Smart City Gnosys
Smart city article details
| Title | Biomcs: A Bio-Inspired Collaborative Data Transfer Framework Over Fog Computing Platforms In Mobile Crowdsensing |
|---|---|
| ID_Doc | 12271 |
| Authors | Roy S.; Ghosh N.; Ghosh P.; Das S.K. |
| Year | 2020 |
| Published | ACM International Conference Proceeding Series, Part F165625 |
| DOI | http://dx.doi.org/10.1145/3369740.3369788 |
| 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. © 2020 ACM. |
| Author Keywords | Collaborative sensing; Fog computing; Mobile crowdsensing; Smart city applications; Transcriptional regulatory network |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
570  | 0.876 | Belli D.; Chessa S.; Kantarci B.; Foschini L. | A Capacity-Aware User Recruitment Framework For Fog-Based Mobile Crowd-Sensing Platforms | Proceedings - IEEE Symposium on Computers and Communications, 2019-June (2019) |
| 12289 | 0.874 | Roy S.; Ghosh N.; Das S.K. | Biosmartsense: A Bio-Inspired Data Collection Framework For Energy-Efficient, Qoi-Aware Smart City Applications | 2019 IEEE International Conference on Pervasive Computing and Communications, PerCom 2019 (2019) |
| 31040 | 0.862 | Du Y.; Issarny V.; Sailhan F. | In-Network Collaborative Mobile Crowdsensing | 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 (2020) |
| 5177 | 0.855 | Ray A.; Chowdhury C.; Bhattacharya S.; Roy S. | A Survey Of Mobile Crowdsensing And Crowdsourcing Strategies For Smart Mobile Device Users | CCF Transactions on Pervasive Computing and Interaction, 5, 1 (2023) |
| 44699 | 0.853 | Wildan M.A.; Widyaningrum M.E.; Padmapriya T.; Sah B.; Pani N.K. | Recruitment Algorithm In Edge-Cloud Servers Based On Mobile Crowd-Sensing In Smart Cities | International Journal of Interactive Mobile Technologies, 17, 16 (2023) |