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Design of an Efficient QoS-Aware Adaptive Data Dissemination Engine with DTFC for Mobile Edge Computing Deployments


Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, Punjab, India
2 Menzies Institute of Technology, Melbourne, Australia
 

In the transformative landscape of mobile edge computing (MEC), where the convergence of computation and communication fuels the era of ubiquitous connectivity, formidable challenges loom large. The burgeoning demand for real-time, data-intensive applications places unprecedented pressure on existing infrastructure, demanding innovative solutions to address the intricate web of challenges. This paper embarks on a compelling journey through the realm of MEC, uncovering the multifaceted challenges that have hitherto impeded its seamless integration into our digital lives. As the proliferation of mobile devices and their insatiable appetite for data strain the network's capacity, latency becomes a formidable adversary, threatening the integrity of applications requiring split-second responsiveness. Furthermore, the capricious nature of mobile devices and their mobility introduces an unpredictable dynamism into the network topology, rendering traditional traffic control approaches ineffective. The consequence is a tangled web of congestion, resource underutilization, and compromised Quality of Service (QoS), all of which hinder the realization of MEC's full potential. In response to these challenges, we unveil a pioneering solution—a QoS-aware Adaptive Data Dissemination Engine (QADE) paired with Dynamic Traffic Flow Control (DTFC). This synergistic model augments the capabilities of MEC deployments by harnessing the power of content-based routing and advanced optimization techniques. QADE, with its innovative utilization of Elephant Herding Particle Swarm Optimizer (EHPSO), refines data dissemination processes with an unprecedented focus on QoS metrics. Temporal delay, energy consumption, throughput, and Packet Delivery Ratio (PDR) become our guiding stars in the quest for routing efficiency. By harnessing this wealth of information, QADE emerges as a beacon of efficiency, driving latency to its lowest ebb, magnifying bandwidth, mitigating packet loss, elevating throughput, and rationalizing operational costs. DTFC complements this endeavor by dynamically steering traffic flows by edge processing capacity, thereby circumventing congestion pitfalls and achieving resource utilization efficiency hitherto considered unattainable. In a series of exhaustive evaluations, our proposed QADE with DTFC emerges as a beacon of hope, surpassing traditional methodologies. With an 8.5% reduction in latency compared to RL, a 16.4% reduction compared to MTO SA, and an impressive 18.0% reduction compared to HFL, it ushers in a new era of real-time data dissemination. By championing QoS awareness, adaptability, and efficiency, this study catapults mobile edge computing into a future defined by resource optimization and stellar network performance, ushering in an era where challenges bow before innovation processes.

Keywords

Data, Dissemination, Trust, Routing, Data Flow, Control, Scenarios.
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  • Q. Huang, Y. Yang, and J. Fu, "Secure Data Group Sharing and Dissemination with Attribute and Time Conditions in Public Cloud," in IEEE Transactions on Services Computing, vol. 14, no. 4, pp. 1013-1025, 1 July-Aug. 2021, doi 10.1109/TSC.2018.2850344.

  • Q. Huang, Y. Yang, W. Yue, and Y. He, "Secure Data Group Sharing and Conditional Dissemination with Multi-Owner in Cloud Computing," in IEEE Transactions on Cloud Computing, vol. 9, no. 4, pp. 1607-1618, 1 Oct.-Dec. 2021, doi 10.1109/TCC.2019.2908163.

  • K. Liu, K. Xiao, P. Dai, V. C. S. Lee, S. Guo, and J. Cao, "Fog Computing Empowered Data Dissemination in Software Defined Heterogeneous VANETs," in IEEE Transactions on Mobile Computing, vol. 20, no. 11, pp. 3181-3193, 1 Nov. 2021, doi: 10.1109/TMC.2020.2997460.

  • J. Liu, J. Yang, W. Wu, X. Huang and Y. Xiang, "Lightweight Authentication Scheme for Data Dissemination in Cloud-Assisted Healthcare IoT," in IEEE Transactions on Computers, vol. 72, no. 5, pp. 1384-1395, 1 May 2023, doi: 10.1109/TC.2022.3207138.

  • W. Zhang, Z. Li, and X. Chen, "Quality-aware user recruitment based on federated learning in mobile crowd sensing," in Tsinghua Science and Technology, vol. 26, no. 6, pp. 869-877, Dec. 2021, doi: 10.26599/TST.2020.9010046.

  • J. Liu et al., "Leakage-Free Dissemination of Authenticated Tree-Structured Data with Multi-Party Control," in IEEE Transactions on Computers, vol. 70, no. 7, pp. 1120-1131, 1 July 2021, doi: 10.1109/TC.2020.3006835.

  • Y. Bao, W. Qiu, P. Tang, and X. Cheng, "Efficient, Revocable, and Privacy-Preserving Fine-Grained Data Sharing with Keyword Search for the Cloud-Assisted Medical IoT System," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2041-2051, May 2022, doi: 10.1109/JBHI.2021.3100871.

  • L. Zhu, M. M. Karim, K. Sharif, C. Xu, and F. Li, "Traffic Flow Optimization for UAVs in Multi-Layer Information-Centric Software-Defined FANET," in IEEE Transactions on Vehicular Technology, vol. 72, no. 2, pp. 2453-2467, Feb. 2023, doi: 10.1109/TVT.2022.3213040.

  • C. Zhang, M. Yu, W. Wang, and F. Yan, "Enabling Cost-Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud," in IEEE Transactions on Cloud Computing, vol. 10, no. 3, pp. 1765-1779, 1 July-Sept. 2022, doi 10.1109/TCC.2020.3006751.

  • X. Li, X. Yin and J. Ning, "Trustworthy Announcement Dissemination Scheme with Blockchain-Assisted Vehicular Cloud," in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 2, pp. 1786-1800, Feb. 2023, doi: 10.1109/TITS.2022.3220580.

  • J. Liu et al., "Conditional Anonymous Remote Healthcare Data Sharing Over Blockchain," in IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 5, pp. 2231-2242, May 2023, doi: 10.1109/JBHI.2022.3183397.

  • Q. Deng, Y. Ouyang, S. Tian, R. Ran, J. Gui, and H. Sekiya, "Early Wake-Up Ahead Node for Fast Code Dissemination in Wireless Sensor Networks," in IEEE Transactions on Vehicular Technology, vol. 70, no. 4, pp. 3877-3890, April 2021, doi: 10.1109/TVT.2021.3066216.

  • G. Manogaran, M. Alazab, K. Muhammad and V. H. C. de Albuquerque, "Smart Sensing Based Functional Control for Reducing Uncertainties in Agricultural Farm Data Analysis," in IEEE Sensors Journal, vol. 21, no. 16, pp. 17469-17478, 15 Aug.15, 2021, doi: 10.1109/JSEN.2021.3054561.

  • Q. Kong, R. Lu, F. Yin, and S. Cui, "Blockchain-Based Privacy-Preserving Driver Monitoring for MaaS in the Vehicular IoT," in IEEE Transactions on Vehicular Technology, vol. 70, no. 4, pp. 3788-3799, April 2021, doi: 10.1109/TVT.2021.3064834.

  • J. G. Chamani, Y. Wang, D. Papadopoulos, M. Zhang, and R. Jalili, "Multi-User Dynamic Searchable Symmetric Encryption with Corrupted Participants," in IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 114-130, 1 Jan.-Feb. 2023, doi: 10.1109/TDSC.2021.3127546.

  • M. Zhang, J. Zhou, P. Cong, G. Zhang, C. Zhuo, and S. Hu, "LIAS: A Lightweight Incentive Authentication Scheme for Forensic Services in IoV," in IEEE Transactions on Automation Science and Engineering, vol. 20, no. 2, pp. 805-820, April 2023, doi: 10.1109/TASE.2022.3165174.

  • T. Ye, M. Luo, Y. Yang, K. -K. R. Choo and D. He, "A Survey on Redactable Blockchain: Challenges and Opportunities," in IEEE Transactions on Network Science and Engineering, vol. 10, no. 3, pp. 1669-1683, 1 May-June 2023, doi: 10.1109/TNSE.2022.3233448.

  • S. U. Jamil, M. A. Khan, and S. U. Rehman, "Resource Allocation and Task Off-Loading for 6G Enabled Smart Edge Environments," in IEEE Access, vol. 10, pp. 93542-93563, 2022, doi: 10.1109/ACCESS.2022.3203711.

  • R. H. Kim, H. Noh, H. Song and G. S. Park, "Quick Block Transport System for Scalable Hyperledger Fabric Blockchain Over D2D-Assisted 5G Networks," in IEEE Transactions on Network and Service Management, vol. 19, no. 2, pp. 1176-1190, June 2022, doi: 10.1109/TNSM.2021.3122923.

  • J. Bai et al., "Adversarial Knowledge Distillation Based Biomedical Factoid Question Answering," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 1, pp. 106-118, 1 Jan.-Feb. 2023, doi: 10.1109/TCBB.2022.3161032.

  • S. Yaqoob, A. Hussain, F. Subhan, G. Pappalardo, and M. Awais, "Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network," in IEEE Access, vol. 11, pp. 19024-19038, 2023, doi 10.1109/ACCESS.2023.3246660.

  • N. Kumar, R. Chaudhry, O. Kaiwartya, N. Kumar and S. H. Ahmed, "Green Computing in Software Defined Social Internet of Vehicles," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3644-3653, June 2021, doi: 10.1109/TITS.2020.3028695.

  • Z. Li, H. Du and X. Chen, "A Two-Stage Incentive Mechanism Design for Quality Optimization of Hierarchical Federated Learning," in IEEE Access, vol. 10, pp. 132752-132762, 2022, doi: 10.1109/ACCESS.2022.3230695.

  • N. Magaia, P. Ferreira, P. R. Pereira, K. Muhammad, J. D. Ser and V. H. C. de Albuquerque, "Group'n Route: An Edge Learning-Based Clustering and Efficient Routing Scheme Leveraging Social Strength for the Internet of Vehicles," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 19589-19601, Oct. 2022, doi 10.1109/TITS.2022.3171978.

  • Y. Hu, X. Yao, R. Zhang, and Y. Zhang, "Freshness Authentication for Outsourced Multi-Version Key-Value Stores," in IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 3, pp. 2071-2084, 1 May-June 2023, doi: 10.1109/TDSC.2022.3172380.

  • G. Kaur and R. S. Batth, "Edge Computing: Classification, Applications, and Challenges," 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2021, pp. 254-259, doi: 10.1109/ICIEM51511.2021.9445331.

  • IOP Conference Series: Materials Science and Engineering, Volume 993, International Conference on Mechanical, Electronics and Computer Engineering 2020 22 April 2020, Kancheepuram, India, Chandini et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 993 012060

  • A. Vashisth, R. Singh Batth, and R. Ward, "Existing Path Planning Techniques in Unmanned Aerial Vehicles (UAVs): A Systematic Review," 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, 2021, pp. 366-372, doi: 10.1109/ICCIKE51210.2021.9410787.

  • A. Vashisth and R. S. Batth, "An Overview, Survey, and Challenges in UAVs Communication Network," 2020 International Conference on Intelligent Engineering and Management (ICIEM), London, UK, 2020, pp. 342-347, doi: 10.1109/ICIEM48762.2020.9160197.

  • N. Yadav, G. Kaur, S. Kaur, A. Vashisth and C. Rohith, "A Complete Study on Malware Types and Detecting Ransomware Using API Calls," 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2021, pp. 1-5, doi: 10.1109/ICRITO51393.2021.9596085.


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  • Design of an Efficient QoS-Aware Adaptive Data Dissemination Engine with DTFC for Mobile Edge Computing Deployments

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Authors

Gagandeep Kaur
School of Computer Science and Engineering, Lovely Professional University, Punjab, India
Balraj Singh
School of Computer Science and Engineering, Lovely Professional University, Punjab, India
Ranbir Singh Batth
Menzies Institute of Technology, Melbourne, Australia

Abstract


In the transformative landscape of mobile edge computing (MEC), where the convergence of computation and communication fuels the era of ubiquitous connectivity, formidable challenges loom large. The burgeoning demand for real-time, data-intensive applications places unprecedented pressure on existing infrastructure, demanding innovative solutions to address the intricate web of challenges. This paper embarks on a compelling journey through the realm of MEC, uncovering the multifaceted challenges that have hitherto impeded its seamless integration into our digital lives. As the proliferation of mobile devices and their insatiable appetite for data strain the network's capacity, latency becomes a formidable adversary, threatening the integrity of applications requiring split-second responsiveness. Furthermore, the capricious nature of mobile devices and their mobility introduces an unpredictable dynamism into the network topology, rendering traditional traffic control approaches ineffective. The consequence is a tangled web of congestion, resource underutilization, and compromised Quality of Service (QoS), all of which hinder the realization of MEC's full potential. In response to these challenges, we unveil a pioneering solution—a QoS-aware Adaptive Data Dissemination Engine (QADE) paired with Dynamic Traffic Flow Control (DTFC). This synergistic model augments the capabilities of MEC deployments by harnessing the power of content-based routing and advanced optimization techniques. QADE, with its innovative utilization of Elephant Herding Particle Swarm Optimizer (EHPSO), refines data dissemination processes with an unprecedented focus on QoS metrics. Temporal delay, energy consumption, throughput, and Packet Delivery Ratio (PDR) become our guiding stars in the quest for routing efficiency. By harnessing this wealth of information, QADE emerges as a beacon of efficiency, driving latency to its lowest ebb, magnifying bandwidth, mitigating packet loss, elevating throughput, and rationalizing operational costs. DTFC complements this endeavor by dynamically steering traffic flows by edge processing capacity, thereby circumventing congestion pitfalls and achieving resource utilization efficiency hitherto considered unattainable. In a series of exhaustive evaluations, our proposed QADE with DTFC emerges as a beacon of hope, surpassing traditional methodologies. With an 8.5% reduction in latency compared to RL, a 16.4% reduction compared to MTO SA, and an impressive 18.0% reduction compared to HFL, it ushers in a new era of real-time data dissemination. By championing QoS awareness, adaptability, and efficiency, this study catapults mobile edge computing into a future defined by resource optimization and stellar network performance, ushering in an era where challenges bow before innovation processes.

Keywords


Data, Dissemination, Trust, Routing, Data Flow, Control, Scenarios.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F223420