Abstract
Recently, the mobile crowd sensing (MCS) technique is believed to be an important role in multi-source data acquisition tasks. With devices or people with different sensing abilities in the cities, we can easily split and distribute the complex task in an appropriate way so that those devices or people can be stimulated to collect data within different scopes individually, while the results of them can be analyzed and integrated collaboratively to fulfill that complex task. However, in typical centralized architecture, the latency brought by unstable and time-consuming long-distance network transmission limits the development of MCS. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a service provisioning system based at the edge of the network, the latency can be reduced and the analysis or integration can also be conducted in time with the help of corresponding services deployed on nearby edge servers. However, as the edge servers are resource-limited, the sensing abilities vary among devices or people, and the budget of fulfilling a task is determined, we should be more careful in task assignment and service deployment. In this paper, we investigate the relationship between the task quality and the cost in the MEC-based MCS system and propose the analysis framework of it based on two classical cost-performance balancing problems. Besides, we conduct comprehensive experiments to evaluate the performance of our approach. The results show that the proposed approach can easily obtain exact solutions, and the factors that may impact the results are also adequately explored.
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References
Chen, Y., Zhou, M., Zheng, Z., Chen, D.: Time-aware smart object recommendation in social Internet of Things. IEEE Internet Things J. 7(3), 2014–2027 (2020)
Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., Zomaya, A.Y.: Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J. 7(8), 7457–7469 (2020)
Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)
Ren, J., Pan, Y., Goscinski, A., Beyah, R.A.: Edge computing for the Internet of Things. IEEE Netw. 32(1), 6–7 (2018)
Wang, D., Xu, D., Yu, D., Xu, G.: Time-aware sequence model for next-item recommendation. Appl. Intell. 1–15 (2020). https://doi.org/10.1007/s10489-020-01820-2
Filippini, I., Sciancalepore, V., Devoti, F., Capone, A.: Fast cell discovery in mm-wave 5G networks with context information. IEEE Trans. Mob. Comput. 17(7), 1538–1552 (2017)
Fan, Q., Ansari, N.: Application aware workload allocation for edge computing-based IoT. IEEE Internet Things J. 5(3), 2146–2153 (2018)
Chen, Y., Deng, S., Ma, H., Yin, J.: Deploying data-intensive applications with multiple services components on edge. Mob. Netw. Appl. 25(2), 426–441 (2020). https://doi.org/10.1007/s11036-019-01245-3
Zhao, H., Deng, S., Zhang, C., Du, W., He, Q., Yin, J.: A mobility-aware cross-edge computation offloading framework for partitionable applications. In: Bertino, E., Chang, C.K., Chen, P., Damiani, E., Goul, M., Oyama, K. (eds.) 2019 IEEE International Conference on Web Services, ICWS 2019, Milan, Italy, 8–13 July 2019, pp. 193–200. IEEE (2019)
Deng, S., Wu, H., Tan, W., Xiang, Z., Wu, Z.: Mobile service selection for composition: an energy consumption perspective. IEEE Trans. Autom. Sci. Eng. 14(3), 1478–1490 (2015)
Gupta, O.K., Ravindran, A.: Branch and bound experiments in convex nonlinear integer programming. Manag. Sci. 31(12), 1533–1546 (1985)
Deng, S., Huang, L., Taheri, J., Yin, J., Zhou, M., Zomaya, A.Y.: Mobility-aware service composition in mobile communities. IEEE Trans. Syst. Man Cybern. Syst. 47(3), 555–568 (2017)
Liu, H., Zhang, J., Zhang, X., Kurniawan, A., Juhana, T., Ai, B.: Tabu-search-based pilot assignment for cell-free massive MIMO systems. IEEE Trans. Veh. Technol. 69(2), 2286–2290 (2019)
Deterding, S., Dixon, D., Khaled, R., Nacke, L.: From game design elements to gamefulness: defining “gamification". In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, pp. 9–15 (2011)
Gao, L., Hou, F., Huang, J.: Providing long-term participation incentive in participatory sensing. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2803–2811. IEEE (2015)
Sun, J.: An incentive scheme based on heterogeneous belief values for crowdsensing in mobile social networks. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1717–1722. IEEE (2013)
Jaimes, L.G., Vergara-Laurens, I., Labrador, M.A.: A location-based incentive mechanism for participatory sensing systems with budget constraints. In: 2012 IEEE International Conference on Pervasive Computing and Communications, pp. 103–108. IEEE (2012)
Dinh, T.Q., Tang, J., La, Q.D., Quek, T.Q.: Adaptive computation scaling and task offloading in mobile edge computing. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2017)
Kao, Y.-H., Krishnamachari, B., Ra, M.-R., Bai, F.: Hermes: latency optimal task assignment for resource-constrained mobile computing. IEEE Trans. Mob. Comput. 16(11), 3056–3069 (2017)
Zhang, W., Wen, Y., Wu, D.O.: Collaborative task execution in mobile cloud computing under a stochastic wireless channel. IEEE Trans. Wirel. Commun. 14(1), 81–93 (2014)
Sardellitti, S., Scutari, G., Barbarossa, S.: Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Trans. Sig. Inf. Process. Netw. 1(2), 89–103 (2015)
Deng, M., Tian, H., Fan, B.: Fine-granularity based application offloading policy in cloud-enhanced small cell networks. In: 2016 IEEE International Conference on Communications Workshops (ICC), pp. 638–643. IEEE (2016)
Kwak, J., Kim, Y., Lee, J., Chong, S.: DREAM: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33(12), 2510–2523 (2015)
Jiang, Z., Mao, S.: Energy delay tradeoff in cloud offloading for multi-core mobile devices. IEEE Access 3, 2306–2316 (2015)
You, C., Zeng, Y., Zhang, R., Huang, K.: Asynchronous mobile-edge computation offloading: energy-efficient resource management. IEEE Trans. Wirel. Commun. 17(11), 7590–7605 (2018)
Acknowledgement
This research was partially supported by the National Key Research and Development Program of China (No. 2017YFB1400601), Key Research and Development Project of Natural Science Foundation of China (NO. 61772461, No. 61802343, No. 62072402) and Zhejiang Provincial Natural Science Foundation of China (No. LQ21F020007, No. LQ20F020015, No. LR18F020003).
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Xiang, Z. et al. (2021). Activate Cost-Effective Mobile Crowd Sensing with Multi-access Edge Computing. In: Gao, H., Fan, P., Wun, J., Xiaoping, X., Yu, J., Wang, Y. (eds) Communications and Networking. ChinaCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-67720-6_6
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