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These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.
Abdullah Lakhan; Mazhar Dootio; Tor Groenli; Ali Sodhro; Muhammad Khokhar. Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks. Electronics 2021, 10, 1719 .
AMA StyleAbdullah Lakhan, Mazhar Dootio, Tor Groenli, Ali Sodhro, Muhammad Khokhar. Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks. Electronics. 2021; 10 (14):1719.
Chicago/Turabian StyleAbdullah Lakhan; Mazhar Dootio; Tor Groenli; Ali Sodhro; Muhammad Khokhar. 2021. "Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks." Electronics 10, no. 14: 1719.
Since the purchase of Siri by Apple, and its release with the iPhone 4S in 2011, virtual assistants (VAs) have grown in number and popularity. The sophisticated natural language processing and speech recognition employed by VAs enables users to interact with them conversationally, almost as they would with another human. To service user voice requests, VAs transmit large amounts of data to their vendors; these data are processed and stored in the Cloud. The potential data security and privacy issues involved in this process provided the motivation to examine the current state of the art in VA research. In this study, we identify peer-reviewed literature that focuses on security and privacy concerns surrounding these assistants, including current trends in addressing how voice assistants are vulnerable to malicious attacks and worries that the VA is recording without the user’s knowledge or consent. The findings show that not only are these worries manifold, but there is a gap in the current state of the art, and no current literature reviews on the topic exist. This review sheds light on future research directions, such as providing solutions to perform voice authentication without an external device, and the compliance of VAs with privacy regulations.
Tom Bolton; Tooska Dargahi; Sana Belguith; Mabrook Al-Rakhami; Ali Sodhro. On the Security and Privacy Challenges of Virtual Assistants. Sensors 2021, 21, 2312 .
AMA StyleTom Bolton, Tooska Dargahi, Sana Belguith, Mabrook Al-Rakhami, Ali Sodhro. On the Security and Privacy Challenges of Virtual Assistants. Sensors. 2021; 21 (7):2312.
Chicago/Turabian StyleTom Bolton; Tooska Dargahi; Sana Belguith; Mabrook Al-Rakhami; Ali Sodhro. 2021. "On the Security and Privacy Challenges of Virtual Assistants." Sensors 21, no. 7: 2312.
The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.
Hina Magsi; Ali Sodhro; Noman Zahid; Sandeep Pirbhulal; Lei Wang; Mabrook Al-Rakhami. A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications. Electronics 2021, 10, 367 .
AMA StyleHina Magsi, Ali Sodhro, Noman Zahid, Sandeep Pirbhulal, Lei Wang, Mabrook Al-Rakhami. A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications. Electronics. 2021; 10 (4):367.
Chicago/Turabian StyleHina Magsi; Ali Sodhro; Noman Zahid; Sandeep Pirbhulal; Lei Wang; Mabrook Al-Rakhami. 2021. "A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications." Electronics 10, no. 4: 367.
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.
Ijaz Ahmad; Shariar Shahabuddin; Hassan Malik; Erkki Harjula; Teemu Leppanen; Lauri Loven; Antti Anttonen; Ali Hassan Sodhro; Muhammad Mahtab Alam; Markku Juntti; Antti Yla-Jaaski; Thilo Sauter; Andrei Gurtov; Mika Ylianttila; Jukka Riekki. Machine Learning Meets Communication Networks: Current Trends and Future Challenges. IEEE Access 2020, 8, 223418 -223460.
AMA StyleIjaz Ahmad, Shariar Shahabuddin, Hassan Malik, Erkki Harjula, Teemu Leppanen, Lauri Loven, Antti Anttonen, Ali Hassan Sodhro, Muhammad Mahtab Alam, Markku Juntti, Antti Yla-Jaaski, Thilo Sauter, Andrei Gurtov, Mika Ylianttila, Jukka Riekki. Machine Learning Meets Communication Networks: Current Trends and Future Challenges. IEEE Access. 2020; 8 (99):223418-223460.
Chicago/Turabian StyleIjaz Ahmad; Shariar Shahabuddin; Hassan Malik; Erkki Harjula; Teemu Leppanen; Lauri Loven; Antti Anttonen; Ali Hassan Sodhro; Muhammad Mahtab Alam; Markku Juntti; Antti Yla-Jaaski; Thilo Sauter; Andrei Gurtov; Mika Ylianttila; Jukka Riekki. 2020. "Machine Learning Meets Communication Networks: Current Trends and Future Challenges." IEEE Access 8, no. 99: 223418-223460.
Energy-efficient communication has become the center of attention from various interdisciplinary fields such as industrial automation, healthcare, and transportation, among others. Besides, proliferation in the Artificial Intelligence (AI)-based sixth generation (6G) technology for achieving the smart automation system has caught the attention of both academia and industry. Most of the intelligent automation systems are formed by IoT-based user terminal (UT) devices for multimedia (i.e., video, audio, image, and text) content delivery with high clarity and efficiency. Customer satisfaction/perception, i.e., quality of experience (QoE), is an essential factor to be analyzed because the quality of service (QoS) is not a suitable candidate to portray the feelings and expectations of users during multimedia transmission. Therefore, the energy-efficient, entropy-aware communication, and QoE analysis through IoT devices are the dire need. This paper focuses on how the energyefficient communication and user’s QoE level can be captured through UT device during multimedia transmission. Thus first, QoS-based joint energy and entropy optimization (QJEEO) algorithm is proposed. Second, the 6G-driven multimedia data structure model and framework are developed for modeling and evaluation of QoE with acquisition time. Third, the relationship between subjective test score (i.e., surveyed data) and objective performance metrics with mobility/speed of IoT-based devices for multimedia service is established. Forth, the correlation model is proposed for integrating QoS parameters with estimated QoE perceptions. Experimental results indicate that QoE is modeled and evaluated with acquisition time and correlated with QoS parameter, i.e., packet loss ratio (PLR), and average transfer delay during energy-efficient multimedia transmission in 6G-based networks to improve the satisfaction level of customers.
Ali Hassan Sodhro; Sandeep Pirbhulal; Zongwei Luo; Khan Muhammad; Noman Zahid Zahid. Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems. IEEE Internet of Things Journal 2020, 8, 5141 -5148.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Zongwei Luo, Khan Muhammad, Noman Zahid Zahid. Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems. IEEE Internet of Things Journal. 2020; 8 (7):5141-5148.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Zongwei Luo; Khan Muhammad; Noman Zahid Zahid. 2020. "Toward 6G Architecture for Energy-Efficient Communication in IoT-Enabled Smart Automation Systems." IEEE Internet of Things Journal 8, no. 7: 5141-5148.
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.
Ali Hassan Sodhro; Sandeep Pirbhulal; Gul Hassan Sodhro; Muhammad Muzammal; Luo Zongwei; Andrei Gurtov; Antonio Roberto L. de Macedo; Lei Wang; Nuno M. Garcia; Victor Hugo C. de Albuquerque. Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 5223 -5231.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Gul Hassan Sodhro, Muhammad Muzammal, Luo Zongwei, Andrei Gurtov, Antonio Roberto L. de Macedo, Lei Wang, Nuno M. Garcia, Victor Hugo C. de Albuquerque. Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (8):5223-5231.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Gul Hassan Sodhro; Muhammad Muzammal; Luo Zongwei; Andrei Gurtov; Antonio Roberto L. de Macedo; Lei Wang; Nuno M. Garcia; Victor Hugo C. de Albuquerque. 2020. "Towards 5G-Enabled Self Adaptive Green and Reliable Communication in Intelligent Transportation System." IEEE Transactions on Intelligent Transportation Systems 22, no. 8: 5223-5231.
The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) promotes the energy efficient communication in smart homes. Quality of Service (QoS) optimization during video streaming through wireless micro medical devices (WMMD) in smart healthcare homes is the main purpose of this research. This paper contributes in four distinct ways. First, to propose a novel Lazy Video Transmission Algorithm (LVTA). Second, a novel Video Transmission Rate Control Algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission) and standard deviation is investigated while comparing the LVTA, VTRCA, and Baseline approaches. Experimental results demonstrate that the reduction in encoding (32%, 35.4%) and transmission (37%, 39%) energy drains, PMR (5, 4), and standard deviation (3dB, 4dB) for VTRCA and LVTA, respectively, is greater than that obtained by Baseline during video streaming through WMMD.
Ali Hassan Sodhro; Andrei Gurtov; Noman Zahid; Sandeep Pirbhulal; Lei Wang; Muhammad Mahboob Ur Rahman; Muhammad Ali Imran; Qammer H. Abbasi. Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes. IEEE Internet of Things Journal 2020, 8, 9664 -9671.
AMA StyleAli Hassan Sodhro, Andrei Gurtov, Noman Zahid, Sandeep Pirbhulal, Lei Wang, Muhammad Mahboob Ur Rahman, Muhammad Ali Imran, Qammer H. Abbasi. Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes. IEEE Internet of Things Journal. 2020; 8 (12):9664-9671.
Chicago/Turabian StyleAli Hassan Sodhro; Andrei Gurtov; Noman Zahid; Sandeep Pirbhulal; Lei Wang; Muhammad Mahboob Ur Rahman; Muhammad Ali Imran; Qammer H. Abbasi. 2020. "Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes." IEEE Internet of Things Journal 8, no. 12: 9664-9671.
Deep learning (DL) driven cardiac image processing methods manage and monitor the massive medical data collected by the internet of things (IoT) based on wearable devices. A Joint DL and IoT platform are known as Deep-IoMT that extracts the accurate cardiac image data from noisy conventional devices and tools. Besides, smart and dynamic technological trends have caught the attention of every corner such as, healthcare, which is possible through portable and lightweight sensor-enabled devices. Tiny size and resourceconstrained nature restrict them to perform several tasks at a time. Thus, energy drain, limited battery lifetime, and high packet loss ratio (PLR) are the keys challenges to be tackled carefully for ubiquitous medical care. Sustainability (i.e., longer battery lifetime), energy efficiency, and reliability are the vital ingredients for wearable devices to empower a cost-effective and pervasive healthcare environment. Thus, the key contribution of this paper is the sixth fold. First, a novel self-adaptive power control-based enhanced efficient-aware approach (EEA) is proposed to reduce energy consumption and enhance the battery lifetime and reliability. The proposed EEA and conventional constant TPC are evaluated by adopting real-time data traces of static (i.e., sitting) and dynamic (i.e., cycling) activities and cardiac images. Second, a novel joint DL-IoMT framework is proposed for the cardiac image processing of remote elderly patients. Third, DL driven layered architecture for IoMT is proposed. Forth, the battery model for IoMT is proposed by adopting the features of a wireless channel and body postures. Fifth, network performance is optimized by introducing sustainability, energy drain, and PLR and average threshold RSSI indicators. Sixth, a Use-case for cardiac image-enabled elderly patient’s monitoring is proposed. Finally, it is revealed through experimental results in MATLAB that the proposed EEA scheme performs better than the constant TPC by enhancing energy efficiency, sustainability, and reliability during data transmission for elderly healthcare.
Tianle Zhang; Ali Hassan Sodhro; Zongwei Luo; Noman Zahid; Muhammad Wasim Nawaz; Sandeep Pirbhulal; Muhammad Muzammal. A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients. IEEE Access 2020, 8, 75822 -75832.
AMA StyleTianle Zhang, Ali Hassan Sodhro, Zongwei Luo, Noman Zahid, Muhammad Wasim Nawaz, Sandeep Pirbhulal, Muhammad Muzammal. A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients. IEEE Access. 2020; 8 (99):75822-75832.
Chicago/Turabian StyleTianle Zhang; Ali Hassan Sodhro; Zongwei Luo; Noman Zahid; Muhammad Wasim Nawaz; Sandeep Pirbhulal; Muhammad Muzammal. 2020. "A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients." IEEE Access 8, no. 99: 75822-75832.
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).
Ali Hassan Sodhro; Joel J. P. C. Rodrigues; Sandeep Pirbhulal; Noman Zahid; Antonio Roberto L. de Macedo; Victor Hugo C. de Albuquerque. Link Optimization in Software Defined IoV Driven Autonomous Transportation System. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 3511 -3520.
AMA StyleAli Hassan Sodhro, Joel J. P. C. Rodrigues, Sandeep Pirbhulal, Noman Zahid, Antonio Roberto L. de Macedo, Victor Hugo C. de Albuquerque. Link Optimization in Software Defined IoV Driven Autonomous Transportation System. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (6):3511-3520.
Chicago/Turabian StyleAli Hassan Sodhro; Joel J. P. C. Rodrigues; Sandeep Pirbhulal; Noman Zahid; Antonio Roberto L. de Macedo; Victor Hugo C. de Albuquerque. 2020. "Link Optimization in Software Defined IoV Driven Autonomous Transportation System." IEEE Transactions on Intelligent Transportation Systems 22, no. 6: 3511-3520.
The goal of this paper is to develop, deploy, test, and evaluatea a lightweight portable intrusion detection system (LPIDS) over wireless networks by adopting two different string matching algorithms: Aho‐Corasick algorithm and Knuth‐Morris‐Pratt algorithm (KMP). Thus, this research contributes in three ways. First, an efficient and lightweight IDS (LPIDS) is proposed. Second, the LPIDS was developed, implemented, tested, and evaluated using Aho‐Corasick and KMP on two different hardware platforms: Wi‐Fi Pineapple and Raspberry Pi. Third, a comparative analysis of proposed LPIDS is done in terms of network metrics such as throughput, power consumption, and response time with regard to their counterparts. Additionally, the proposed LPIDS is suggested for consultants while performing security audits. The experimental results reveal that Aho‐Corasick performs better than KMP throughout the majority of the process, but KMP is typically faster in the beginning with fewer rules. Similarly, Raspberry Pi shows remarkably higher performance than Wi‐Fi Pineapple in all of the measurements. Moreover, we compared the throughput between LPIDS and Snort, it is observed and analyzed that former has significantly higher throughput than later when most of the rules do not include content parameters. This paper concludes that due to computational complexity and slow hardware processing capabilities of Wi‐Fi Pineapple, it could not become suitable IDS in the presence of different pattern matching strategies. Finally, we propose modification of Snort to increase the throughput of the system.
Carl Nykvist; Martin Larsson; Ali Hassan Sodhro; Andrei Gurtov. A lightweight portable intrusion detection communication system for auditing applications. International Journal of Communication Systems 2020, 33, e4327 .
AMA StyleCarl Nykvist, Martin Larsson, Ali Hassan Sodhro, Andrei Gurtov. A lightweight portable intrusion detection communication system for auditing applications. International Journal of Communication Systems. 2020; 33 (7):e4327.
Chicago/Turabian StyleCarl Nykvist; Martin Larsson; Ali Hassan Sodhro; Andrei Gurtov. 2020. "A lightweight portable intrusion detection communication system for auditing applications." International Journal of Communication Systems 33, no. 7: e4327.
Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data.
Romana Talat; Mohammad S. Obaidat; Muhammad Muzammal; Ali Hassan Sodhro; Zongwei Luo; Sandeep Pirbhulal. A decentralised approach to privacy preserving trajectory mining. Future Generation Computer Systems 2019, 102, 382 -392.
AMA StyleRomana Talat, Mohammad S. Obaidat, Muhammad Muzammal, Ali Hassan Sodhro, Zongwei Luo, Sandeep Pirbhulal. A decentralised approach to privacy preserving trajectory mining. Future Generation Computer Systems. 2019; 102 ():382-392.
Chicago/Turabian StyleRomana Talat; Mohammad S. Obaidat; Muhammad Muzammal; Ali Hassan Sodhro; Zongwei Luo; Sandeep Pirbhulal. 2019. "A decentralised approach to privacy preserving trajectory mining." Future Generation Computer Systems 102, no. : 382-392.
Wireless Body Sensor Network (BSNs) are wearable sensors with varying sensing, storage, computation, and transmission capabilities. When data is obtained from multiple devices, multi-sensor fusion is desirable to transform potentially erroneous sensor data into high quality fused data. In this work, a data fusion enabled Ensemble approach is proposed to work with medical data obtained from BSNs in a fog computing environment. Daily activity data is obtained from a collection of sensors which is fused together to generate high quality activity data. The fused data is later input to an Ensemble classifier for early heart disease prediction. The ensembles are hosted in a Fog computing environment and the prediction computations are performed in a decentralised manners. The results from the individual nodes in the fog computing environment are then combined to produce a unified output. For the classification purpose, a novel kernel random forest ensemble is used that produces significantly better quality results than random forest. An extensive experimental study supports the applicability of the solution and the obtained results are promising, as we obtain 98% accuracy when the tree depth is equal to 15, number of estimators is 40, and 8 features are considered for the prediction task.
Muhammad Muzammal; Romana Talat; Ali Hassan Sodhro; Sandeep Pirbhulal. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion 2019, 53, 155 -164.
AMA StyleMuhammad Muzammal, Romana Talat, Ali Hassan Sodhro, Sandeep Pirbhulal. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion. 2019; 53 ():155-164.
Chicago/Turabian StyleMuhammad Muzammal; Romana Talat; Ali Hassan Sodhro; Sandeep Pirbhulal. 2019. "A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks." Information Fusion 53, no. : 155-164.
Ali Hassan Sodhro; Sandeep Pirbhulal; Victor Hugo C. De Albuquerque. Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications. IEEE Transactions on Industrial Informatics 2019, 15, 4235 -4243.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Victor Hugo C. De Albuquerque. Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications. IEEE Transactions on Industrial Informatics. 2019; 15 (7):4235-4243.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Victor Hugo C. De Albuquerque. 2019. "Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications." IEEE Transactions on Industrial Informatics 15, no. 7: 4235-4243.
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.
Ali Hassan Sodhro; Sandeep Pirbhulal; Marwa Qaraqe; Sonia Lohano; Gul Hassan Sodhro; Naveed Ur Rehman Junejo; Zongwei Luo. Power Control Algorithms for Media Transmission in Remote Healthcare Systems. IEEE Access 2018, 6, 42384 -42393.
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Marwa Qaraqe, Sonia Lohano, Gul Hassan Sodhro, Naveed Ur Rehman Junejo, Zongwei Luo. Power Control Algorithms for Media Transmission in Remote Healthcare Systems. IEEE Access. 2018; 6 ():42384-42393.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Marwa Qaraqe; Sonia Lohano; Gul Hassan Sodhro; Naveed Ur Rehman Junejo; Zongwei Luo. 2018. "Power Control Algorithms for Media Transmission in Remote Healthcare Systems." IEEE Access 6, no. : 42384-42393.
Fog 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.
Ali Hassan Sodhro; Sandeep Pirbhulal; Arun Kumar Sangaiah; Sonia Lohano; Gul Hassan Sodhro; Zongwei Luo. 5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices. Sustainability 2018, 10, 1258 .
AMA StyleAli Hassan Sodhro, Sandeep Pirbhulal, Arun Kumar Sangaiah, Sonia Lohano, Gul Hassan Sodhro, Zongwei Luo. 5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices. Sustainability. 2018; 10 (4):1258.
Chicago/Turabian StyleAli Hassan Sodhro; Sandeep Pirbhulal; Arun Kumar Sangaiah; Sonia Lohano; Gul Hassan Sodhro; Zongwei Luo. 2018. "5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices." Sustainability 10, no. 4: 1258.
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).
Ali Hassan Sodhro; Arun Kumar Sangaiah; Gul Hassan Sodhro; Sonia Lohano; Sandeep Pirbhulal. An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors 2018, 18, 923 .
AMA StyleAli Hassan Sodhro, Arun Kumar Sangaiah, Gul Hassan Sodhro, Sonia Lohano, Sandeep Pirbhulal. An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications. Sensors. 2018; 18 (3):923.
Chicago/Turabian StyleAli Hassan Sodhro; Arun Kumar Sangaiah; Gul Hassan Sodhro; Sonia Lohano; Sandeep Pirbhulal. 2018. "An Energy-Efficient Algorithm for Wearable Electrocardiogram Signal Processing in Ubiquitous Healthcare Applications." Sensors 18, no. 3: 923.
Video transmission is considered as a quite significant step towards health monitoring of the emergency patients during any critical incident. However, the energy hungry video transmission and slow progress in battery technologies have become a major and serious problem for the evolution of video technology in WBSNs. Therefore, the need arose to conduct research on sustainable, “Green”, i.e., energy-efficient and “Friendly”, i.e., battery-friendly technologies to cater the need of upcoming mobile and portable devices. The main challenge addressed in this research is how to increase the battery lifetime during on-demand Variable Bit Rate (VBR) video transmission from medical video server to base station in WBSNs. In order to overcome this problem, sustainable, Green and Friendly frame transmission algorithms are enunciated i.e. Lazy Algorithm (LA) and Battery-friendly Smoothing Algorithm (BSA) with analytical battery model. The proposed algorithms minimize transmission energy consumption, battery charge consumption and high current profile. These algorithms also prolongs the battery lifetime of those sensor nodes during video transmission. Experimental results demonstrates that BSA outperforms LA to minimize battery drain by improving its lifetime up to 19.8 %. However, the LA performs better than BSA in context of transmission energy saving up to 49.49 %. Furthermore, a video transmission framework of Remote Medical Education System (RMES) for elderly persons and infants is proposed to provide viable and sustainable battery solutions to serve the community.
Ali Hassan Sodhro; Ye Li; Madad Ali Shah. Green and friendly media transmission algorithms for wireless body sensor networks. Multimedia Tools and Applications 2016, 76, 20001 -20025.
AMA StyleAli Hassan Sodhro, Ye Li, Madad Ali Shah. Green and friendly media transmission algorithms for wireless body sensor networks. Multimedia Tools and Applications. 2016; 76 (19):20001-20025.
Chicago/Turabian StyleAli Hassan Sodhro; Ye Li; Madad Ali Shah. 2016. "Green and friendly media transmission algorithms for wireless body sensor networks." Multimedia Tools and Applications 76, no. 19: 20001-20025.