research-article

bioMCS: A Bio-inspired Collaborative Data Transfer Framework over Fog Computing Platforms in Mobile Crowdsensing

Authors:

Sajal K. DasAuthors Info & Claims

ICDCN '20: Proceedings of the 21st International Conference on Distributed Computing and Networking

Article No.: 23, Pages 1 - 10

https://doi.org/10.1145/3369740.3369788

Published: 19 February 2020 Publication History

Abstract

Mobile crowdsensing (MCS) leverages the participation of active citizens and establishes a cost-effective sensing infrastructure using their devices. The MCS platform allocates sensing tasks, for which individual user reports are collected to enable decision making. Task sensing and communication not only consume user's device energy, but also spawn redundant data leading to network congestion and issues in data management at the platform's end. MCS, being a building block of sustainable smart city applications, must ensure judicious utilization of device energy and network resources. To address these challenges, this paper proposes a bio-inspired data transfer framework, bioMCS, deployed over a fog computing platform and capable of enforcing collaborative sensing among proximate users. bioMCS achieves energy efficiency and robustness through the topological properties of a biological network called transcriptional regulatory network. It employs collaborative sensing to further restrict device energy overhead by taking advantage of energy efficient device-to-device communications like Wi-Fi direct data transfer via group owner. We evaluate our framework through extensive simulation-based experiments and demonstrate that the bioMCS framework achieves better energy and network efficiency compared to individual user-centric data transfer mechanism.

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https://doi.org/10.3389/fagro.2024.1410829
Afrasiabi SEbrahimzadeh AMouradian CMalektaji SGlitho R(2023)Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog EnvironmentsIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321772320:2(1866-1883)Online publication date: Jun-2023
https://doi.org/10.1109/TNSM.2022.3217723
Roy SDas S(2023)Bio-Inspired Design of Biosensor NetworksEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00131-X(86-102)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00131-X
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ICDCN '20: Proceedings of the 21st International Conference on Distributed Computing and Networking

January 2020

460 pages

ISBN:9781450377515

DOI:10.1145/3369740

Copyright © 2020 ACM.

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Published: 19 February 2020

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ICDCN 2020: 21st International Conference on Distributed Computing and Networking

January 4 - 7, 2020

Kolkata, India

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Cited By

Singh ABalabaygloo BBekee BBlair SFey SFotouhi FGupta AJha AMartinez-Palomares JMenke KPrestholt ATanwar VTao XVangala ACarroll MDas SDePaula GKyveryga PSarkar SSegovia MSilvestri SValdivia C(2024)Smart connected farms and networked farmers to improve crop production, sustainability and profitabilityFrontiers in Agronomy10.3389/fagro.2024.14108296Online publication date: 8-Aug-2024
https://doi.org/10.3389/fagro.2024.1410829
Afrasiabi SEbrahimzadeh AMouradian CMalektaji SGlitho R(2023)Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog EnvironmentsIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321772320:2(1866-1883)Online publication date: Jun-2023
https://doi.org/10.1109/TNSM.2022.3217723
Roy SDas S(2023)Bio-Inspired Design of Biosensor NetworksEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00131-X(86-102)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00131-X
Roy SGhosh PGhosh NDas S(2021)Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A SurveyACM Computing Surveys10.1145/346826654:8(1-36)Online publication date: 4-Oct-2021
https://dl.acm.org/doi/10.1145/3468266
Hazra KShah VRoy SDeep SSaha SNandi S(2021)Exploring Biological Robustness for Reliable Multi-UAV NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2021.307754418:3(2776-2788)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2021.3077544
Roy SDutta RGhosh NGhosh P(2021)Adaptive Motif-based Topology Control in Mobile Software Defined Wireless Sensor Networks2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49032.2021.9369601(1-6)Online publication date: 9-Jan-2021
https://dl.acm.org/doi/10.1109/CCNC49032.2021.9369601
Roy SGhosh NGhosh PDas S(2021)bioMCS 2.0: A distributed, energy-aware fog-based framework for data forwarding in mobile crowdsensingPervasive and Mobile Computing10.1016/j.pmcj.2021.10138173(101381)Online publication date: Jun-2021
https://doi.org/10.1016/j.pmcj.2021.101381
Seyfollahi AAbeshloo HGhaffari A(2021)Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimizationJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-021-03344-013:9(4543-4558)Online publication date: 13-Jun-2021
https://doi.org/10.1007/s12652-021-03344-0
Majeed DZhang LShi K(2020)Optimal Data Collection for Mobile Crowdsensing Over Integrated Cellular and Opportunistic NetworksIEEE Access10.1109/ACCESS.2020.30195378(157270-157283)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3019537
Roy SGhosh NDas S(2020)bioSmartSense+: A bio-inspired probabilistic data collection framework for priority-based event reporting in IoT environmentsPervasive and Mobile Computing10.1016/j.pmcj.2020.10117967(101179)Online publication date: Sep-2020
https://doi.org/10.1016/j.pmcj.2020.101179

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