| 1. |
- Sodhro, Ali Hassan, et al.
(author)
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5G-based Transmission Power Control Mechanism in Fog Computing for IoT Devices
- 2018
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In: Sustainability. - : MDPI. - 2071-1050. ; 10:4, s. 1258-1258
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Journal article (peer-reviewed)abstract
- og computing has become the revolutionary paradigm and one of the intelligent services of the 5th Generation (5G) emerging network, while Internet of Things (IoT) lies under its main umbrella. Enhancing and optimizing the quality of service (QoS) in Fog computing networks is one of the critical challenges of the present. In the meantime, strong links between the Fog, IoT devices and the supporting back-end servers is done through large scale cloud data centers and with the linear exponential trend of IoT devices and voluminous generated data. Fog computing is one of the vital and potential solutions for IoT in close connection with things and end users with less latency but due to high computational complexity, less storage capacity and more power drain in the cloud it is inappropriate choice. So, to remedy this issue, we propose transmission power control (TPC) based QoS optimization algorithm named (QoS-TPC) in the Fog computing. Besides, we propose the Fog-IoT-TPC-QoS architecture and establish the connection between TPC and Fog computing by considering static and dynamic conditions of wireless channel. Experimental results examine that proposed QoS-TPC optimizes the QoS in terms of maximum throughput, less delay, less jitter and minimum energy drain as compared to the conventional that is, ATPC, SKims and constant TPC methods
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| 2. |
- Sodhro, Ali Hassan, 1986-, et al.
(author)
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A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System
- 2019
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In: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 19:19, s. 8479-8486
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Journal article (peer-reviewed)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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| 3. |
- Sodhro, Ali Hassan, et al.
(author)
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A Novel Energy Optimization Approach for Artificial Intelligence-enabled Massive Internet of Things
- 2019
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In: PROCEEDINGS OF THE 2019 SUMMER SIMULATION CONFERENCE (SUMMERSIM 19). - : ACM Digital Library.
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Conference paper (peer-reviewed)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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| 4. |
- Sodhro, Ali Hassan, et al.
(author)
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An adaptive QoS computation for medical data processing in intelligent healthcare applications
- 2020
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In: Neural Computing and Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 32:2020, s. 723-734
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Journal article (peer-reviewed)abstract
- Efficient computation of quality of service (QoS) during medical data processing through intelligent measurement methods is one of the mandatory requirements of the medial healthcare world. However, emergency medical services often involve transmission of critical data, thus having stringent requirements for network quality of service (QoS). This paper contributes in three distinct ways. First, it proposes the novel adaptive QoS computation algorithm (AQCA) for fair and efficient monitoring of the performance indicators, i.e., transmission power, duty cycle and route selection during medical data processing in healthcare applications. Second, framework of QoS computation in medical applications is proposed at physical, medium access control (MAC) and network layers. Third, QoS computation mechanism with proposed AQCA and quality of experience (QoE) is developed. Besides, proper examination of QoS computation for medical healthcare application is evaluated with 4–10 inches large-screen user terminal (UT) devices (for example, LCD panel size, resolution, etc.). These devices are based on high visualization, battery lifetime and power optimization for ECG service in emergency condition. These UT devices are used to achieve highest level of satisfaction in terms, i.e., less power drain, extended battery lifetime and optimal route selection. QoS parameters with estimation of QoE perception identify the degree of influence of each QoS parameters on the medical data processing is analyzed. The experimental results indicate that QoS is computed at physical, MAC and network layers with transmission power (− 15 dBm), delay (100 ms), jitter (40 ms), throughput (200 Bytes), duty cycle (10%) and route selection (optimal). Thus it can be said that proposed AQCA is the potential candidate for QoS computation than Baseline for medical healthcare applications.
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| 5. |
- Sodhro, Ali Hassan, 1986-, et al.
(author)
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An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications
- 2018
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In: Sensors. - : MDPI AG. - 1424-8220. ; 18:3, s. 923-923
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Journal article (peer-reviewed)abstract
- Rapid progress and emerging trends in miniaturized medical devices have enabled the un-obtrusive monitoring of physiological signals and daily activities of everyone’s life in a prominent and pervasive manner. Due to the power-constrained nature of conventional wearable sensor devices during ubiquitous sensing (US), energy-efficiency has become one of the highly demanding and debatable issues in healthcare. This paper develops a single chip-based wearable wireless electrocardiogram (ECG) monitoring system by adopting analog front end (AFE) chip model ADS1292R from Texas Instruments. The developed chip collects real-time ECG data with two adopted channels for continuous monitoring of human heart activity. Then, these two channels and the AFE are built into a right leg drive right leg drive (RLD) driver circuit with lead-off detection and medical graded test signal. Human ECG data was collected at 60 beats per minute (BPM) to 120 BPM with 60 Hz noise and considered throughout the experimental set-up. Moreover, notch filter (cutoff frequency 60 Hz), high-pass filter (cutoff frequency 0.67 Hz), and low-pass filter (cutoff frequency 100 Hz) with cut-off frequencies of 60 Hz, 0.67 Hz, and 100 Hz, respectively, were designed with bilinear transformation for rectifying the power-line noise and artifacts while extracting real-time ECG signals. Finally, a transmission power control-based energy-efficient (ETPC) algorithm is proposed, implemented on the hardware and then compared with the several conventional TPC methods. Experimental results reveal that our developed chip collects real-time ECG data efficiently, and the proposed ETPC algorithm achieves higher energy savings of 35.5% with a slightly larger packet loss ratio (PLR) as compared to conventional TPC (e.g., constant TPC, Gao’s, and Xiao’s methods).
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| 6. |
- Sodhro, Ali Hassan, et al.
(author)
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Artificial Intelligence based QoS optimization for multimedia communication in IoV systems
- 2019
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In: Future Generation Computer Systems. - : ELSEVIER SCIENCE BV. - 0167-739X .- 1872-7115. ; 95, s. 667-680
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Journal article (peer-reviewed)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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| 7. |
- Sodhro, Ali Hassan, et al.
(author)
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Power Control Algorithms for Media Transmission in Remote Healthcare Systems
- 2018
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In: IEEE Access. - USA : IEEE. - 2169-3536. ; 6:2018, s. 42384-42393
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Journal article (peer-reviewed)abstract
- Currently, medical media technologies have become a center of attention due to emerging trends in miniaturized wearable devices from factories to health corner stores everywhere. Due to the power-constrained nature of these portable devices, it is challenging to adopt them during critical medical operations and diagnoses. Maximizing energy efficiency and, hence, extending the battery life is vital. In addition, conventional approaches with constant transmission power are inappropriate option for green and smart healthcare. Thus, this paper first proposes a transmission power control (TPC)-based energy-efficient algorithm (EEA) for when a subject is in different postures, i.e., standing, walking, and running, in wireless body sensor networks. Second, a hardware platform was developed on the Intel Galileo board to test and compare the proposed EEA and conventional adaptive TPC (ATPC) in terms of energy and channel reliability or packet loss ratio (PLR). Experimental results revealed that the proposed EEA obtained energy savings of 42.5% with an acceptable PLR compared with that of the traditional ATPC method.
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| 8. |
- Sodhro, Ali Hassan, et al.
(author)
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Power Management Strategies for Medical Information Transmission in Wireless Body Sensor Networks
- 2020
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In: IEEE Consumer Electronics Magzine. - USA. ; 9:2, s. 47-51
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Journal article (peer-reviewed)abstract
- To minimize and manage the power drain, and extend battery lifetime of wireless body sensor networks (WBSN) is one of the major challenges. There are three key purposes of this survey article, first, to examine the downsides of the classical power-management methods in WBSNs; second, considering the life-critical applications and emergency contexts that are encompassed by WBSN; and, third, studying the impact of power-management techniques on resource-confined networks for economical healthcare. A specific power-management solution is also discussed.
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| 9. |
- 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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| 10. |
- Sodhro, Ali, et al.
(author)
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AI-Enabled Reliable Channel Modelling Architecture for FoG Computing Vehicular Networks’
- 2020
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In: IEEE Wireless Communication Magazine. - IEEE : IEEE. - 1536-1284 .- 1558-0687. ; 27:2, s. 14-21
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI)-driven fog computing (FC) and its emerging role in vehicular networks is playing a remarkable role in revolutionizing daily human lives. Fog radio access networks are accommodating billions of Internet of Things devices for real-time interactive applications at high reliability. One of the critical challenges in today's vehicular networks is the lack of standard wireless channel models with better quality of service (QoS) for passengers while enjoying pleasurable travel (i.e., highly visualized videos, images, news, phone calls to friends/relatives). To remedy these issues, this article contributes significantly in four ways. First, we develop a novel AI-based reliable and interference-free mobility management algorithm (RIMMA) for fog computing intra-vehicular networks, because traffic monitoring and driver's safety management are important and basic foundations. The proposed RIMMA in association with FC significantly improves computation, communication, cooperation, and storage space. Furthermore, its self-adaptive, reliable, intelligent, and mobility-aware nature, and sporadic contents are monitored effectively in highly mobile vehicles. Second, we propose a reliable and delay-tolerant wireless channel model with better QoS for passengers. Third, we propose a novel reliable and efficient multi-layer fog driven inter-vehicular framework. Fourth, we optimize QoS in terms of mobility, reliability, and packet loss ratio. Also, the proposed RIMMA is compared to an existing competitive conventional method (i.e., baseline). Experimental results reveal that the proposed RIMMA outperforms the traditional technique for intercity vehicular networks.
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