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    • Mohsen Mokhtar Guizani
    • Mohsen Mokhtar Guizani: Influence Statistics

      Mohsen Mokhtar Guizani

      Mohsen Mokhtar Guizani

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      Department of Computer Science and Engineering, Qatar University, Doha, Qatar. | Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu ...

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      Mohsen Mokhtar Guizani:Expert Impact

      Concepts for whichMohsen Mokhtar Guizanihas direct influence:Smart grid,Federated learning,Vehicular networks,Sensor networks,Wireless sensor networks,Smart cities,Edge computing,Big data.

      Mohsen Mokhtar Guizani:KOL impact

      Concepts related to the work of other authors for whichfor which Mohsen Mokhtar Guizani has influence:Internet things,Smart grid,Machine learning,Wireless sensor networks,Edge computing,Iot devices,Big data.

      KOL Resume for Mohsen Mokhtar Guizani


      Department of Computer Science and Engineering, Qatar University, Doha, Qatar.

      College of Engineering, Qatar University, Qatar

      Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, United Arab Emirates


      CSE Department, Qatar University, Qatar

      Machine Learning Department, Mohamed Bin Zayed University Of Artificial Intelligence, Abu Dhabi, 51133, UAE

      Department of Electrical and Computer Engineerin, University of Idaho, MS, Idaho, United States, (e-mail:


      Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar;,

      ECE, University of Idaho, 5640 Moscow, Idaho United States 83844-1023 (e-mail:

      College of Engineering, Qatar University

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      Sample of concepts for which Mohsen Mokhtar Guizani is among the top experts in the world.
      Concept World rank
      framework sharing #1
      vehicles authentication #1
      predefined sensing capabilities #1
      centric internet #1
      malicious mining code #1
      pdodc #1
      design issues opportunities #1
      marymcm #1
      alloptical dwdm networks #1
      forensics process #1
      chunks videos #1
      fcns uavs #1
      sebexp3 #1
      iot applications massive #1
      proposed method dsst #1
      common communication point #1
      prestige‐based edge computing #1
      proposed schemes ciot #1
      authentication scheme access #1
      potential massive #1
      vehicles time delay #1
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      domains domain trust #1
      reputation management schemes #1
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      self‐driving vehicles #1
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      antiinterference scheme marymcm #1
      macrocelltier #1
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      machine learning hiot #1
      trusted authority #1
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      algorithm cpu #1
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      Prominent publications by Mohsen Mokhtar Guizani

      KOL-Index: 12714

      Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in ...

      Known for Charging Station | Uavs Uav | Disaster Management | Unmanned Aerial Vehicles | Security Swarm
      KOL-Index: 11642

      This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as ...

      Known for Enabling Technologies | Protocols Iot | Paper Overview | Smart Sensors | New Applications
      KOL-Index: 11120

      Due to the rapid development of wireless access technologies and smart terminals, mobile data traffic is continuously increasing, which is expected to lead to an explosive growth of data in heterogeneous networks especially cellular networks. It is significant for network operators to expand the capacity of cellular networks to avoid congestion and overload so as to guarantee users' satisfaction. Given that contemporary terminals are capable of both WiFi and cellular networks, WiFi ...

      Known for Heterogeneous Networks | Wifi Offloading | Existing Solutions | Explosive Growth | Network Operators
      KOL-Index: 9484

      The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency ...

      Known for Edge Computing | Industrial Internet | Things Environment | Big Data | Cloud Interplay
      KOL-Index: 9340

      In wavelength division multiplexing (WDM) all-optical networks, the size of a request stream may be less than the maximum capacity of a lightpath. To avoid assigning an entire lightpath to a small request, many researchers have looked at adding traffic grooming to the routing and wavelength assignment (RWA) problem. In this work, we consider the RWA problem with traffic grooming (GRWA) for mesh networks. The GRWA problem is NP-Complete since it is a generalization of the RWA problem ...

      Known for Traffic Grooming | Optical Networks | Wavelength Assignment | Rwa Problem | Blocking Performance
      KOL-Index: 9267

      Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labeled IoT data from multiple entities, raising great concerns about data ...

      Known for Iot Data | Vector Machine | Smart Cities | Proposed Scheme | Svm Training
      KOL-Index: 9140

      Green radio communications has received a lot of attention in recent years due to its impact on telecom business, technology and environment. On the other hand, energy harvesting communication has emerged as a potential candidate to reduce the communication cost by tackling the problem in a contrasting fashion. While green communication techniques focus on minimizing the use of radio resources, energy harvesting communication relies on environment friendly techniques to generate energy ...

      Known for Energy Harvesting | Wireless Communications | Resource Allocation | Major Work | Paradigm Shift
      KOL-Index: 9109

      The fundamental role of the software defined networks (SDNs) is to decouple the data plane from the control plane, thus providing a logically centralized visibility of the entire network to the controller. This enables the applications to innovate through network programmability. To establish a centralized visibility, a controller is required to discover a network topology of the entire SDN infrastructure. However, discovering a network topology is challenging due to: 1) the frequent ...

      Known for Topology Discovery | Software Defined Networks | Data Plane | Entire Network | Comprehensive Survey
      KOL-Index: 9087

      Compressive sensing (CS) provides a new paradigm for efficient data gathering in wireless sensor networks (WSNs). In this paper, with the assumption that sensor data is sparse we apply the theory of CS to data gathering for a WSN where n nodes are randomly deployed. We investigate the fundamental limitation of data gathering with CS for both single-sink and multi-sink random networks under protocol interference model, in terms of capacity and delay. For the single-sink case, we present a ...

      Known for Data Gathering | Compressive Sensing | Wireless Sensor Networks | Proposed Scheme | Capacity Delay
      KOL-Index: 9060

      Internet of Things (IoT) has gained extensive attention from industry and academia alike in past decade. The connectivity of each and every piece of technology in the environment with Internet, has opened many avenues of research and development. Applications, algorithms, trust models, devices, all have evolved to accommodate the demands of user needs in the most optimal way possible. However, one thing still remains constant: host-centric communication. It is the most predominant way of ...

      Known for Internet Things | Iot Icn | Centric Networking | Extensive Attention | Based Security
      KOL-Index: 9025

      The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, ...

      Known for Iot Security | Deep Learning | Internet Things | Smart Devices | Access Control
      KOL-Index: 8907

      Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in smart city. Recently, the research community in the field of IoT Security endeavor hard to build anomaly, intrusion and cyber attacks traffic identification model using Machine Learning (ML) algorithms for IoT security analysis. However, the critical and significant problem still not studied in depth that is how to select an effective ML algorithm when there are numbers of ML algorithms for ...

      Known for Smart City | Machine Learning | Iot Attacks | Internet Things | Traffic Identification
      KOL-Index: 8664

      In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a ...

      Known for Iot Data | Deep Learning | Streaming Analytics | Sensing Devices | Promising Approach
      KOL-Index: 8603

      The dissemination of patients’ medical records results in diverse risks to patients’ privacy as malicious activities on these records cause severe damage to the reputation, finances, and so on of all parties related directly or indirectly to the data. Current methods to effectively manage and protect medical records have been proved to be insufficient. In this paper, we propose MeDShare, a system that addresses the issue of medical data sharing among medical big data custodians in a ...

      Known for Medical Data | Cloud Service | Patients Privacy | Malicious Activities | Trustless Environment
      KOL-Index: 8584

      We study the throughput capacity of hybrid wireless networks with a directional antenna. The hybrid wireless network consists of n randomly distributed nodes equipped with a directional antenna, and m regularly placed base stations connected by optical links. We investigate the ad hoc mode throughput capacity when each node is equipped with a directional antenna under an L-maximum-hop resource allocation. That is, a source node transmits to its destination only with the help of normal ...

      Known for Directional Antenna | Hybrid Wireless Networks | Throughput Capacity | Base Stations | Normal Nodes

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      Department of Computer Science and Engineering, Qatar University, Doha, Qatar. | Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates | Machine Learning Department, Mohamed bin Zayed Un

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