Telecommunication in 2008.He published papers, and book chapters in wireless communications, and networking in peer reviewed academic international journals. Research interest include, routing protocols, communication system and networks, image processing, IoT and cloud computing. member of IEEE Communication Society (ComSoc), and International Association of Engineers (IAENG). Six Sigma Yellow Belt (SSYB) , and Scrum Fundamentals certified (SFC)
Nowadays, many wireless communication applications and services demand high capacity and bandwidth. Experts expect that in next few years, communications markets need to use high data rates up to 40 Gbps. For meeting the requirements of high data rate, bands up to 3 THz hold promise as they can achieve data rates up to a few tens of Gbps. The bands ranging from 275 to 3,000 GHz are known as terahertz (THz) bands. THz bands are not assigned yet globally for dedicated services and communication applications. The implementation of THz communication requires various kinds of evaluations and effort in order to assess different aspects related to channel characterizations, THz system designs, propagation, algorithms and techniques for signal processing, platform configurations, in addition to security and privacy issues. The evaluation and assessment should also consider the statistical parameters and measurements of the channels. This chapter introduces the key aspects of THz channel specifications and related considerations. In addition, a comparison between mm-waves and THz channel characteristics is made. The chapter also discusses the theoretical concepts of THz channel characterizations, limitations, requirements, and methodologies for measurements including a comparison between different channel analyses. Moreover, the chapter provides a brief description of THz channel characterization in nano communications.
Elmustafa Sayed Ali; Mona Bakri Hassan; Rashid A. Saeed. Terahertz Communication Channel Characteristics and Measurements. Next Generation Wireless Terahertz Communication Networks 2021, 133 -167.
AMA StyleElmustafa Sayed Ali, Mona Bakri Hassan, Rashid A. Saeed. Terahertz Communication Channel Characteristics and Measurements. Next Generation Wireless Terahertz Communication Networks. 2021; ():133-167.
Chicago/Turabian StyleElmustafa Sayed Ali; Mona Bakri Hassan; Rashid A. Saeed. 2021. "Terahertz Communication Channel Characteristics and Measurements." Next Generation Wireless Terahertz Communication Networks , no. : 133-167.
Terahertz (THz) band communications enables high data traffic and superior communication speed for future generation technologies. However, many challenges have not been yet studied for this technology. These challenges relate to the power limits and propagation losses that govern the use of THz in short-range communications. The use of ultra-massive multiple input multiple output (UM-MIMO) antenna systems also reduce the THz band propagation loss and which leads to improve system capacity dramatically. This chapter will introduce the key fundamental aspects of UM-MIMO antenna system and plasmonic nano-antenna array. Due to the very small size of the THz plasmonic antenna, the properties of nanomaterial will be achieved, which enables the deployment of large plasmonic arrays in very small areas. The chapter discusses many design aspects, mathematical model, implementation challenges and applications of THz ultra-massive MIMO.
Mona Bakri Hassan; Elmustafa Sayed Ali; Rashid A. Saeed. Ultra-Massive MIMO in THz Communications. Next Generation Wireless Terahertz Communication Networks 2021, 267 -297.
AMA StyleMona Bakri Hassan, Elmustafa Sayed Ali, Rashid A. Saeed. Ultra-Massive MIMO in THz Communications. Next Generation Wireless Terahertz Communication Networks. 2021; ():267-297.
Chicago/Turabian StyleMona Bakri Hassan; Elmustafa Sayed Ali; Rashid A. Saeed. 2021. "Ultra-Massive MIMO in THz Communications." Next Generation Wireless Terahertz Communication Networks , no. : 267-297.
The term Internet of Energy (IoE) was firstly inspired in the third industrial revolution by, which refers to an electricity solution for power flow and bidirectional information in an internet-style, which is also known as energy internet and it considered as a smart grid extension. The need for continuous development in energy management has different factors indeed. Starting by development in energy generation techniques from traditional ways to modern renewable techniques. Then distribution and transformation development will be also required to compatible forms with that developed energy generation techniques. Traditionally, energy systems deploy generation, transmission, and distribution. Then IoE is invented as an information and communications technology solution to add a communication layer or functionality that integrates all system components together in end-to-end fashion, while providing other system services.
Rania Salih Abdalla; Sara A. Mahbub; Rania A. Mokhtar; Elmustafa Sayed Ali; Rashid A. Saeed. IoE Design Principles and Architecture. Internet of Energy for Smart Cities 2021, 145 -170.
AMA StyleRania Salih Abdalla, Sara A. Mahbub, Rania A. Mokhtar, Elmustafa Sayed Ali, Rashid A. Saeed. IoE Design Principles and Architecture. Internet of Energy for Smart Cities. 2021; ():145-170.
Chicago/Turabian StyleRania Salih Abdalla; Sara A. Mahbub; Rania A. Mokhtar; Elmustafa Sayed Ali; Rashid A. Saeed. 2021. "IoE Design Principles and Architecture." Internet of Energy for Smart Cities , no. : 145-170.
Internet of vehicles (IoV) has become an important revolution of intelligent transportation system (ITS). It became an emerging research area as the need for it has increased tremendously. With a great number of applications available, in addition to the intention to improve the quality of life and quality of services, the application of artificial intelligence (AI) techniques would dramatically enhance the performance of the IoV overall system. This chapter will discuss deep learning networks as a type of machine learning use in IoV with influence of Neural Networks (NN), where great amounts of unlabeled data are processed, classified and clustered. Deep learning network approaches i.e., Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Reinforcement Learning (DRL), classification, clustering, and predictive analysis (regression) will briefly discussed in this chapter, in addition to review its ability to obtain better performing IoV applications.
Lina Elmoiz Alatabani; Elmustafa Sayed Ali; Rashid A. Saeed. Deep Learning Approaches for IoV Applications and Services. Smart Sensors, Measurement and Instrumentation 2021, 253 -291.
AMA StyleLina Elmoiz Alatabani, Elmustafa Sayed Ali, Rashid A. Saeed. Deep Learning Approaches for IoV Applications and Services. Smart Sensors, Measurement and Instrumentation. 2021; ():253-291.
Chicago/Turabian StyleLina Elmoiz Alatabani; Elmustafa Sayed Ali; Rashid A. Saeed. 2021. "Deep Learning Approaches for IoV Applications and Services." Smart Sensors, Measurement and Instrumentation , no. : 253-291.
Recently, there was much interest in Technology which has emerged greatly to the development of smart cars. Internet of Vehicle (IoV) enables vehicles to communicate with public networks and interact with surrounding environment. It also enables vehicles to exchange information in addition to collect information about other vehicles and roads. However, actual applications of smart IoV systems face many challenges. These challenges are related to different problematic issues like big data connection with IoV, cloud network, data processing, and efficient communication between a large amount of different vehicles types, in addition to optimum decision data processing on or off board. Intelligence of the huge amount of data that can be processed to reduce road congestion and improve traffic management, as well as ensuring road safety is an important issue in future IoV trends. Artificial Intelligence (AI) technology with Machine Learning (ML) mechanisms offers smart solutions that can improve IoV network efficiency. For example, decision for data processing at various layers i.e. on-board units (OBUs), Fog level or cloud level are one of the problems which need ML algorithms. Other critical issues that can be resolved by ML mechanisms are time, energy, rapid topology of IoV, optimization quality of experience (QoE) and channel modeling. These issues need to be optimized. This chapter provides theoretical fundamentals for ML models, algorithms in IoV applications and future directions.
Elmustafa Sayed Ali; Mona Bakri Hassan; Rashid A. Saeed. Machine Learning Technologies in Internet of Vehicles. Smart Sensors, Measurement and Instrumentation 2021, 225 -252.
AMA StyleElmustafa Sayed Ali, Mona Bakri Hassan, Rashid A. Saeed. Machine Learning Technologies in Internet of Vehicles. Smart Sensors, Measurement and Instrumentation. 2021; ():225-252.
Chicago/Turabian StyleElmustafa Sayed Ali; Mona Bakri Hassan; Rashid A. Saeed. 2021. "Machine Learning Technologies in Internet of Vehicles." Smart Sensors, Measurement and Instrumentation , no. : 225-252.
Underwater wireless communications play a vital role in marine applications such as oceans' monitoring, exploration, and scientific data collection. Underwater wireless network (UWN) is an important area for the research due to the unique and harsh underwater environment and application which are quite challenging and problematics issues still remain to be addressed. Hence, intelligent solutions are needed for various levels of network architecture. Recently, many academic and industrial researchers have paid attention to the development of state-of-the-art intelligent solutions for future underwater wireless communications and networks. This chapter enlightens and guides the potential research communities about the recent progress in the area of intelligent UWC. This chapter also reviews underwater wireless networks based on acoustics, even electromagnetic, and optical signals. UWN systems also are reviewed for data gathering and recharge docking systems. Intelligent methodologies and modelings are the key drivers for future intelligent underwater communication.
Elmustafa Sayed Ali; Rashid A. Saeed. Intelligent underwater wireless communications. Intelligent Wireless Communications 2021, 271 -305.
AMA StyleElmustafa Sayed Ali, Rashid A. Saeed. Intelligent underwater wireless communications. Intelligent Wireless Communications. 2021; ():271-305.
Chicago/Turabian StyleElmustafa Sayed Ali; Rashid A. Saeed. 2021. "Intelligent underwater wireless communications." Intelligent Wireless Communications , no. : 271-305.
Artificial intelligence (AI) and Internet of things (IoT) are both become most amazing technologies nowadays. Artificial intelligence deals with intelligent reasoning and speedy data analysis which would cover the smartest future applications. Internet of things promises the deployment of these smart applications everywhere in the world. Both AI and the IoT interact in a unique way to increase increased human-machine interaction efficiency, increased operational efficiency, and true digital transformation. In IoT, the AI can ensure to collect only adequate data in order to reduce the processing time of big data amount sent by sensors. Artificial intelligence processes such as data integration, data selection, data cleaning, data transformation, data mining, and pattern evaluation all will provide the best solution to manage huge data flows and storage in the IoT network. The use of AI in IoT will extract the unique capability in different intelligent aspects such as smart decisions, smart metering, and forecasting and these will generate a new intelligent application in industrial security, health care, and smart homes. In this chapter, we will provide a brief concept about the AI and its relationship to the IoT, in addition to the benefits of AI to solve many challenges in IoT operations such as sensing, computing, energy management, and security. The chapter will also provide different types of AI methodologies related to the IoT applications.
Mona Bakri Hassan; Elmustafa Sayed Ali; Nahla Nurelmadina; Rashid A. Saeed. Artificial intelligence in IoT and its applications. Intelligent Wireless Communications 2021, 33 -58.
AMA StyleMona Bakri Hassan, Elmustafa Sayed Ali, Nahla Nurelmadina, Rashid A. Saeed. Artificial intelligence in IoT and its applications. Intelligent Wireless Communications. 2021; ():33-58.
Chicago/Turabian StyleMona Bakri Hassan; Elmustafa Sayed Ali; Nahla Nurelmadina; Rashid A. Saeed. 2021. "Artificial intelligence in IoT and its applications." Intelligent Wireless Communications , no. : 33-58.
Nowadays, intelligence Internet of things (IoT) wireless networks are the most promising technologies for intelligent future applications, since it takes the advantages of artificial intelligence for sensing and data analysis to develop a new generation of smarter IoT applications around everywhere. Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decisionmaking so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. The main challenge for future IoT networks is how to cope with such complex systems, where a huge number of devices compete for limited wireless resources and where heterogeneity is everincreasing. There is an urgent need for more intelligent networks that lead to more interoperable solutions and that can make autonomous decisions on optimal operation modes and configurations. In IoT wireless networks, the AI can insure to collect only adequate data in order to reduce the processing time of big data amount sent by sensors. To manage big data and store it in IoT networks, AI processes play a major role in data integration, selection, classification, and mining, as well as assessment of information patterns. It offers unique solutions in decision-making, measurement, and contribution to the construction of IoT networks with a new, fast, and intelligent character. In this chapter, we will provide intelligence IoT wireless networks in addition to the AI contribution in programming and configuring of IoT network devices. This chapter will also introduce the algorithms and strategy of intelligence related to cognitive IoT networks and quality of service, in addition to the benefits of cognitive and SDN to IoT operations in heterogeneous networks.
Mona Bakri Hassan; Elmustafa Sayed Ali; Rashid A. Saeed. Intelligent Internet of things in wireless networks. Intelligent Wireless Communications 2021, 135 -162.
AMA StyleMona Bakri Hassan, Elmustafa Sayed Ali, Rashid A. Saeed. Intelligent Internet of things in wireless networks. Intelligent Wireless Communications. 2021; ():135-162.
Chicago/Turabian StyleMona Bakri Hassan; Elmustafa Sayed Ali; Rashid A. Saeed. 2021. "Intelligent Internet of things in wireless networks." Intelligent Wireless Communications , no. : 135-162.
Smart public safety network is one of the hot topics in wireless communication. This chapter presents the design and development of first responders network (First Net) which provides broadband spectrum to the emergency service personnel at the time of disaster in a national level. In the same direction, the proposed design solves the problems of radio networks' interoperability. The smart public safety network uses incompatible radio systems and technologies in different frequencies to coordinate and collaborate works without any interferences. Smart radio network technologies aim to optimize the connections between different public safety organizations. An adaptive controlled system for different two-way radio networks interoperability is designed to increase the trust and reliability at the disaster and crisis times. The design usually depends upon the infrastructure and environmental condition of radio networks that already exist in the country. For simplicity, the presented design is focused on voice communications by accessing audio through commonly featured radio sockets. On the other hand, the existence of the third parties in the system such as the Internet, cellular, or even a console in large twoway radio systems reduces the reliability of the whole design and increases the cost and lack of time.
Adil Akasha; Rashid A. Saeed; Elmustafa Sayed Ali. Smart interoperability public safety wireless network. Intelligent Wireless Communications 2021, 399 -418.
AMA StyleAdil Akasha, Rashid A. Saeed, Elmustafa Sayed Ali. Smart interoperability public safety wireless network. Intelligent Wireless Communications. 2021; ():399-418.
Chicago/Turabian StyleAdil Akasha; Rashid A. Saeed; Elmustafa Sayed Ali. 2021. "Smart interoperability public safety wireless network." Intelligent Wireless Communications , no. : 399-418.
Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.
Elmustafa Sayed Ali; Mohammad Kamrul Hasan; Rosilah Hassan; Rashid A. Saeed; Mona Bakri Hassan; Shayla Islam; Nazmus Shaker Nafi; Savitri Bevinakoppa. Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications. Security and Communication Networks 2021, 2021, 1 -23.
AMA StyleElmustafa Sayed Ali, Mohammad Kamrul Hasan, Rosilah Hassan, Rashid A. Saeed, Mona Bakri Hassan, Shayla Islam, Nazmus Shaker Nafi, Savitri Bevinakoppa. Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications. Security and Communication Networks. 2021; 2021 ():1-23.
Chicago/Turabian StyleElmustafa Sayed Ali; Mohammad Kamrul Hasan; Rosilah Hassan; Rashid A. Saeed; Mona Bakri Hassan; Shayla Islam; Nazmus Shaker Nafi; Savitri Bevinakoppa. 2021. "Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications." Security and Communication Networks 2021, no. : 1-23.
There were errors in the first and fourth authors' names in the initial online publication. The original article has been corrected.
Mona Bakri Hassan; Sameer Alsharif; Hesham Alhumyani; Elmustafa Sayed Ali; Rania A. Mokhtar; Rashid A. Saeed. Correction to: An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB‑IoT. Wireless Personal Communications 2021, 1 -1.
AMA StyleMona Bakri Hassan, Sameer Alsharif, Hesham Alhumyani, Elmustafa Sayed Ali, Rania A. Mokhtar, Rashid A. Saeed. Correction to: An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB‑IoT. Wireless Personal Communications. 2021; ():1-1.
Chicago/Turabian StyleMona Bakri Hassan; Sameer Alsharif; Hesham Alhumyani; Elmustafa Sayed Ali; Rania A. Mokhtar; Rashid A. Saeed. 2021. "Correction to: An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB‑IoT." Wireless Personal Communications , no. : 1-1.
Narrowband-IoT (NB-IoT) is a standard-based Low Power Wide Area Network technology developed to connect a wide range of new Internet of Things (IoT) devices and services. NB-IoT bandwidth is limited to a single narrow-band of 180 kHz. Although NB-IoT provides low-cost connectivity, it provides channel to large number of smart IoT installed in households, building etc. However, in NB-IoT systems, repeating same signal over additional period of time has been taken as a key technique to enhance radio coverage up to 20 dB compared to the conventional LTE. Performance of NB-IoT system optimization and modeling are still challenging particularly coverage improvement in the case of real applications. For example, the narrow bandwidth in IoT and low energy have led to problematic issues in communication between IoT devices and network station, which results in low transmitter channel quality. Repetition process is used in the paper to enhance coverage and throughput, however in mean time increase the number of repetitions demands high bandwidth. So, an enhanced cooperative relay is used with repetition to reduce the demanded bandwidth. In this paper, we proposed an enhanced repetitions cooperative process of narrowband physical uplink shared channel (NPUSCH). The NPUSCH is transmitted using one or more resource units (RUs) and each of these RUs are repeated up to 128 times to enhance coverage as well as to meet requirement of ultra-low end IoT. The optimum number of repetitions of identical slots for NPUSCH per RUs is calculated and then simulated. In addition, the paper describes the analytical simulation to evaluate the proposed repetition of cooperative process performance for LTE-NPUSCH channel. Results show dramatical enhancement of uplink NB-IoT channel quality where the performance evaluation metrics were BLER, data rate, system throughput, spectral efficiency and transmission delay. The enhanced cooperative communication scheme for NPUSCH transmission channel in NB-IoT is achieved an average 23% enhancement in overall network throughput.
Mona Bakri Hassan; Sameer Alsharif; Hesham Alhumyani; Elmustafa Sayed Ali; Rania A. Mokhtar; Rashid A. Saeed. An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB-IoT. Wireless Personal Communications 2021, 1 -20.
AMA StyleMona Bakri Hassan, Sameer Alsharif, Hesham Alhumyani, Elmustafa Sayed Ali, Rania A. Mokhtar, Rashid A. Saeed. An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB-IoT. Wireless Personal Communications. 2021; ():1-20.
Chicago/Turabian StyleMona Bakri Hassan; Sameer Alsharif; Hesham Alhumyani; Elmustafa Sayed Ali; Rania A. Mokhtar; Rashid A. Saeed. 2021. "An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB-IoT." Wireless Personal Communications , no. : 1-20.
Internet of vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. It is a part of the internet of things (IoT) which deals with vehicle communications. As vehicular nodes are considered always in motion, they cause frequent changes in the network topology. These changes cause issues in IoV such as scalability, dynamic topology changes, and shortest path for routing. In this chapter, the authors will discuss different optimization algorithms (i.e., clustering algorithms, ant colony optimization, best interface selection [BIS] algorithm, mobility adaptive density connected clustering algorithm, meta-heuristics algorithms, and quality of service [QoS]-based optimization). These algorithms provide an important intelligent role to optimize the operation of IoV networks and promise to develop new intelligent IoV applications.
Elmustafa Sayed Ali Ahmed; Zahraa Tagelsir Mohammed; Mona Bakri Hassan; Rashid A. Saeed. Algorithms Optimization for Intelligent IoV Applications. Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies 2021, 1 -25.
AMA StyleElmustafa Sayed Ali Ahmed, Zahraa Tagelsir Mohammed, Mona Bakri Hassan, Rashid A. Saeed. Algorithms Optimization for Intelligent IoV Applications. Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies. 2021; ():1-25.
Chicago/Turabian StyleElmustafa Sayed Ali Ahmed; Zahraa Tagelsir Mohammed; Mona Bakri Hassan; Rashid A. Saeed. 2021. "Algorithms Optimization for Intelligent IoV Applications." Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies , no. : 1-25.
A smart grid is an advanced utility, stations, meters, and energy systems that comprises a diversity of power processes of smart meters, and various power resources. The cyber-physical systems (CPSs) can play a vital role boosting the realization of the smart power grid. Applied CPS techniques that comprise soft computing methods, communication network, management, and control into a smart physical power grid can greatly boost to realize this industry. The cyber-physical smart power systems (CPSPS) are an effective model system architecture for smart grids. Topics as control policies, resiliency methods for secure utility meters, system stability, and secure end-to-end communications between various sensors/controllers would be quite interested in CPSPS. One of the essential categories in CPSPS applications is the energy management system (EMS). The chapter will spotlight the model and design the relationship between the grid and EMS networks with standardization. The chapter also highlights some necessary standards in the context of CPSPS for the grid infrastructure.
Nagi Faroug M. Osman; Ali Ahmed A. Elamin; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. Cyber-Physical System for Smart Grid. Advances in Systems Analysis, Software Engineering, and High Performance Computing 2021, 301 -323.
AMA StyleNagi Faroug M. Osman, Ali Ahmed A. Elamin, Elmustafa Sayed Ali Ahmed, Rashid A. Saeed. Cyber-Physical System for Smart Grid. Advances in Systems Analysis, Software Engineering, and High Performance Computing. 2021; ():301-323.
Chicago/Turabian StyleNagi Faroug M. Osman; Ali Ahmed A. Elamin; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. 2021. "Cyber-Physical System for Smart Grid." Advances in Systems Analysis, Software Engineering, and High Performance Computing , no. : 301-323.
The Industrial Internet of things (IIoT) helps several applications that require power control and low cost to achieve long life. The progress of IIoT communications, mainly based on cognitive radio (CR), has been guided to the robust network connectivity. The low power communication is achieved for IIoT sensors applying the Low Power Wide Area Network (LPWAN) with the Sigfox, NBIoT, and LoRaWAN technologies. This paper aims to review the various technologies and protocols for industrial IoT applications. A depth of assessment has been achieved by comparing various technologies considering the key terms such as frequency, data rate, power, coverage, mobility, costing, and QoS. This paper provides an assessment of 64 articles published on electricity control problems of IIoT between 2007 and 2020. That prepares a qualitative technique of answering the research questions (RQ): RQ1: “How cognitive radio engage with the industrial IoT?”, RQ2: “What are the Proposed architectures that Support Cognitive Radio LPWAN based IIOT?”, and RQ3: What key success factors need to comply for reliable CIIoT support in the industry?”. With the systematic literature assessment approach, the effects displayed on the cognitive radio in LPWAN can significantly revolute the commercial IIoT. Thus, researchers are more focused in this regard. The study suggests that the essential factors of design need to be considered to conquer the critical research gaps of the existing LPWAN cognitive-enabled IIoT. A cognitive low energy architecture is brought to ensure efficient and stable communications in a heterogeneous IIoT. It will protect the network layer from offering the customers an efficient platform to rent AI, and various LPWAN technology were explored and investigated.
Nahla Nurelmadina; Mohammad Hasan; Imran Memon; Rashid Saeed; Khairul Zainol Ariffin; Elmustafa Ali; Rania Mokhtar; Shayla Islam; Eklas Hossain; Arif Hassan. A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications. Sustainability 2021, 13, 338 .
AMA StyleNahla Nurelmadina, Mohammad Hasan, Imran Memon, Rashid Saeed, Khairul Zainol Ariffin, Elmustafa Ali, Rania Mokhtar, Shayla Islam, Eklas Hossain, Arif Hassan. A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications. Sustainability. 2021; 13 (1):338.
Chicago/Turabian StyleNahla Nurelmadina; Mohammad Hasan; Imran Memon; Rashid Saeed; Khairul Zainol Ariffin; Elmustafa Ali; Rania Mokhtar; Shayla Islam; Eklas Hossain; Arif Hassan. 2021. "A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications." Sustainability 13, no. 1: 338.
The use of AI algorithms in the IoT enhances the ability to analyse big data and various platforms for a number of IoT applications, including industrial applications. AI provides unique solutions in support of managing each of the different types of data for the IoT in terms of identification, classification, and decision making. In industrial IoT (IIoT), sensors, and other intelligence can be added to new or existing plants in order to monitor exterior parameters like energy consumption and other industrial parameters levels. In addition, smart devices designed as factory robots, specialized decision-making systems, and other online auxiliary systems are used in the industries IoT. Industrial IoT systems need smart operations management methods. The use of machine learning achieves methods that analyse big data developed for decision-making purposes. Machine learning drives efficient and effective decision making, particularly in the field of data flow and real-time analytics associated with advanced industrial computing networks.
Mona Bakri Hassan; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. Machine Learning for Industrial IoT Systems. Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies 2021, 336 -358.
AMA StyleMona Bakri Hassan, Elmustafa Sayed Ali Ahmed, Rashid A. Saeed. Machine Learning for Industrial IoT Systems. Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies. 2021; ():336-358.
Chicago/Turabian StyleMona Bakri Hassan; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. 2021. "Machine Learning for Industrial IoT Systems." Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies , no. : 336-358.
Cyber-physical systems (CPS) have emerged with development of most great applications in the modern world due to their ability to integrate computation, networking, and physical process. CPS and ML applications are widely used in Industry 4.0, military, robotics, and physical security. Development of ML techniques in CPS is strongly linked according to the definition of CPS that states CPS is the mechanism of monitoring and controlling processes using computer-based algorithms. Optimizations adopted with ML in CPS include domain adaptation and fine tuning of current systems, boosting, introducing more safety and robustness by detection and reduction of vulnerabilities, and reducing computation time in time-critical systems. Generally, ML helps CPS to learn and adapt using intelligent models that are generated from training of large-scale data after processing and analysis.
Rania Salih Ahmed; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. Machine Learning in Cyber-Physical Systems in Industry 4.0. Advances in Systems Analysis, Software Engineering, and High Performance Computing 2021, 20 -41.
AMA StyleRania Salih Ahmed, Elmustafa Sayed Ali Ahmed, Rashid A. Saeed. Machine Learning in Cyber-Physical Systems in Industry 4.0. Advances in Systems Analysis, Software Engineering, and High Performance Computing. 2021; ():20-41.
Chicago/Turabian StyleRania Salih Ahmed; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. 2021. "Machine Learning in Cyber-Physical Systems in Industry 4.0." Advances in Systems Analysis, Software Engineering, and High Performance Computing , no. : 20-41.
One of the most important requirements is security and accessibility efforts which are represented as a critical issue that should be considered in many applications for the purpose of system confidentiality and safety. To ensure the security of current and emerging CPSs by taking into consideration the unique challenges present in this environment, development of current security mechanisms should be further studied and deployed in a manner that make it becomes more compatible with CPS environment, introduce a safer environment and maintain the quality of service at the same time. Systems known as intrusion detection systems (IDS) and intrusion prevention systems (IPS) are the most common security mechanisms used in networking and communication applications. These systems are based on artificial intelligence (AI) where computer-based algorithms are used to analyze, diagnose, and recognize that threats pattern according to an expected suspicious pattern.
Sara A. Mahboub; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. Smart IDS and IPS for Cyber-Physical Systems. Advances in Systems Analysis, Software Engineering, and High Performance Computing 2021, 109 -136.
AMA StyleSara A. Mahboub, Elmustafa Sayed Ali Ahmed, Rashid A. Saeed. Smart IDS and IPS for Cyber-Physical Systems. Advances in Systems Analysis, Software Engineering, and High Performance Computing. 2021; ():109-136.
Chicago/Turabian StyleSara A. Mahboub; Elmustafa Sayed Ali Ahmed; Rashid A. Saeed. 2021. "Smart IDS and IPS for Cyber-Physical Systems." Advances in Systems Analysis, Software Engineering, and High Performance Computing , no. : 109-136.
Congestion is one of the problems that will reduce the performance of the networks. In wireless ad-hoc networks connected to the wired backbone through gateways. The performance will be affected due to the type of the routing protocol, and the queuing interfaces were used in the link between the wireless ad-hoc network and the wired backbone network through gateways, which will lead to delay in packet delivered ratio and packet loss in addition to reduce the network throughput. The technique of queue management is used to reduce the congestion rate to have successfully transferred data between any sources and destinations. In this paper, the effect of four queuing management algorithms, fairness queue (FQ), stochastic fairness queuing (SFQ), random early detection {RED) and random exponential marking (REM) will be evaluated on FTP application, based on IEEE802.11 MAC protocol. The algorithms will be simulated in the Hybrid wireless ad-hoc network using NS-2, and the results will be discussed in three scenarios; hybrid network, queuing area (gateways) and different bandwidth with dedicated simulation time, and packet size by measuring the packet delay, packet delivery ratio, and packet losses.
Ertshag Hamza; Honge Ren; Elmustafa Sayed Ali Ahmed; Xiaolong Zhu. Performance Evaluation of Queuing Management Algorithms in Hybrid Wireless Ad-Hoc Network. Programmieren für Ingenieure und Naturwissenschaftler 2018, 646 -655.
AMA StyleErtshag Hamza, Honge Ren, Elmustafa Sayed Ali Ahmed, Xiaolong Zhu. Performance Evaluation of Queuing Management Algorithms in Hybrid Wireless Ad-Hoc Network. Programmieren für Ingenieure und Naturwissenschaftler. 2018; ():646-655.
Chicago/Turabian StyleErtshag Hamza; Honge Ren; Elmustafa Sayed Ali Ahmed; Xiaolong Zhu. 2018. "Performance Evaluation of Queuing Management Algorithms in Hybrid Wireless Ad-Hoc Network." Programmieren für Ingenieure und Naturwissenschaftler , no. : 646-655.
Lana Ibrahim; Hana Osman; Abdala Osman; Elmustafa Sayed Ali. Impact of Power Consumption in Sensors Life Time for Wireless Body Area Networks. International Journal of Grid and Distributed Computing 2016, 9, 97 -108.
AMA StyleLana Ibrahim, Hana Osman, Abdala Osman, Elmustafa Sayed Ali. Impact of Power Consumption in Sensors Life Time for Wireless Body Area Networks. International Journal of Grid and Distributed Computing. 2016; 9 (8):97-108.
Chicago/Turabian StyleLana Ibrahim; Hana Osman; Abdala Osman; Elmustafa Sayed Ali. 2016. "Impact of Power Consumption in Sensors Life Time for Wireless Body Area Networks." International Journal of Grid and Distributed Computing 9, no. 8: 97-108.