Folo: Latency and quality optimized task allocation in vehicular fog computing

IEEE Internet of Things Journal

Volumn 6, Issue 3, 2019, Pages 4150-4161

  Zhu, Chao ^a   Tao, Jin ^a,d   Pastor, Giancarlo ^a   Xiao, Yu ^a   Ji, Yusheng ^b   Zhou, Quan ^a   Li, Yong ^c   Yla Jaaski, Antti ^a  

^a AALTO UNIVERSITY   (Finland)

^b NATIONAL INSTITUTE OF INFORMATICS   (Japan)

^c TSINGHUA UNIVERSITY   (China)

^d PEKING UNIVERSITY   (China)

Author keywords

Binary particle swarm optimization (BPSO); Computation offloading; Dynamic task allocation (DTA); Linear programming (LP); Vehicular fog computing (VFC)

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; FOG; LINEAR PROGRAMMING; OBJECT RECOGNITION; PARTICLE SWARM OPTIMIZATION (PSO); POLYNOMIAL APPROXIMATION; TAXICABS;

BINARY PARTICLE SWARM OPTIMIZATION; COMPUTING OFFLOADING; DYNAMIC TASK ALLOCATION; MINIMIZATION PROBLEMS; REAL-TIME OBJECT RECOGNITION; SITUATIONAL AWARENESS; VEHICULAR APPLICATIONS; VEHICULAR FOG COMPUTING (VFC);

FOG COMPUTING;

EID: 85054605724     PISSN: None     EISSN: 23274662     Source Type: Journal    
DOI: 10.1109/JIOT.2018.2875520     Document Type: Article

References (50)

1. Toward Fully Connected Vehicles: Edge Computing for Advanced Automotive Communications, 5G Autom. Assoc., Munich, Germany, Dec. 2017.
2. S. Kumar et al., "CarSpeak: A content-centric network for autonomous driving," SIGCOMM Comput. Commun. Rev., vol. 42, no. 4, pp. 259-270, Aug. 2012.
3. C. Tran, K. Bark, and V. Ng-Thow-Hing, "A left-turn driving aid using projected oncoming vehicle paths with augmented reality," in Proc. 5th Int. Conf. Autom. User Interfaces Interact. Veh. Appl. (AutomotiveUI), Eindhoven, The Netherlands, 2013, pp. 300-307.
4. P. Gomes, C. Olaverri-Monreal, and M. Ferreira, "Making vehicles transparent through V2V video streaming," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 930-938, Jun. 2012.
5. H. Qiu et al., "Augmented vehicular reality: Enabling extended vision for future vehicles," in Proc. 18th Int. Workshop Mobile Comput. Syst. Appl. (HotMobile), 2017, pp. 67-72.
6. F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog computing and its role in the Internet of Things," in Proc. 1st Ed. MCC Workshop Mobile Cloud Comput. (MCC), Helsinki, Finland, 2012, pp. 13-16.
7. M. Chiang and T. Zhang, "Fog and IoT: An overview of research opportunities," IEEE Internet Things J., vol. 3, no. 6, pp. 854-864, Dec. 2016.
8. Y. Xiao and C. Zhu, "Vehicular fog computing: Vision and challenges," in Proc. IEEE Int. Conf. Pervasive Comput. Commun. Workshops (PerCom Workshops), Mar. 2017, pp. 6-9.
9. M. Satyanarayanan, "Edge computing for situational awareness," in Proc. IEEE Int. Symp. Local Metropolitan Area Netw. (LANMAN), Jun. 2017, pp. 1-6.
10. C. Zhu, G. Pastor, Y. Xiao, Y. Li, and A. Ylae-Jaeaeski, "Fog following me: Latency and quality balanced task allocation in vehicular fog computing," in Proc. 15th Annu. IEEE Int. Conf. Sens. Commun. Netw. (SECON), Jun. 2018, pp. 1-9.
11. Y. Li, Y. Chen, T. Lan, and G. Venkataramani, "MobiQoR: Pushing the envelope of mobile edge computing via quality-of-result optimization," in Proc. IEEE 37th Int. Conf. Distrib. Comput. Syst. (ICDCS), Jun. 2017, pp. 1261-1270.
12. iData Laboratory, Tongji Univ., Shanghai, China, 2015. Accessed: Dec. 12, 2017.
13. F. Hagenauer, F. Dressler, and C. Sommer, "Poster: A simulator for heterogeneous vehicular networks," in Proc. IEEE Veh. Netw. Conf. (VNC), Dec. 2014, pp. 185-186.
14. P. Corcoran and S. K. Datta, "Mobile-edge computing and the Internet of Things for consumers: Extending cloud computing and services to the edge of the network," IEEE Consum. Electron. Mag., vol. 5, no. 4, pp. 73-74, Oct. 2016.
15. X. Hou et al., "Vehicular fog computing: A viewpoint of vehicles as the infrastructures," IEEE Trans. Veh. Technol., vol. 65, no. 6, pp. 3860-3873, Jun. 2016.
16. J. Ni, A. Zhang, X. Lin, and X. S. Shen, "Security, privacy, and fairness in fog-based vehicular crowdsensing," IEEE Commun. Mag., vol. 55, no. 6, pp. 146-152, Jun. 2017.
17. R. Yu, G. Xue, and X. Zhang, "Application provisioning in fog computing-enabled Internet-of-Things: A network perspective," in Proc. IEEE INFOCOM, 2018, pp. 783-791.
18. O. Skarlat, S. Schulte, M. Borkowski, and P. Leitner, "Resource provisioning for IoT services in the fog," in Proc. IEEE 9th Int. Conf. Service Orient. Comput. Appl. (SOCA), Nov. 2016, pp. 32-39.
19. J. Xu, B. Palanisamy, H. Ludwig, and Q. Wang, "Zenith: Utility-aware resource allocation for edge computing," in Proc. IEEE Int. Conf. Edge Comput. (EDGE), Jun. 2017, pp. 47-54.
20. J. Ni, K. Zhang, Y. Yu, X. Lin, and X. S. Shen, "Providing task allocation and secure deduplication for mobile crowdsensing via fog computing," IEEE Trans. Depend. Secure Comput., to be published, doi: 10.1109/TDSC.2018.2791432.
21. Y. Gu, Z. Chang, M. Pan, L. Song, and Z. Han, "Joint radio and computational resource allocation in IoT fog computing," IEEE Trans. Veh. Technol., vol. 67, no. 8, pp. 7475-7484, Aug. 2018.
22. J. Du, L. Zhao, J. Feng, and X. Chu, "Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee," IEEE Trans. Commun., vol. 66, no. 4, pp. 1594-1608, Apr. 2018.
23. L. Ni, J. Zhang, C. Jiang, C. Yan, and K. Yu, "Resource allocation strategy in fog computing based on priced timed Petri nets," IEEE Internet Things J., vol. 4, no. 5, pp. 1216-1228, Oct. 2017.
24. L. Wang et al., "SPACE-TA: Cost-effective task allocation exploiting intradata and interdata correlations in sparse crowdsensing," ACM Trans. Intell. Syst. Technol., vol. 9, no. 2, pp. 1-20, Oct. 2017.
25. S. Sardellitti, G. Scutari, and S. Barbarossa, "Joint optimization of radio and computational resources for multicell mobile-edge computing," IEEE Trans. Signal Inf. Process. Netw., vol. 1, no. 2, pp. 89-103, Jun. 2015.
26. Y. Liu, M. J. Lee, and Y. Zheng, "Adaptive multi-resource allocation for cloudlet-based mobile cloud computing system," IEEE Trans. Mobile Comput., vol. 15, no. 10, pp. 2398-2410, Oct. 2016.
27. T. Q. Dinh, J. Tang, Q. D. La, and T. Q. S. Quek, "Offloading in mobile edge computing: Task allocation and computational frequency scaling," IEEE Trans. Commun., vol. 65, no. 8, pp. 3571-3584, Aug. 2017.
28. R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang, "Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption," IEEE Internet Things J., vol. 3, no. 6, pp. 1171-1181, Dec. 2016.
29. J. Feng, Z. Liu, C. Wu, and Y. Ji, "AVE: Autonomous vehicular edge computing framework with ACO-based scheduling," IEEE Trans. Veh. Technol., vol. 66, no. 12, pp. 10660-10675, Dec. 2017.
30. J. B. Kenney, "Dedicated short-range communications (DSRC) standards in the United States," Proc. IEEE, vol. 99, no. 7, pp. 1162-1182, Jul. 2011.
31. W. Hu et al., "Live synthesis of vehicle-sourced data over 4G LTE," in Proc. 20th ACM Int. Conf. Model. Anal. Simulat. Wireless Mobile Syst., 2017, pp. 161-170.
32. E. Jonas, Q. Pu, S. Venkataraman, I. Stoica, and B. Recht, "Occupy the cloud: Distributed computing for the 99%," in Proc. Symp. Cloud Comput. (SoCC), 2017, pp. 445-451.
33. How to Integrate CRIU Pre-Dump Feature With Docker for Iterative Migration? Issue #456. Checkpoint-Restore/Criu. [Online]. Available: https://github.com/checkpoint-restore/criu/issues/456
34. M. Noreikis, Y. Xiao, and A. Ylä-Jaäiski, "QoS-oriented capacity planning for edge computing," in Proc. IEEE Int. Conf. Commun. (ICC), May 2017, pp. 1-6.
35. N. Karmarkar, "A new polynomial-time algorithm for linear programming," Combinatorica, vol. 4, no. 4, pp. 373-395, 1984.
36. J. Kennedy, "Particle swarm optimization," in Encyclopedia of Machine Learning. New York, NY, USA: Springer, 2010, pp. 760-766.
37. A. Salman, I. Ahmad, and S. Al-Madani, "Particle swarm optimization for task assignment problem," Microprocess. Microsyst., vol. 26, no. 8, pp. 363-371, Nov. 2002.
38. J. Tao, Q. Sun, P. Tan, Z. Chen, and Y. He, "Active disturbance rejection control (ADRC)-based autonomous homing control of powered parafoils," Nonlin. Dyn., vol. 86, no. 3, pp. 1461-1476, Nov. 2016.
39. S. Biswas, S. Kundu, and S. Das, "Inducing niching behavior in differential evolution through local information sharing," IEEE Trans. Evol. Comput., vol. 19, no. 2, pp. 246-263, Apr. 2015.
40. X. Wang, H. Zhang, S. Fan, and H. Gu, "Coverage control of sensor networks in IoT based on RPSO," IEEE Internet Things J., to be published, doi: 10.1109/JIOT.2018.2829160.
41. L. Tong, X. Li, J. Hu, and L. Ren, "A PSO optimization scaletransformation stochastic-resonance algorithm with stability mutation operator," IEEE Access, vol. 6, pp. 1167-1176, 2018.
42. L. López et al., "Kurento: The WebRTC modular media server," in Proc. ACM Multimedia Conf. (MM), 2016, pp. 1187-1191.
43. P. Forret. Video Filesize Calculator-Toolstudio. [Online]. Available: https://toolstud.io/video/filesize.php
44. J. Redmon and A. Farhadi, "YOLO9000: Better, faster, stronger," arXiv:1612.08242 [cs], Dec. 2016.
45. L. Codeca, R. Frank, and T. Engel, "Luxembourg SUMO traffic (LuST) scenario: 24 hours of mobility for vehicular networking research," in Proc. IEEE Veh. Netw. Conf. (VNC), Dec. 2015, pp. 1-8.
46. Z. Xu, X. Li, X. Zhao, M. H. Zhang, and Z. Wang, "DSRC versus 4GLTE for connected vehicle applications: A study on field experiments of vehicular communication performance," J. Adv. Transp., vol. 2017, Aug. 2017, Art. no. 2750452.
47. G. Araniti, C. Campolo, M. Condoluci, A. Iera, and A. Molinaro, "LTE for vehicular networking: A survey," IEEE Commun. Mag., vol. 51, no. 5, pp. 148-157, May 2013.
48. C. P. Schnorr and M. Euchner, "Lattice basis reduction: Improved practical algorithms and solving subset sum problems," Math. Program., vol. 66, nos. 1-3, pp. 181-199, Aug. 1994.
49. C. Sommer, R. German, and F. Dressler, "Bidirectionally coupled network and road traffic simulation for improved IVC analysis," IEEE Trans. Mobile Comput., vol. 10, no. 1, pp. 3-15, Jan. 2011.
50. D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker, "Recent development and applications of SUMO-Simulation of urban mobility," Int. J. Adv. Syst. Meas., vol. 5, nos. 3-4, pp. 128-138, Dec. 2012.