| 1. |
- Sodhro, Ali Hassan, 1986-, et al.
(författare)
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A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System
- 2019
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 19:19, s. 8479-8486
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Tidskriftsartikel (refereegranskat)abstract
- Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, Body Sensor Networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio-signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (PTPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus this paper first, proposes a novel joint transmission power control (TPC) and duty-cycle adaptation based framework for pervasive healthcare. Second, adaptive energy-efficient transmission power control (AETPC) algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a Feedback Control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio (PLR) by adjusting both transmission power and duty-cycle unlike the conventional constant TPC and PTPC methods.
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| 2. |
- Sodhro, Ali Hassan, et al.
(författare)
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A Novel Energy Optimization Approach for Artificial Intelligence-enabled Massive Internet of Things
- 2019
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Ingår i: PROCEEDINGS OF THE 2019 SUMMER SIMULATION CONFERENCE (SUMMERSIM 19). - : ACM Digital Library.
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Konferensbidrag (refereegranskat)abstract
- Emerging trends in Internet of things (IoT) has caught the attention of every domain e.g., industrial, business, and healthcare etc. Sensor-embedded IoT devices are the key drivers for collecting large amount of data. Managing these large datasets is one of the critical challenges to be tackled. Continuous huge information collection through sensor-enabled devices is known as the massive IoT (mIoT). Thus, there is a need of self-adaptive artificial intelligence (AI)based strategies to effectively cluster, examine and interpret the entire entities in the system. With increased data volumes and power hungry natured IoT devices it is a dire need to manage their power wisely. To fairly allot the power levels to the tiny portable devices it is important to integrate mIoT with AI-based techniques. To remedy these issues this paper proposes a novel cross-layer based energy optimization algorithm (CEOA) in mIoT system by examining the detailed features and data patterns. Experimental analysis reveals that proposed CEOA outperforms its competing counterpart i.e., Baseline in terms of efficient power management and monitoring.
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| 3. |
- 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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| 4. |
- Sodhro, Ali Hassan, 1986-, et al.
(författare)
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Convergence of IoT and Product Lifecycle Management in Medical Health Care
- 2019
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Ingår i: Future Generation Computer Systems. - : Elsevier. - 0167-739X. ; 86:2019, s. 380-391
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Tidskriftsartikel (refereegranskat)abstract
- Emerging trends in Internet of Medical Things (IoMT) or Medical Internet of Things (MIoT), and miniaturized devices with have entirely changed the landscape of the every corner. Main challenges that heterogeneous sensor-enabled devices are facing during the connectivity and convergence with other domains are, first, the information/knowledge sharing and collaboration between several communicating parties such as, from manufacturing engineer to medical expert, then from hospitals/healthcare centers to patients during disease diagnosis and treatment. Second, battery lifecycle and energy management of wearable/portable devices. This paper solves first problem by integrating IoMT with Product Lifecycle Management (PLM), to regulate the information transfer from one entity to another and between devices in an efficient and accurate way. While, second issue is resolved by proposing two, battery recovery-based algorithm (BRA), and joint energy harvesting and duty-cycle optimization-based (JEHDO) algorithm for managing the battery lifecycle and energy of the resource-constrained tiny wearable devices, respectively. Besides, a novel joint IoMT and PLM based framework is proposed for medical healthcare applications. Experimental results reveal that BRA and JEHDO are battery-efficient and energy-efficient respectively.
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| 5. |
- Sodhro, Ali Hassan, et al.
(författare)
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Quality of Service Optimization in IoT Driven Intelligent Transportation System
- 2019
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Ingår i: IEEE Wireless Communication Magazine. - USA : IEEE. - 1536-1284 .- 1558-0687. ; 26:6, s. 10-17
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Tidskriftsartikel (refereegranskat)abstract
- High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms.
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| 6. |
- 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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