Abstract
The end-edge-cloud architecture brings an efficient solution to the big data processing problem caused by massive IoT devices. The characteristics of this architecture, such as massive devices, heterogeneous resources, and complex layers, bring new challenges to privacy protection issues. This paper considers the cooperation among terminal devices, edge nodes, and remote clouds to solve the task scheduling problem with privacy constraints by optimizing the offloading decision and resource allocation. A heuristic privacy-aware task offloading and resource allocation algorithm is proposed to maximize the number of successful tasks, which offloads low-privacy and non-privacy tasks to find sub-optimal offloading decisions by offloading sequence generation rules and decision adjustment. Task scheduling algorithms are presented by communication and computing resource allocation strategies for low-privacy and non-privacy tasks. ANOVA technique is used to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm is superior to others in terms of the number of devices, the amount of different task data, and the proportion of privacy tasks at different levels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cohen, J.: Embedded speech recognition applications in mobile phones: status, trends, and challenges. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5352–5355. IEEE, Las Vegas, NV, USA (2008)
Kumar, K., Lu, Y.H.: Cloud computing for mobile users: can offloading computation save energy? Computer 43(4), 51–56 (2010)
Soyata, T., et al.: Cloud-vision: real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. In: IEEE Symposium on Computers and Communications (ISCC), pp. 59–66. IEEE, Cappadocia, Turkey (2012)
Guo, F., et al.: An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing. IEEE/ACM Trans. Network. 26(6), 2651–2664 (2018)
Wang, C., et al.: Integration of networking, caching, and computing in wireless systems: a survey, some research issues, and challenges. IEEE Commun. Surv. Tutor. 20(1), 7–38 (2017)
Khan, W.Z., et al: Edge computing: a survey. Future Gener. Comput. Syst. 97(AUG.), 219–235 (2019)
Pace, P., et al: An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Trans. Indust. Inform. 15(1), 481–489 (2018)
Mora, H., et al.: Multilayer architecture model for mobile cloud computing paradigm. Complexity 2019, 1–13 (2019)
Zhou, J., et al.: Research advances on privacy preserving in edge computing. J. Comput. Res. Develop. 57(10), 2027–2051 (2020)
Sonmez, C., Ozgovde, A., Ersoy, C.: Edgecloudsim: an environment for performance evaluation of edge computing systems. Trans. Emerg. Telecommun. Technol. 29(11), e3493 (2018)
Mao, Y.Y., Zhang, J., Letaief, K.B.: Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE, San Francisco, CA, USA (2017)
Zhang, G.W., et al.: FEMTO: fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Internet Things J. 6(3), 4388–4400 (2018)
Lyu, X., Tian, H.: Adaptive receding horizon offloading strategy under dynamic environment. IEEE Commun. Lett. 20(5), 878–881 (2016)
Chen, M., Hao, Y.X.: Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J. Sel. Areas Commun. 36(3), 587–597 (2018)
Zhang, Q., et al.: Dynamic task offloading and resource allocation for mobile-edge computing in dense cloud RAN. IEEE Internet Things J. 7(4), 3282–3299 (2021)
Yan, J., et al.: Optimal task offloading and resource allocation in mobile-edge computing with inter-user task dependency. IEEE Trans. Wireless Commun. 19(1), 235–250 (2019)
Chen, X., et al.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Networking 24(5), 2795–2808 (2015)
Chen, S.G., et al.: Efficient privacy preserving data collection and computation offloading for fog-assisted IoT. IEEE Trans. Sustain. Comput. 5(4), 526–540 (2020)
Hwang, R.H., Hsueh, Y.L., Chung, H.W.: A novel time-obfuscated algorithm for trajectory privacy protection. IEEE Trans. Serv. Comput. 7(2), 126–139 (2013)
Razaq, M.M., et al.: Privacy-aware collaborative task offloading in fog computing. IEEE Trans. Comput. Soc. Syst. 9(1), 88–96 (2022)
Wang, T., et al.: A three-layer privacy preserving cloud storage scheme based on computational intelligence in fog computing. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 3–12 (2018)
Lyu, X., et al.: Multiuser joint task offloading and resource optimization in proximate clouds. IEEE Trans. Veh. Technol. 66(4), 3435–3447 (2016)
Peng, K., Huang, H., Wan, S., Leung, V.C.M.: End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment. Wireless Netw. 1–12 (2020). https://doi.org/10.1007/s11276-020-02385-1
Fizza, K., et al: PASHE: Privacy aware scheduling in a heterogeneous fog environment. In: IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 333–340. Barcelona, Spain (2018)
Acknowledgment
This work was supported by the Key-Area Research and Development Program of Guangdong Province (No.2021B0101200003), the National Natural Science Foundation of China (Nos. 61872077 and 61832004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhu, X., Sun, W., Li, X. (2023). Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-2385-4_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2384-7
Online ISBN: 978-981-99-2385-4
eBook Packages: Computer ScienceComputer Science (R0)