| 1. |
- Sodhro, Ali Hassan, et al.
(författare)
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Artificial Intelligence Driven Mechanism for Edge Computing based Industrial Applications
- 2019
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Ingår i: IEEE Transaction on Industrial Informatics. - USA : IEEE. - 1551-3203 .- 1941-0050. ; 15:7, s. 4235-4243
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Tidskriftsartikel (refereegranskat)abstract
- Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (~0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.
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| 2. |
- Sodhro, Ali Hassan, et al.
(författare)
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Link Optimization in Software Defined IoV driven Autonomous Transportation System
- 2021
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - : IEEE. - 1524-9050 .- 1558-0016. ; 22:6, s. 3511-3520
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Tidskriftsartikel (refereegranskat)abstract
- Due to the high mobility, dynamic nature, and legacy vehicular networks, the seamless connectivity and reliability become a new challenge in software-defined internet of vehicles based intelligent transportation systems (ITS). Thus, effieicnt optimization of the link with proper monitoring of the high speed of vehicles in ITS is very vital to promote the error-free and trustable platform. Key issues related to reliability, connectivity and stability optimization for vehicular networks are addressed. Thus, this study proposes a novel reliable connectivity framework by developing a stable, and scalable link optimization (SSLO) algorithm, state-of-the-art system model. In addition, a Use-case of smart city with stable and reliable connectivity is proposed by examining the importance of vehicular networks. The numerical experimental results are extracted from software defined-Internet of Vehicle (SD-IoV) platform which shows high stability and reliability of the proposed SSLO under different test scenarios, such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to anything (V2X). The proposed SSLO and Baseline algorithms are compared in terms of performance metrics e.g. packet loss ratio, transmission power (i.e., stability), average throughput, and average delay transfer. Finally, the validated results reveal that SSLO algorithm optimizes connectivity (95%), energy efficiency (67%), throughput (4Kbps) and delay (3 sec).
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| 3. |
- Sodhro, Ali Hassan, et al.
(författare)
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Artificial Intelligence based QoS optimization for multimedia communication in IoV systems
- 2019
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Ingår i: Future generations computer systems. - : ELSEVIER SCIENCE BV. - 0167-739X .- 1872-7115. ; 95, s. 667-680
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Tidskriftsartikel (refereegranskat)abstract
- Due to the advancements in multimedia communication in internet of vehicles (boy) through emerging technologies i.e., WiFi, Bluetooth, and fifth generation (5G) etc. The critical challenge for boy during multimedia communication in healthcare is the quality of experience (QoE) optimization by managing the mobility of wireless channel between vehicles. Besides, Artificial Intelligence (Al) based approaches have entirely changed the landscape of IoVs, also the portable devices for transmitting multimedia content in IoV system has become very necessary for the end-users in their respective fields. Most of the end users are facing is their annoyed and less satisfactory perspective about the quality they are experiencing i.e., QoE. If the service provisioning is not pleasant then most of the end-users/consumers give-up to continue, and finally market devaluates the overall performance of the devices, company or entire system. So remedy that problem this paper first proposes two novel algorithms named, Power-aware QoE Optimization (PQO) and Buffer-aware QoE Optimization (BQO) and compares their performance with the Baseline. Second proposes multimedia communication mechanism. Third, proposes the QoE optimization framework during multimedia communication in boy system through portable devices. Besides, experimental results reveal that proposed PQO and BQO algorithms optimizes the QoE at (31%, 33.5%) with improved lifetime of portable devices at (25%, 27%) higher level than the Baseline (25%, 17) accordingly by satisfying the end-users. Hence, it is concluded that our proposed algorithms outperforms the Baseline, so can be considered as potential candidates for the boy applications during multimedia communication. (C) 2019 Elsevier B.V. All rights reserved.
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| 4. |
- Sodhro, Ali Hassan, 1986-, et al.
(författare)
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Toward ML-Based Energy-Efficient Mechanism for 6G Enabled Industrial Network in Box Systems
- 2021
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Ingår i: IEEE Transaction on Industrial Informatics. - USA : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1551-3203 .- 1941-0050. ; 17:10, s. 7185-7192
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) techniques in association to emerging sixth generation (6G) technologies, i.e., massive Internet of Things (IoT), big data analytics have caught too much attention from academia to the business world since last few years due to their high and fast computing capabilities. The role of ML-based 6G techniques is to reshape the imaginary idea into physical world for resolving the challenging issues of energy, quality of service (QoS), and quality of experience (QoE). Besides, ML techniques with better association to 6G reshapes the industrial network in box (NIB) platform. In the mean-time rapidly increasing market of the IoT devices to deliver multimedia content has caught the attention of various fields such as, industrial, and healthcare. The challenging issue that end-users are facing is the unsatisfactory and annoyed performance of portable devices while surfing the video, and image to/from desired entity, i.e., low QoE. To resolve these issues this research first, proposes a novel ML-driven mobility management method for the efficient communication in industrial NIB applications. Second, a novel architecture of 6G-based intelligent QoE and QoS optimization in industrial NIB is proposed. Third, a 6G-based NIB framework is proposed in association to the long-term evolution. Forth, use-case for 6G-empowered industrial NIB is recommended for an energy efficient communication. Experimental results are extracted with high energy efficiency, better QoE, and QoS in 6G-based industrial NIB.
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| 5. |
- Sodhro, Ali Hassan, et al.
(författare)
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Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System
- 2021
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 22:8, s. 5223-5231
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Tidskriftsartikel (refereegranskat)abstract
- Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.
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| 6. |
- Alizadehsani, Roohallah, et al.
(författare)
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Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
- 2024
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 12, s. 35796-35812
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Forskningsöversikt (refereegranskat)abstract
- The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.
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| 7. |
- Manikandan, Ramachandran, et al.
(författare)
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Quality of Service-Aware Resource Selection in Healthcare IoT Using Deep Autoencoder Neural Networks
- 2022
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Ingår i: Human-centric Computing and Information Sciences. - Heidelberg : Springer. - 2192-1962. ; 12:36, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Heterogeneous network and device-to-device communication are two possible solutions for improving wireless network spectral efficiency. Techniques based on the Internet of Things (IoT) can interact between a large number of smart devices as well as heterogeneous networks. The goal of this study is to investigate proposed quality of service-aware resource selection in an IoT network for healthcare data using a deep auto encoder neural network with spectrum reuse utilizing mixed integer nonlinear programming (MINLP). The suggested MINLP spectrum reuse was used to address the optimization problem, and the spectrum allocation was done using fast Fourier transform based reinforcement Q-learning. Increased transmission repetitions have been identified as a promising strategy for improving IoT coverage by up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement over traditional LTE technology, particularly for serving customers in deep coverage. Based on a comparison of existing methodologies, the experimental study is performed using parameters such as bit error rate of 40%, signal-to-interference plus noise ratio of 72%, sum rate of 88%, and spectral efficiency of 98% © 2022. Human-centric Computing and Information Sciences. All Rights Reserved.
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| 8. |
- Sodhro, Ali Hassan, et al.
(författare)
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Towards an optimal resource management for IoT based Green and sustainable smart cities
- 2019
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Ingår i: Journal of Cleaner Production. - : ELSEVIER SCI LTD. - 0959-6526 .- 1879-1786. ; 220, s. 1167-1179
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Tidskriftsartikel (refereegranskat)abstract
- Internet of Things (IoT) is an emerging technology for the smart city that interconnects various digital devices through Internet, hence, providing multiple innovative facilities from academia to industry and healthcare to business. Smart city is the ubiquitous and a paradigm shift which has revolutionized the entire landscape with the support of information and communication technology (ICT), sensor-enabled IoT devices. For the better and big picture of the entire scenarios with high visibility multimedia (i.e., video, audio, text, and images) transmission is the soul-concept in the smart world, but due to resource constrained (power hungry and limited battery lifetime) nature of these tiny devices (which are building blocks of smart city) and voluminous amount of the data it is very challenging to openly talk about the sustainable and Green smart city platform. Thus, to remedy these problems two Hybrid Adaptive Bandwidth and Power Algorithm (HABPA), and Delay-tolerant Streaming Algorithm (DSA) are proposed by adopting stored video stream titled, StarWarsIV. Besides, a novel architecture of smart city system is proposed. Experimental results are obtained and analyzed in terms of performance metrics i.e., power drain, battery lifetime, delay, standard deviation and packet loss ratio (PLR) in association to the buffer size. It is concluded that the HABPA (45%,37%,20 ms) significantly optimizes power drain, battery lifetime (37%), standard deviation (3.5 dB), PLR (4.5%) of the IoT-enabled devices with less delay than DSA (43%, 32%,25 ms, 5 dB, 5.75%) and Baseline (42%,28%, 30 ms, 6 dB, 6.53%) respectively during media transmission in smart city. (C) 2019 Elsevier Ltd. All rights reserved.
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