Blending big data analytics : review on challenges and a recent study
- Amalina, Fairuz, Targio Hashem, Ibrahim, Azizul, Zati, Fong, Ang, Imran, Muhammad
- Authors: Amalina, Fairuz , Targio Hashem, Ibrahim , Azizul, Zati , Fong, Ang , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 3629-3645
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- Description: With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE.
- Authors: Amalina, Fairuz , Targio Hashem, Ibrahim , Azizul, Zati , Fong, Ang , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 3629-3645
- Full Text:
- Reviewed:
- Description: With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE.
Wireless powering internet of things with UAVs : challenges and opportunities
- Liu, Yalin, Dai, Hong-Ning, Wang, Qubeijian, Imran, Muhammad, Guizani, Nadra
- Authors: Liu, Yalin , Dai, Hong-Ning , Wang, Qubeijian , Imran, Muhammad , Guizani, Nadra
- Date: 2022
- Type: Text , Journal article , Review
- Relation: IEEE Network Vol. 36, no. 2 (2022), p. 146-152
- Full Text: false
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- Description: Unmanned aerial vehicles (UAVs) have the potential to overcome the deployment constraint of The Internet of Things (IoT) in remote or rural areas. Wirelessly powered communications (WPC) can address the battery limitation of IoT devices through transferring wireless power to IoT devices. The integration of UAVs and WPC, namely UAV-enabled wireless powering IoT (Ue-WPI-o T) can greatly extend the IoT applications from cities to remote or rural areas. In this article, we present a state-of-the-art overview of Ue-WPIoT by first illustrating the working flow of Ue-WPIoT and discussing the challenges. We then introduce the enabling technologies in realizing Ue-WPI-oT. Simulation results validate the effectiveness of the enabling technologies in Ue-WPIoT. We finally outline the future directions and open issues. © 1986-2012 IEEE.
Real-time big data processing for anomaly detection : a survey
- Ariyaluran Habeeb, Riyaz, Nasaruddin, Fariza, Gani, Abdullah, Targio Hashem, Ibrahim, Ahmed, Ejaz, Imran, Muhammad
- Authors: Ariyaluran Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Targio Hashem, Ibrahim , Ahmed, Ejaz , Imran, Muhammad
- Date: 2019
- Type: Text , Journal article , Review
- Relation: International Journal of Information Management Vol. 45, no. (2019), p. 289-307
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- Description: The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Ltd
- Authors: Ariyaluran Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Targio Hashem, Ibrahim , Ahmed, Ejaz , Imran, Muhammad
- Date: 2019
- Type: Text , Journal article , Review
- Relation: International Journal of Information Management Vol. 45, no. (2019), p. 289-307
- Full Text:
- Reviewed:
- Description: The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Ltd
Secure big data ecosystem architecture : challenges and solutions
- Anwar, Memoona, Gill, Asif, Hussain, Farookh, Imran, Muhammad
- Authors: Anwar, Memoona , Gill, Asif , Hussain, Farookh , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
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- Description: Big data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems. © 2021, The Author(s).
- Authors: Anwar, Memoona , Gill, Asif , Hussain, Farookh , Imran, Muhammad
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Big data ecosystems are complex data-intensive, digital–physical systems. Data-intensive ecosystems offer a number of benefits; however, they present challenges as well. One major challenge is related to the privacy and security. A number of privacy and security models, techniques and algorithms have been proposed over a period of time. The limitation is that these solutions are primarily focused on an individual or on an isolated organizational context. There is a need to study and provide complete end-to-end solutions that ensure security and privacy throughout the data lifecycle across the ecosystem beyond the boundary of an individual system or organizational context. The results of current study provide a review of the existing privacy and security challenges and solutions using the systematic literature review (SLR) approach. Based on the SLR approach, 79 applicable articles were selected and analyzed. The information from these articles was extracted to compile a catalogue of security and privacy challenges in big data ecosystems and to highlight their interdependencies. The results were categorized from theoretical viewpoint using adaptive enterprise architecture and practical viewpoint using DAMA framework as guiding lens. The findings of this research will help to identify the research gaps and draw novel research directions in the context of privacy and security in big data-intensive ecosystems. © 2021, The Author(s).
Unmanned aerial vehicle for internet of everything : opportunities and challenges
- Liu, Yalin, Dai, Hong-Ning, Wang, Qubeijian, Shukla, Mahendra, Imran, Muhammad
- Authors: Liu, Yalin , Dai, Hong-Ning , Wang, Qubeijian , Shukla, Mahendra , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 155, no. (2020), p. 66-83
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- Description: The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole “things” aspect to the omnipotent role of “intelligent connection of things”. Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: (1) scalability requiring a scalable network architecture with ubiquitous coverage, (2) intelligence requiring a global computing plane enabling intelligent things, (3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE. © 2020 Elsevier B.V.
- Authors: Liu, Yalin , Dai, Hong-Ning , Wang, Qubeijian , Shukla, Mahendra , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 155, no. (2020), p. 66-83
- Full Text:
- Reviewed:
- Description: The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole “things” aspect to the omnipotent role of “intelligent connection of things”. Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: (1) scalability requiring a scalable network architecture with ubiquitous coverage, (2) intelligence requiring a global computing plane enabling intelligent things, (3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE. © 2020 Elsevier B.V.
Deep learning and big data technologies for IoT security
- Amanullah, Mohamed, Habeeb, Riyaz, Nasaruddin, Fariza, Gani, Abdullah, Ahmed, Ejaz, Nainar, Abdul, Akim, Nazihah, Imran, Muhammad
- Authors: Amanullah, Mohamed , Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Ahmed, Ejaz , Nainar, Abdul , Akim, Nazihah , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 151, no. (2020), p. 495-517
- Full Text: false
- Reviewed:
- Description: Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. © 2020 Elsevier B.V.
Network slicing : a next generation 5G perspective
- Subedi, Prashant, Alsadoon, Abeer, Prasad, Prasad, Rehman, Sabih, Giweli, Nabil, Imran, Muhammad, Arif, Samrah
- Authors: Subedi, Prashant , Alsadoon, Abeer , Prasad, Prasad , Rehman, Sabih , Giweli, Nabil , Imran, Muhammad , Arif, Samrah
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
- Full Text:
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- Description: Fifth-generation (5G) wireless networks are projected to bring a major transformation to the current fourth-generation network to support the billions of devices that will be connected to the Internet. 5G networks will enable new and powerful capabilities to support high-speed data rates, better connectivity and system capacity that are critical in designing applications in virtual reality, augmented reality and mobile online gaming. The infrastructure of a network that can support stringent application requirements needs to be highly dynamic and flexible. Network slicing can provide these dynamic and flexible characteristics to a network architecture. Implementing network slicing in 5G requires domain modification of the preexisting network architecture. A network slicing architecture is proposed for an existing 5G network with the aim of enhancing network dynamics and flexibility to support modern network applications. To enable network slicing in a 5G network, we established the virtualisation of the underlying physical 5G infrastructure by utilising technological advancements, such as software-defined networking and network function virtualisation. These virtual networks can fulfil the requirement of multiple use cases as required by creating slices of these virtual networks. Thus, abstracting from the physical resources to create virtual networks and then applying network slicing on these virtual networks enable the 5G network to address the increased demands for high-speed communication. © 2021, The Author(s).
- Authors: Subedi, Prashant , Alsadoon, Abeer , Prasad, Prasad , Rehman, Sabih , Giweli, Nabil , Imran, Muhammad , Arif, Samrah
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Eurasip Journal on Wireless Communications and Networking Vol. 2021, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Fifth-generation (5G) wireless networks are projected to bring a major transformation to the current fourth-generation network to support the billions of devices that will be connected to the Internet. 5G networks will enable new and powerful capabilities to support high-speed data rates, better connectivity and system capacity that are critical in designing applications in virtual reality, augmented reality and mobile online gaming. The infrastructure of a network that can support stringent application requirements needs to be highly dynamic and flexible. Network slicing can provide these dynamic and flexible characteristics to a network architecture. Implementing network slicing in 5G requires domain modification of the preexisting network architecture. A network slicing architecture is proposed for an existing 5G network with the aim of enhancing network dynamics and flexibility to support modern network applications. To enable network slicing in a 5G network, we established the virtualisation of the underlying physical 5G infrastructure by utilising technological advancements, such as software-defined networking and network function virtualisation. These virtual networks can fulfil the requirement of multiple use cases as required by creating slices of these virtual networks. Thus, abstracting from the physical resources to create virtual networks and then applying network slicing on these virtual networks enable the 5G network to address the increased demands for high-speed communication. © 2021, The Author(s).
Energy harvesting in underwater acoustic wireless sensor networks : design, taxonomy, applications, challenges and future directions
- Khan, Anwar, Imran, Muhammad, Alharbi, Abdullah, Mohamed, Ehab, Fouda, Mostafa
- Authors: Khan, Anwar , Imran, Muhammad , Alharbi, Abdullah , Mohamed, Ehab , Fouda, Mostafa
- Date: 2022
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 10, no. (2022), p. 134606-134622
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- Description: In underwater acoustic wireless sensor networks (UAWSNs), energy harvesting either enhances the lifetime of a network by increasing the battery power of sensor nodes or ensures battery-less operation of nodes. This, in effect, results in sustainable and reliable operation of the network deployed for various underwater applications. This work provides a survey of the energy harvesting techniques for UAWSNs. Our work is unique than the existing work on underwater energy harvesting in that it includes state-of-the art techniques designed in the last decade. It analyzes every harvesting scheme in terms of its main idea, merits, demerits and the extent of the harvested power (energy). The description of the merits results in selection of the suitable scheme for the suitable underwater applications. The demerits of the addressed schemes provide an insight to their future enhancement and improvement. Moreover, the harvested techniques are classified into various categories depending upon the involved energy harvesting mechanism and compared based on the maximum and minimum amount of harvested power, which helps in selection of the suitable category keeping in view the power budget of an underwater network before deployment. The challenges in energy harvesting and in UAWSNs are described to provide an insight to them and to address them for further enhancement in the harvested extent. Finally, research directions are specified for future investigation. © 2013 IEEE.
- Authors: Khan, Anwar , Imran, Muhammad , Alharbi, Abdullah , Mohamed, Ehab , Fouda, Mostafa
- Date: 2022
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 10, no. (2022), p. 134606-134622
- Full Text:
- Reviewed:
- Description: In underwater acoustic wireless sensor networks (UAWSNs), energy harvesting either enhances the lifetime of a network by increasing the battery power of sensor nodes or ensures battery-less operation of nodes. This, in effect, results in sustainable and reliable operation of the network deployed for various underwater applications. This work provides a survey of the energy harvesting techniques for UAWSNs. Our work is unique than the existing work on underwater energy harvesting in that it includes state-of-the art techniques designed in the last decade. It analyzes every harvesting scheme in terms of its main idea, merits, demerits and the extent of the harvested power (energy). The description of the merits results in selection of the suitable scheme for the suitable underwater applications. The demerits of the addressed schemes provide an insight to their future enhancement and improvement. Moreover, the harvested techniques are classified into various categories depending upon the involved energy harvesting mechanism and compared based on the maximum and minimum amount of harvested power, which helps in selection of the suitable category keeping in view the power budget of an underwater network before deployment. The challenges in energy harvesting and in UAWSNs are described to provide an insight to them and to address them for further enhancement in the harvested extent. Finally, research directions are specified for future investigation. © 2013 IEEE.
Securing internet of medical things with friendly-jamming schemes
- Li, Xuran, Dai, Hong-Ning, Wang, Qubeijian, Imran, Muhammad, Li, Dengwang
- Authors: Li, Xuran , Dai, Hong-Ning , Wang, Qubeijian , Imran, Muhammad , Li, Dengwang
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 160, no. (2020), p. 431-442
- Full Text: false
- Reviewed:
- Description: The Internet of Medical Things (IoMT)-enabled e-healthcare can complement traditional medical treatments in a flexible and convenient manner. However, security and privacy become the main concerns of IoMT due to the limited computational capability, memory space and energy constraint of medical sensors, leading to the in-feasibility for conventional cryptographic approaches, which are often computationally-complicated. In contrast to cryptographic approaches, friendly jamming (Fri-jam) schemes will not cause extra computing cost to medical sensors, thereby becoming potential countermeasures to ensure security of IoMT. In this paper, we present a study on using Fri-jam schemes in IoMT. We first analyze the data security in IoMT and discuss the challenges. We then propose using Fri-jam schemes to protect the confidential medical data of patients collected by medical sensors from being eavesdropped. We also discuss the integration of Fri-jam schemes with various communication technologies, including beamforming, Simultaneous Wireless Information and Power Transfer (SWIPT) and full duplexity. Moreover, we present two case studies of Fri-jam schemes in IoMT. The results of these two case studies indicate that the Fri-jam method will significantly decrease the eavesdropping risk while leading to no significant influence on legitimate transmission. © 2020
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