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Elmustafa Sayed Ali
Project Researcher Specialist at MASAD
Khartoum, Sudan
Elmustafa Sayed Ali Ahmed is currently working as Senior Lecturer in Electrical & Electronics Departments , Collage of Engineering - Red Sea University. He Received his M.Sc. Telecom in 2012, and B.Sc. (Honor) degree in electrical engineering, Telecom in 2008. Worked (former) as a senior engineer in Sudan Sea Port Corporation for five years and a team leader of new projects in wireless networks includes (Tetra system, Wi-Fi, Wi-Max, and CCTV). Worked (former) as a head of Electrical and Electronics Engineering department for one year in the Engineering Faculty , Red Sea University . Also worked (former) as a director of Marine Systems Management in Flamingo Maritime Enterprises CO.LTD. His main area of interest focuses on Wireless Networks, IoT, Digital Image Processing, Routing Protocols, Computer networks and Cloud Computing. His area of expertise includes IoT , Wireless Communication, Routing protocols, big data, and DIP. He has published more than 50 papers and book chapters in wireless communications, internet of things, AI based IoT in networking in peer reviewed academic international journals as author/co-author. Member of IEEE Communication Society (ComSoc), and International Association of Engineers (IAENG). Six Sigma Yellow Belt (SSYB) , and Scrum Fundamentals certified (SFC), and SEC.
Elmustafa Sayed Ali
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239
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239
Points based upon Thinkers360 patent-pending algorithm.
Exploratory Investigation for Some Universities’ E-Learning Systems during Covid-19 Pandemic
International Journal of Computer Science and Network Security
June 12, 2023
Green Machine Learning Approach for QoS Improvement in Cellular Communications
IEEE
August 26, 2022
Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due to the dynamic cellular network behaviors related to interference distribution, traffic bottlenecks, congestion points, and hotspots, there is a need to evaluate the dynamic processes in cellular systems in addition to ensuring spectrum availability. The delay, loss rate, and SNR are the most issues that may affect cellular communication performance. Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. It enables the extraction of spectrum knowledge from the network autonomously. The extracted information helps to know about every dynamic change in wireless parameters, related to frequency, modulation, route selection, etc. This paper provides details about the use of ML in green cellular networks to efficiently upgrade the communications and enhances different related approaches including quality of services (QoS), signal traffic load, and energy efficiency, which are critical issues of green cellular communication paradigms. The paper also presents the technical concept of green ML approaches to solve significant problems in cellular communications, in addition to future aspects and considerations for energy consumption minimization using the green ML approach in cellular radio communications.
Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization
electronics1, MDPI
July 10, 2022
Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms
Efficient Energy Mechanism in Heterogeneous WSNs for Underground Mining Monitoring Applications
IEEE Access
July 05, 2022
Wireless Sensor Networks (WSNs) play an important role in underground mining applications. In particular, they help to collect information using sensors and provide monitoring of complex mine environments to avoid potential risks and manage operations. Despite the importance of WSNs, they face the problem of energy consumption and the difficulty of replacing the batteries of the sensor nodes. The distributed energy-efficient aggregation protocol (DEECP) helps to reduce the power consumption of the WSN. This protocol enables an increase in the lifetime of a WSN. The DEECP algorithm uses the clustering concept and selects cluster heads (CHs) according to the election probability based on the ratio between the residual energy and network average energy of each node. However, this method does not provide an optimum solution because it does not take into account the different sensor energy levels. In addition, the algorithm does not consider the effect of the distance between the base station and sensor node likely be chosen to become a CH. This can significantly affect the performance of the WSN. This paper proposes an optimization threshold for CH selection based on three energy levels of a sensor, namely, low, high, and super as well as the measurement of the distances between base stations and possible nodes to be selected as CHs to optimize the CH selection method. The proposed approach is evaluated and compared with DEECP in terms of dead nodes, alive nodes, and network throughput. The results show that the proposed approach outperforms DEECPs in terms of network stability and lifetime.
Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends
Journal of Advanced Transportation
June 28, 2022
Green Machine Learning for Green Cloud Energy Efficiency
IEEE
May 23, 2022
The efficiency of energy in cloud computing (CC) is one of the critical and vital parameters for large server-based networks due to CC consuming a great amount of energy and the dramatic energy consumption growth in the area of digital services. Focus on cloud computing energy consumption and efficiency is one of the key issues in a modern data center structure for operation costs reduction and fulfilling the green computing goals. Machine learning plays an important role to optimize the operation of cloud communication to enhance energy efficiency. This paper provides an overview and details of employing machine learning (ML) to propose energy efficiency solutions in the green cloud communication environment. The paper also presents a background and literature view. Moreover, we will discuss the ML proposals for green cloud resources management
Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load
Wireless Communications and Mobile Computing
January 06, 2022
Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multi-point processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogeneous networks implementation approaches and the homogeneous networks’ typologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that inter site CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intra site CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intra site CoMP almost 10%, and inter site CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intra site CoMP and inter site CoMP has better fairness index than intra site CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.
A comprehensive review on the users' identity privacy for 5G networks
IET Communications
January 05, 2022
Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. Therefore, researchers have deliberated the privacy and security solution of 5G users. The 5G wireless network offers a futuristic concept that helps to solve challenges affecting previous communications generations. The key concern to many scholars in the field of mobile networking is user privacy, which is long-term subscription identifier as International Mobiles Subscribers Identifiers (IMSIs) and short-term subscription identifier as Temporary Mobiles Subscribers Identifiers and Cell-Radio Networks Temporary Identifiers (TMSIs and C-RNTIs), which are used for permanent identifying, paging, and location update. This article investigates the existing literature survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies that handle user identifications in authentication, paging, and location update. This article discusses the various privacy issues in the 5G network that use IMSI in clear text or temporary identities such as TMSI & C-RNTI with IMSI to disclose user identity privacy. This article also investigates the existing literature on user identity and location privacy and highlights the key parameters, issues, challenges, and future recommendations with potential solutions.
Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends
IET Communications
December 30, 2021
The Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud-radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues
Quality of Services Based on Intelligent IoT WLAN MAC Protocol Dynamic Real-Time Applications in Smart Cities
Computational Intelligence and Neuroscience
October 31, 2021
The future directions and challenges for 6G-enabled wireless communication for IoT applications are mainly focused on quality of service (QoS). The selection criteria of mobility management (MM) protocol are mainly the total duration of the delay and packet loss rate during the MM procedure. This is called intelligent handover (IH) to designate a relay with a minimum delay. To solve the problem of handover, media access control (MAC) protocols are used to provide an intelligent protocol for QoS in real-time application in mobility. Moreover, changing the parameter to find the best protocol for mobile stations in WLAN is a good choice. This paper proposed a new QoS structure for the point coordination function that is based on a new intelligent enhanced distribution coordination function that suites with dynamic real-time applications and services. The paper addresses the distributed coordination function (DCF) with QoS-based intelligent mobility management in stations and other scenarios with enhanced distribution coordination function (EDCF) to find the result of throughput, retransmission attempts, delay, and data droop. In this paper, the remote topology comprises a few remote stations and one base station within the remote LAN. All remote stations are found that each station can distinguish a transmission from any other station, and there is portability within the proposed intelligent framework.
Machine learning techniques in internet of UAVs for smart cities applications
Journal of Intelligent and Fuzzy Systems
August 01, 2021
Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends.
Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications
Security and Communication Networks
March 01, 2021
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.
An Enhanced Cooperative Communication Scheme for Physical Uplink Shared Channel in NB-IoT
Wireless Personal Communications
January 23, 2021
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.
A Systematic Review on Cognitive Radio in Low Power Wide Area Network for Industrial IoT Applications
Sustainability, MDPI
January 01, 2021
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
Machine Learning in Healthcare: Theory, Applications, and Future Trends
IGI global
August 26, 2022
Due to the increase in healthcare data provided by IoT, there is a need to use new methods for data analysis. Machine learning (ML) techniques promise solutions for many challenges facing the IoT-based healthcare services. MLs provide significant improvement in different IoT aspects related to storage size, computational power, and data transfer speeds. In addition, MLs provide a number of solutions for medical imaging, resources, medical data processing, detection, diagnosis, and prediction. Recently, many applications have appeared in the field of medicine and healthcare, which are closely related to the IoT. This chapter presents basic concepts related to the use of ML techniques, in addition to some algorithms used in the medical field and healthcare technology based on IoT devices and systems. Moreover, the chapter will discuss the ML
Blockchain for IoT-Based Cyber-Physical Systems (CPS): Applications and Challenges
In: De D., Bhattacharyya S., Rodrigues J.J.P.C. (eds) Blockchain based Internet of Things. Lecture Notes on Data Engineering and Communications Techno
January 01, 2022
Cyber-Physical System (CPS) enables to combine the physical objects with computing and storage capabilities to have data exchange in an interconnected network of systems and objects. Blockchain is a recently distributed computing paradigm that provides a promising solution for modern CPS application. It forms an underpinning technique for CPS that offers strong added value to industrial IoT (IIoT), fault-tolerant, reliable, secure, and efficient computing infrastructure. The inherent integration of consensus algorithms and distributed storage with advanced security protocols provides powerful solutions for CPS applications. Blockchains in CPSs/IoT ensure secure and saved information for different industrial applications and achieve a means of adaptability, process, and operation protection, for example, in manufacturing, transportation, health care, and energy applications. This chapter will provide extensive technical background for blockchain in IoT-based CPS. Applications, opportunities, and challenges for the combination of CPS, IoT, and blockchain were presented.
Cyber-Physical System for Smart Grid
In Research Anthology on Smart Grid and Microgrid Development, edited by Information Resources Management Association, IGI Global, 2022
January 01, 2022
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.
Algorithms Optimization for Intelligent IoV Applications
In book: Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive TechnologiesChapter: 1Publisher: IGI global
August 01, 2021
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.
Machine Learning for Industrial IoT Systems
In book: Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive TechnologiesChapter: 1Publisher: IGI global
August 01, 2021
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.
Terahertz Communication Channel Characteristics and Measurements
In book: Next Generation Wireless Terahertz Communication Networks, CRC Press, USA
June 01, 2021
Deep Learning Approaches for IoV Applications and Services
In book: Intelligent Technologies for Internet of Vehicles, Springer
June 01, 2021
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.
Machine Learning Technologies in Internet of Vehicles
In book: Intelligent Technologies for Internet of Vehicles, Springer
June 01, 2021
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.
Cyber-Physical System for Smart Grid
In book: Artificial Intelligence Paradigms for Smart Cyber-Physical SystemsPublisher: IGI global
November 01, 2020
Machine Learning in Cyber-Physical Systems in Industry 4.0
In book: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems Publisher: IGI global
November 01, 2020
Smart IDS and IPS for Cyber-Physical Systems
In book: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems Publisher: IGI global
November 01, 2020
NB-IoT: concepts, applications, and deployment challenges
In book: LPWAN Technologies for IoT and M2M Applications, Elsevier
January 01, 2020
Narrowband-Internet of Things (NB-IoT) is a standard-based low-power wide-area network (LPWAN) technology developed to connect a wide range of new IoT devices and services. The NB-IoT will improve the power consumption of user devices, system capacity, and spectrum efficiency. It is able to be loaded by major mobile equipment and module manufacturers, and indeed it will be existing to be adaptable with any cellular mobile network’s generations. It also benefits from all the security and privacy features of mobile networks. In this chapter, fundamental key aspects of NB-IoT are investigated and the features of NB-IoT and technical properties are proposed in addition to the theoretical concepts. A detailed overview of the current NB-IoT-related technologies such as Long-Term Evolution for Machines (LTE-M), Third Generation Partnership Project (3GPP), enhanced Machine Type Communication (eMTC) and ultralow power technologies is discussed. This chapter will also provide details of different applications related to NB-IoT such as smart grid, smart cities, and smart industry. Security issues and other deployment considerations will be explored.
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