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- Sodhro, Ali Hassan, et al.
(author)
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Link Optimization in Software Defined IoV driven Autonomous Transportation System
- 2021
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In: IEEE Transactions on Intelligent Transportation Systems. - : IEEE. - 1524-9050 .- 1558-0016. ; 22:6, s. 3511-3520
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Journal article (peer-reviewed)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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| 2. |
- Sodhro, Ali Hassan, 1986-, et al.
(author)
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Toward ML-Based Energy-Efficient Mechanism for 6G Enabled Industrial Network in Box Systems
- 2021
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In: 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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Journal article (peer-reviewed)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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| 3. |
- Sodhro, Ali Hassan, et al.
(author)
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Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System
- 2021
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In: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 22:8, s. 5223-5231
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Journal article (peer-reviewed)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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