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Sodhro, Ali Hassan
Publications (10 of 33) Show all publications
Ali, M., Tang Jung, L., Sodhro, A. H., Ali Laghari, A., Birahim Belhaouari, S. & Gillani, Z. (2023). A Confidentiality-based data Classification-as-a-Service (C2aaS) for cloud security. Alexandria Engineering Journal, 64, 749-760
Open this publication in new window or tab >>A Confidentiality-based data Classification-as-a-Service (C2aaS) for cloud security
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2023 (English)In: Alexandria Engineering Journal, ISSN 1110-0168, E-ISSN 2090-2670, Vol. 64, p. 749-760Article in journal (Refereed) Published
Abstract [en]

Rapid development and massive use of Information Technology (IT) have since produced a massive amount of electronic data. In tandem, the demand for data outsourcing and the associated data security is increasing exponentially. Small organizations are often finding it expensive to save and process their huge amount of data, and keep the data secure from unauthorized access. Cloud computing is a suitable and affordable platform to provide services on user demand. The cloud platform is preferable used by individuals, Small, and Medium Enterprises (SMEs) that cannot afford large-scale hardware, software, and security maintenance cost. Storage and processing of big data in the cloud are becoming the key appealing features to SMEs and individuals. However, the processing of big data in the cloud is facing two issues such as security of stored data and system overload due to the volume of the data. These storage methods are plain text storage and encrypted text storage. Both methods have their strengths and limitations. The fundamental issue in plain text storage is the high risk of data security breaches; whereas, in encrypted text storage, the encryption of complete file data may cause system overload. This paper propose a feasible solution to address these issues with a new service model called Confidentiality-based Classification-as-a-Service (C2aaS) that performs data processing by treating data dynamically according to the data security level in preparation for data storing in the cloud. In comparison to the conventional methods, our proposed service model is strongly showing good security for confidential data and is proficient in reducing cloud system overloading. 

Keywords
Cloud storage method, Data classification, Data confidentiality, Security level
National Category
Computer Systems
Identifiers
urn:nbn:se:miun:diva-46714 (URN)10.1016/j.aej.2022.10.056 (DOI)2-s2.0-85141316631 (Scopus ID)
Available from: 2022-12-22 Created: 2022-12-22 Last updated: 2023-01-10Bibliographically approved
Sodhro, A. H., Sennersten, C. & Ahmad, A. (2022). Towards Cognitive Authentication for Smart Healthcare Applications. Sensors, 22(6), Article ID 2101.
Open this publication in new window or tab >>Towards Cognitive Authentication for Smart Healthcare Applications
2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 6, article id 2101Article in journal (Refereed) Published
Abstract [en]

Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events. 

Keywords
Biometrics, Cognitive authentication, EEG, Healthcare, IoT, Sensing
National Category
Computer Sciences
Identifiers
urn:nbn:se:miun:diva-44629 (URN)10.3390/s22062101 (DOI)000774231400001 ()35336276 (PubMedID)2-s2.0-85125954519 (Scopus ID)
Available from: 2022-03-22 Created: 2022-03-22 Last updated: 2022-04-14Bibliographically approved
Magsi, H., Sodhro, A. H., Al-Rakhami, M. S., Zahid, N., Pirbhula, S. & Wang, L. (2021). A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications. Paper presented at [30]Hina Magsi, Ali Hassan Sodhro, A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications, Electronics, MDPI, vol.10, no.4, pp.367, 2021. Electronics, 10(4), Article ID 367.
Open this publication in new window or tab >>A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications
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2021 (English)In: Electronics, ISSN 2079-9292, Vol. 10, no 4, article id 367Article in journal (Refereed) Published
Abstract [en]

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

Keywords
IoMT, data transmission, intelligent healthcare, proposed ABA, BRLE
National Category
Communication Systems
Identifiers
urn:nbn:se:miun:diva-41202 (URN)10.3390/electronics10040367 (DOI)000623346500001 ()2-s2.0-85100577966 (Scopus ID)
Conference
[30]Hina Magsi, Ali Hassan Sodhro, A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications, Electronics, MDPI, vol.10, no.4, pp.367, 2021
Available from: 2021-02-16 Created: 2021-02-16 Last updated: 2021-03-26Bibliographically approved
Memon, S. K., Nishar, K., Hanafi Ahmad Hijazi, M., Chowdhry, B., Sodhro, A. H., Pirbhulal, S. & Rodrigues, J. J. .. (2021). A survey on 802.11 MAC protocols industrial standards, architecture elements for providing QoS guarantee, supporting emergency traffic, and security: Future directions. Journal of Industrial Information Integration, 24(Dec 2021), Article ID 100225.
Open this publication in new window or tab >>A survey on 802.11 MAC protocols industrial standards, architecture elements for providing QoS guarantee, supporting emergency traffic, and security: Future directions
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2021 (English)In: Journal of Industrial Information Integration, ISSN 2467-964X, E-ISSN 2452-414X, Vol. 24, no Dec 2021, article id 100225Article in journal (Refereed) Published
Abstract [en]

The IEEE 802.11-based Wireless Local Area Network (WLAN) has become a ubiquitous networking technology deployed around the world. IEEE 802.11 WLAN are now widely used for real-time multimedia applications (e.g. voice and video streaming) and distributed emergency services such as telemedicine, healthcare, and disaster recovery. Both time-sensitive applications and emergency traffic are not only characterized by their high bandwidth requirements, but also impose severe restrictions on end-to-end packet delays (i.e. response time), jitter (i.e. delay variance) and packet losses. In other words, time-sensitive applications and emergency services require a strict Quality of Service (QoS) guarantee. Medium Access Control (MAC) protocol is one of the key factors that influence the performance of WLANs. The IEEE 802.11e working group enhanced the 802.11 MAC to provide QoS support in WLANs. However, recent studies have shown that 802.11e Enhanced Distributed Channel Access (EDCA) standard has limitations and it neither supports strict QoS guarantee nor emergency traffic. Providing a strict QoS guarantee as well as supporting emergency traffic under high traffic loads is really a challenging task in WLANs. A thorough review of literature on QoS MAC protocols reveals that most QoS schemes have focused on either network throughput enhancement or service differentiation by adjusting Contention Window (CW) or Inter-Frame Spaces (IFS). Therefore, a research on developing techniques to provide a strict QoS guarantee as well as support for emergency traffic is required in such systems. To achieve this objective, a general understanding of WLANs is required. This paper aims introduce various key concepts of WLANs that are necessary for design, model and develop such framework. Our main contribution in this paper is the QoS for IEEE 802.11 WLAN and MAC protocols for supporting industrial emergency traffic over network and future directions.

Keywords
802.11e, EDCA, MAC, QoS, Emergency traffic support
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-42878 (URN)10.1016/j.jii.2021.100225 (DOI)000725689200007 ()2-s2.0-85110183983 (Scopus ID)
Available from: 2021-08-25 Created: 2021-08-25 Last updated: 2022-07-27Bibliographically approved
Zahid, N., Wang, L., Sodhro, A. H., Gumaei, A., Al-Rakhami, M. S. & Pirbhulal, S. (2021). An Adaptive Energy Optimization Mechanism for Decentralized Smart Healthcare Applications. In: 2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING): . Paper presented at 93rd IEEE Vehicular Technology Conference (VTC) 2021-Spring, Helsinki, Finland, April 25-28, 2021. (pp. 1-5). IEEE
Open this publication in new window or tab >>An Adaptive Energy Optimization Mechanism for Decentralized Smart Healthcare Applications
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2021 (English)In: 2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), IEEE, 2021, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

 Body Sensor Networks (BSNs) is the emergingdriver to revolutionize the entire landscape of the medicalfield. However, sensor-based handheld devices suffer fromhigh power drain and limited battery life, due to theirresource-constrained nature. Hybridization of differentenergy control methods and protocol layers is a best approachto enhance the performance perimeters for smart andconnected healthcare. Thus, this paper mainly contributes intwo ways. First, adaptive duty-cycle optimization algorithm(ADO), is proposed which optimizes the active time byconsidering the specific power level which leads to moreenergy saving instead of increasing the sleep period unlike thetraditional methods. Second, joint Green and sustainablehealthcare framework is proposed. Extensive theoretical andexperimental analysis is performed by adopting real-time datasets with Monte Carlo simulation in MATLAB, and it isrevealed that proposed algorithm enhance reliability andenergy saving by 24.43%, 36.54%, respectively. Thus it canbe said that proposed algorithm have more potential forenergy constrained sensor devices in smart and connectedhealthcare platform. 

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Green and sustainable systems, smart and connected, healthcare, ADO
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-42720 (URN)10.1109/VTC2021-Spring51267.2021.9448673 (DOI)000687839600044 ()2-s2.0-85112448939 (Scopus ID)978-1-7281-8964-2 (ISBN)
Conference
93rd IEEE Vehicular Technology Conference (VTC) 2021-Spring, Helsinki, Finland, April 25-28, 2021.
Available from: 2021-07-29 Created: 2021-07-29 Last updated: 2021-09-23Bibliographically approved
Lakhan, A., Ali Dootio, M., Sodhro, A. H., Pirbhulal, S., Groenli, T. M., Khokhar, M. S. & Wang, L. (2021). Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things. Mathematical Biosciences and Engineering, 18(6), 7344-7362
Open this publication in new window or tab >>Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things
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2021 (English)In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 18, no 6, p. 7344-7362Article in journal (Refereed) Published
Abstract [en]

These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.

Keywords
serverless, IoMT, workflow, service selection, cost-efficient scheduling
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-42924 (URN)10.3934/mbe.2021363 (DOI)000697475900005 ()2-s2.0-85114647785 (Scopus ID)
Available from: 2021-09-02 Created: 2021-09-02 Last updated: 2021-10-21Bibliographically approved
Sodhro, A. H., Wang, L., Zahid, N., Nisar, K., Al-Rakhami, M. S., Magsi, H., . . . Ahmad, A. (2021). Decentralized Energy Efficient Model for Data Transmission in IoT-based Healthcare System. In: 2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING): . Paper presented at 93rd IEEE Vehicular Technology Conference (VTC) 2021-Spring, Helsinki, Finland, April 25-28, 2021. (pp. 1-5). IEEE
Open this publication in new window or tab >>Decentralized Energy Efficient Model for Data Transmission in IoT-based Healthcare System
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2021 (English)In: 2021 IEEE 93RD VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-SPRING), IEEE, 2021, p. 1-5Conference paper, Published paper (Refereed)
Abstract [en]

 The growing world population is facing challengessuch as increased chronic diseases and medical expenses.Integrate the latest modern technology into healthcare systemcan diminish these issues. Internet of medical things (IoMT) isthe vision to provide the better healthcare system. The IoMTcomprises of different sensor nodes connected together. TheIoMT system incorporated with medical devices (sensors) forgiven the healthcare facilities to the patient and physician canhave capability to monitor the patients very efficiently. Themain challenge for IoMT is the energy consumption, batterycharge consumption and limited battery lifetime in sensor basedmedical devices. During charging the charges that are stored inbattery and these charges are not fully utilized due to nonlinearity of discharging process. The short time period neededto restore these unused charges is referred as recovery effect. Analgorithm exploiting recovery effect to extend the batterylifetime that leads to low consumption of energy. This paperprovides the proposed adaptive Energy efficient (EEA)algorithm that adopts this effect for enhancing energyefficiency, battery lifetime and throughput. The results havebeen simulated on MATLAB by considering the Li-ion battery.The proposed adaptive Energy efficient (EEA) algorithm is alsocompared with other state of the art existing method named,BRLE. The Proposed algorithm increased the lifetime ofbattery, energy consumption and provides the improvedperformance as compared to BRLE algorithm. It consumes lowenergy and supports continuous connectivity of devices withoutany loss/ interruptions. 

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Internet of medical things, recovery effect, EEA, discharging, battery charge.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-42719 (URN)10.1109/VTC2021-Spring51267.2021.9448886 (DOI)000687839601087 ()2-s2.0-85112452917 (Scopus ID)978-1-7281-8964-2 (ISBN)
Conference
93rd IEEE Vehicular Technology Conference (VTC) 2021-Spring, Helsinki, Finland, April 25-28, 2021.
Available from: 2021-07-29 Created: 2021-07-29 Last updated: 2021-09-23Bibliographically approved
Sodhro, A. H., Ahlin, K., Mozelius, P. & Ahmad, A. (2021). Internet of Medical Things for Independent Living and Re-Learning. In: Internet of Medical Things for Independent Living and Re-Learning: . Paper presented at GLOBAL HEALTH 2021 : The Tenth International Conference on Global Health Challenges, Barcelona, Spain, October 3-7, 2021. (pp. 1-5).
Open this publication in new window or tab >>Internet of Medical Things for Independent Living and Re-Learning
2021 (English)In: Internet of Medical Things for Independent Living and Re-Learning, 2021, p. 1-5Conference paper, Published paper (Refereed) [Artistic work]
Abstract [en]

This position paper gives better insight about the role and importance of Internet of Medical Things (IoMT) for independent living and re-learning for older adults. Sensing Technologies are the paradigm shift for transforming conventional healthcare practices into the smart, and self-assisted activities, which are envisioned for today's medical world. Internet of Things (IoT) and IoMT are the interrelated technologies for promoting independent living and re-learning practices. In this paper, re-learning is defined as the process for adults to recover useful instrumental activities of daily living skills that have been lost after an impairment.

Keywords
IoMT, Independent Living, Healthcare
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-43578 (URN)
Conference
GLOBAL HEALTH 2021 : The Tenth International Conference on Global Health Challenges, Barcelona, Spain, October 3-7, 2021.
Available from: 2021-11-01 Created: 2021-11-01 Last updated: 2021-11-02Bibliographically approved
Sodhro, A. H., Rodrigues, J. J. P., Pirbhulal, S., Zahid, N., de Macedo, A. R. & de Albuquerque, V. H. (2021). Link Optimization in Software Defined IoV driven Autonomous Transportation System. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3511-3520
Open this publication in new window or tab >>Link Optimization in Software Defined IoV driven Autonomous Transportation System
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2021 (English)In: IEEE Transactions on Intelligent Transportation Systems, Vol. 22, no 6, p. 3511-3520Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Software-defined IoV, link optimization, vehicular networks, SSLO, autonomous, ITS
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-42871 (URN)10.1109/TITS.2020.2973878 (DOI)
Available from: 2021-08-25 Created: 2021-08-25 Last updated: 2021-09-07Bibliographically approved
Lakhan, A., Dootio, M. A., Groenli, T. M., Sodhro, A. H. & Khokhar, M. S. (2021). Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks. Electronics, 10(14), Article ID 1719.
Open this publication in new window or tab >>Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks
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2021 (English)In: Electronics, E-ISSN 2079-9292, Vol. 10, no 14, article id 1719Article in journal (Refereed) [Artistic work] Published
Abstract [en]

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

Place, publisher, year, edition, pages
Switzerland: MDPI, 2021
Keywords
MLAWAS, Q-Learning, response-time, simulation, assignment
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-42718 (URN)10.3390/electronics10141719 (DOI)000676598600001 ()2-s2.0-85110307111 (Scopus ID)
Available from: 2021-07-29 Created: 2021-07-29 Last updated: 2022-02-02Bibliographically approved
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