| 1. |
- Abbas, Muhammad Tahir, et al.
(author)
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Energy-Saving Solutions for Cellular Internet of Things - A Survey
- 2022
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In: IEEE Access. - IEEE : IEEE. - 2169-3536. ; 10, s. 62096-62096
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Journal article (peer-reviewed)abstract
- The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy efficiency in mind. This paper conducts a comprehensive survey on energy-saving solutions in 3GPP-based CIoT networks. In comparison to current studies, the contribution of this paper is the classification and an extensive analysis of existing energy-saving solutions for CIoT, e.g., the configuration of particular parameter values and software modifications of transport- or radio-layer protocols, while also stressing key parameters impacting the energy consumption such as the frequency of data reporting, discontinuous reception cycles (DRX), and Radio Resource Control (RRC) timers. In addition, we discuss shortcomings, limitations, and possible opportunities which can be investigated in the future to reduce the energy consumption of CIoT devices.
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| 2. |
- Abuella, Mohamed, Postdoktor, 1980-, et al.
(author)
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Spatial Clustering Approach for Vessel Path Identification
- 2024
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In: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 12, s. 66248-66258
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Journal article (peer-reviewed)abstract
- This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation. © 2013 IEEE.
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| 3. |
- Afzal, Wasif, et al.
(author)
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On using grey literature and google scholar in systematic literature reviews in software engineering
- 2020
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In: IEEE Access. - United States. - 2169-3536. ; 8, s. 36226-36243
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Journal article (peer-reviewed)abstract
- © 2013 IEEE. Context: The inclusion of grey literature (GL) is important to remove publication bias while gathering available evidence regarding a certain topic. The number of systematic literature reviews (SLRs) in Software Engineering (SE) is increasing but we do not know about the extent of GL usage in these SLRs. Moreover, Google Scholar is rapidly becoming a search engine of choice for many researchers but the extent to which it can find the primary studies is not known. Objective: This tertiary study is an attempt to i) measure the usage of GL in SLRs in SE. Furthermore this study proposes strategies for categorizing GL and a quality checklist to use for GL in future SLRs; ii) explore if it is feasible to use only Google Scholar for finding scholarly articles for academic research. Method: We have conducted a systematic mapping study to measure the extent of GL usage in SE SLRs as well as to measure the feasibility of finding primary studies using Google Scholar. Results and conclusions: a) Grey Literature: 76.09% SLRs (105 out of 138) in SE have included one or more GL studies as primary studies. Among total primary studies across all SLRs (6307), 582 are classified as GL, making the frequency of GL citing as 9.23%. The intensity of GL use indicate that each SLR contains 5 primary studies on average (total intensity of GL use being 5.54). The ranking of GL tells us that conference papers are the most used form 43.3% followed by technical reports 28.52%. Universities, research institutes, labs and scientific societies together make up 67.7% of GL used, indicating that these are useful sources for searching GL. We additionally propose strategies for categorizing GL and criteria for evaluating GL quality, which can become a basis for more detailed guidelines for including GL in future SLRs. b) Google Scholar Results: The results show that Google Scholar was able to retrieve 96% of primary studies of these SLRs. Most of the primary studies that were not found using Google Scholar were from grey sources.
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| 4. |
- Ahmad, Ijaz, et al.
(author)
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Machine Learning Meets Communication Networks: Current Trends and Future Challenges
- 2020
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 8, s. 223418-223460
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Journal article (peer-reviewed)abstract
- The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.
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| 5. |
- Ahmad, Ijaz, et al.
(author)
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Security of Satellite-Terrestrial Communications : Challenges and Potential Solutions
- 2022
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In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 96038-96052
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Journal article (peer-reviewed)abstract
- The integration of satellite and terrestrial networks has become inevitable in the next generations of communications networks due to emerging needs of ubiquitous connectivity of remote locations. New and existing services and critical infrastructures in remote locations in sea, on land and in space will be seamlessly connected through a diverse set of terrestrial and non-terrestrial communication technologies. However, the integration of terrestrial and non-terrestrial systems will open up both systems to unique security challenges that can arise due to the migration of security challenges from one to another. Similarly, security challenges can also arise due to the incompatibility of distinct systems or incoherence of security policies. The resulting security implications, thus, can be highly consequential due to the criticality of the infrastructures such as space stations, autonomous ships, and airplanes, for instance. Therefore, in this article we study existing security challenges in satellite-terrestrial communication systems and discuss potential solutions for those challenges. Furthermore, we provide important research directions to encourage future research on existing security gaps.
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| 6. |
- Ahmad, Muhammad Ovais, Senior Lecturer
(author)
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On the Efficiency of Supernodal Factorization in Interior-Point Method Using CPU-GPU Collaboration
- 2020
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In: IEEE Access. - : IEEE Computer Society Digital Library. - 2169-3536. ; 8, s. 120892-120904
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Journal article (peer-reviewed)abstract
- Primal-dual interior-point method (PDIPM) is the most efficient technique for solving sparse linear programming (LP) problems. Despite its efficiency, PDIPM remains a compute-intensive algorithm. Fortunately, graphics processing units (GPUs) have the potential to meet this requirement. However, their peculiar architecture entails a positive relationship between problem density and speedup, conversely implying a limited affinity of GPUs for problem sparsity. To overcome this difficulty, the state-of-the-art hybrid (CPU-GPU) implementation of PDIPM exploits presence of supernodes in sparse matrices during factorization. Supernodes are groups of similar columns that can be treated as dense submatrices. Factorization method used in the state-of-the-art solver performs only selected operations related to large supernodes on GPU. This method is known to underutilize GPU’s computational power while increasing CPU-GPU communication overhead. These shortcomings encouraged us to adapt another factorization method, which processes sets of related supernodes on GPU, and introduce it to the PDIPM implementation of a popular open-source solver. Our adaptation enabled the factorization method to better mitigate the effects of round-off errors accumulated over multiple iterations of PDIPM. To augment performance gains, we also used an efficient CPU-based matrix multiplication method. When tested for a set of well-known sparse problems, the adapted solver showed average speed-ups of approximately 55X, 1.14X and 1.05X over the open-source solver’s original version, the state-of-the-art solver, and a highly optimized proprietary solver known as CPLEX, respectively. These results strongly indicate that our proposed hybrid approach can lead to significant performance gains for solving large sparse problems.
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| 7. |
- Ahmad, Sarosh, et al.
(author)
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A Compact CPW-Fed Ultra-Wideband Multi-Input-Multi-Output (MIMO) Antenna for Wireless Communication Networks
- 2022
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In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 25278-25289
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Journal article (peer-reviewed)abstract
- In this article, a compact coplanar waveguide (CPW) technique based ultra-wideband multiple-input-multiple-output (MIMO) antenna is proposed. The design is characterized by a broad impedance bandwidth starting from 3 GHz to 11 GHz. The overall size of the MIMO design is 60 x 60 mm(2) (1.24 x 1.24 lambda(2)(g) @ 3 GHz) with a thickness of 1.6 mm. To make the design ultra-wideband, the proposed MIMO antenna design has four jug-shaped radiating elements. The design is printed on a FR-4 substrate (relative permittivity of epsilon(r) = 4.4 and loss tangent of tan delta = 0.025). The polarization diversity phenomenon is realized by placing four antenna elements orthogonally. This arrangement increases the isolation among the MIMO antenna elements. The simulated results of the ultra-wideband MIMO antenna are verified by measured results. The proposed MIMO antenna has a measured diversity gain greater than 9.98, envelope correlation coefficient (ECC) less than 0.02, and good MIMO performance where the isolation is more than -20dB between the elements. The group delay, channel capacity loss (CCL), and the total active reflection coefficient (TARC) multiplexing efficiency and mean effective gain results are also analyzed. The group delay is found to be less than 1.2ns, CCL values calculated to be less than 0.4 bits/sec/Hz, while the TARC is below -10dB for the whole operating spectrum. The proposed design is a perfect candidate for ultra-wideband wireless communication systems and portable devices.
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| 8. |
- Ahmed, Kazi Main Uddin, 1989-, et al.
(author)
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A Novel Reliability Index to Assess the Computational Resource Adequacy in Data Centers
- 2021
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In: IEEE Access. - NY : IEEE. - 2169-3536. ; 9, s. 54530-54541
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Journal article (peer-reviewed)abstract
- The energy demand of data centers is increasing globally with the increasing demand for computational resources to ensure the quality of services. It is important to quantify the required resources to comply with the computational workloads at the rack-level. In this paper, a novel reliability index called loss of workload probability is presented to quantify the rack-level computational resource adequacy. The index defines the right-sizing of the rack-level computational resources that comply with the computational workloads, and the desired reliability level of the data center investor. The outage probability of the power supply units and the workload duration curve of servers are analyzed to define the loss of workload probability. The workload duration curve of the rack, hence, the power consumption of the servers is modeled as a function of server workloads. The server workloads are taken from a publicly available data set published by Google. The power consumption models of the major components of the internal power supply system are also presented which shows the power loss of the power distribution unit is the highest compared to the other components in the internal power supply system. The proposed reliability index and the power loss analysis could be used for rack-level computational resources expansion planning and ensures energy-efficient operation of the data center.
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| 9. |
- Ahmed, Kazi Main Uddin, 1989-, et al.
(author)
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A Review of Data Centers Energy Consumption And Reliability Modeling
- 2021
-
In: IEEE Access. - : IEEE. - 2169-3536. ; 9
-
Research review (peer-reviewed)abstract
- Enhancing the efficiency and the reliability of the data center are the technical challenges for maintaining the quality of services for the end-users in the data center operation. The energy consumption models of the data center components are pivotal for ensuring the optimal design of the internal facilities and limiting the energy consumption of the data center. The reliability modeling of the data center is also important since the end-user’s satisfaction depends on the availability of the data center services. In this review, the state-of-the-art and the research gaps of data center energy consumption and reliability modeling are identified, which could be beneficial for future research on data center design, planning, and operation. The energy consumption models of the data center components in major load sections i.e., information technology (IT), internal power conditioning system (IPCS), and cooling load section are systematically reviewed and classified, which reveals the advantages and disadvantages of the models for different applications. Based on this analysis and related findings it is concluded that the availability of the model parameters and variables are more important than the accuracy, and the energy consumption models are often necessary for data center reliability studies. Additionally, the lack of research on the IPCS consumption modeling is identified, while the IPCS power losses could cause reliability issues and should be considered with importance for designing the data center. The absence of a review on data center reliability analysis is identified that leads this paper to review the data center reliability assessment aspects, which is needed for ensuring the adaptation of new technologies and equipment in the data center. The state-of-the-art of the reliability indices, reliability models, and methodologies are systematically reviewed in this paper for the first time, where the methodologies are divided into two groups i.e., analytical and simulation-based approaches. There is a lack of research on the data center cooling section reliability analysis and the data center components’ failure data, which are identified as research gaps. In addition, the dependency of different load sections for reliability analysis of the data center is also included that shows the service reliability of the data center is impacted by the IPCS and the cooling section.
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| 10. |
- Ait-Mlouk, Addi, et al.
(author)
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KBot : a Knowledge graph based chatBot for natural language understanding over linked data
- 2020
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In: IEEE Access. - : IEEE. - 2169-3536. ; 8, s. 149220-149230
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Journal article (peer-reviewed)abstract
- With the rapid progress of the semantic web, a huge amount of structured data has become available on the web in the form of knowledge bases (KBs). Making these data accessible and useful for end-users is one of the main objectives of chatbots over linked data. Building a chatbot over linked data raises different challenges, including user queries understanding, multiple knowledge base support, and multilingual aspect. To address these challenges, we first design and develop an architecture to provide an interactive user interface. Secondly, we propose a machine learning approach based on intent classification and natural language understanding to understand user intents and generate SPARQL queries. We especially process a new social network dataset (i.e., myPersonality) and add it to the existing knowledge bases to extend the chatbot capabilities by understanding analytical queries. The system can be extended with a new domain on-demand, flexible, multiple knowledge base, multilingual, and allows intuitive creation and execution of different tasks for an extensive range of topics. Furthermore, evaluation and application cases in the chatbot are provided to show how it facilitates interactive semantic data towards different real application scenarios and showcase the proposed approach for a knowledge graph and data-driven chatbot.
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