Skip to main content
SpringerLink
Log in

An optimal uplink traffic offloading algorithm via opportunistic communications based on machine learning

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Opportunistic communications as an efficient traffic offloading method can be used to offload uplink traffic of cellular networks to Wi-Fi networks. However, because of its contact pattern (contact frequency and contact duration) the offloading method could not ensure the data to be successfully offloaded to Wi-Fi Access Points (APs) within a time constraint. In this paper, we focus on maximizing the probability of offloading data to Wi-Fi APs by fragmenting the data and assigning the fragments to different direct or indirect paths generated by opportunistic contacts. Firstly, we propose two methods based on mobility prediction, which is realized by machine learning, to separately calculate the probability of offloading data to Wi-Fi APs by the direct offloading path considering multiple opportunistic contacts and contact duration, and the probability of indirectly offloading data to Wi-Fi APs by the indirect offloading path. Then, based on the probability calculation methods the offloading probability maximization is formulated as a non-linear integer programming problem, and we propose a distributed heuristic algorithm to solve it considering complexity of the probability calculation and limited computation capacities of devices. Simulation results prove the data offloading probability of our proposed algorithm outperforms other algorithms under different simulation environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Index Cisco Visual Networking (2015) Cisco visual networking index: Forecast and methodology 2015-2020. White paper, CISCO

  2. Xu D, Li Y, Chen X et al (2018) A survey of opportunistic offloading. IEEE Communications Surveys & Tutorials 20:2198–2236

    Article  Google Scholar 

  3. Lu Z, Sun X, La Porta T (2016) Cooperative data offloading in opportunistic mobile networks. INFOCOM, IEEE, pp. 1–9

  4. Gao G, Xiao M, Wu J et al (2016) Deadline-sensitive mobile data offloading via opportunistic communications. SECON, IEEE, pp.1–9

  5. Lu Z, Sun X, Wen Y et al (2014) Skeleton construction in mobile social networks: Algorithms and applications, SECON, IEEE, 477–485

  6. Lu Z, Sun X, Wen Y et al (2015) Algorithms and applications for community detection in weighted networks. IEEE Transactions on Parallel and Distributed Systems 26:2916–2926

    Article  Google Scholar 

  7. Pietilnen AK, Diot C (2012) Dissemination in opportunistic social networks: the role of temporal communities. In: Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing, ACM, 165–174

  8. Balasubramanian A, Levine B, Venkataramani A (2007) DTN routing as a resource allocation problem. ACM SIGCOMM 37:373–384

    Article  Google Scholar 

  9. Wang N, Wu J (2016) Opportunistic WiFi offloading in a vehicular environment: Waiting or downloading now?. INFOCOM, IEEE, pp. 1–9

  10. Li H, Yang Y, Dai Y et al (2017) Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data. IEEE Transactions on Cloud Computing

  11. Jiang W, Li H, Xu G et al (2019) PTAS: Privacy-preserving thin-client authentication scheme in blockchain-based PKI. Future Generation Computer Systems 96:185–195

    Article  Google Scholar 

  12. Zhu X, Li Y, Jin D et al (2017) Contact-aware optimal resource allocation for mobile data offloading in opportunistic vehicular networks. IEEE Transactions on Vehicular Technology 66:7384–7399

    Article  Google Scholar 

  13. Tang Y, Cheng N, Wu W (2019) Delay-Minimization Routing for Heterogeneous VANETs With Machine Learning Based Mobility Prediction. IEEE Transactions on Vehicular Technology 68:3967–3979

    Article  Google Scholar 

  14. Xu G, Li H, Ren H et al (2019) Data security issues in deep learning: Attacks, countermeasures and opportunities. IEEE Communications Magazine 116-122:57

    Google Scholar 

  15. Meng H, Li H, Luo X et al (2019) Efficient and Privacy-enhanced Federated Learning for Industrial Artificial Intelligence. IEEE Transactions on Industrial Informatics

  16. Xi O, Chaoyun Z, Zhou P et al (2016) Deepspace: An online deep learning framework for mobile big data to understand human mobility patterns, arXiv:1610.07009

  17. Al-Molegi A, Jabreel M, Ghaleb B (2016) STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location, 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1 –7

  18. Song X, Kanasugi H, Shibasaki R (2016) DeepTransport: Prediction and simulation of human mobility and transportation mode at a citywide level. IJCAI 16:2618–2624

    Google Scholar 

  19. Ghouti L et al (2014) Mobility Prediction Using Fully-Complex Extreme Learning Machines. ESANN, Citeseer

  20. Agarwal A, Dubey S, Khan MA et al (2016) Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access. In: 2016 International Conference on Signal Processing and Communications (SPCOM), pp. 1–5

  21. Xu G, Li H, Dai Y et al (2019) Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data. IEEE Transactions on Information Forensics and Security 14:870–885

    Article  Google Scholar 

  22. Li H, Liu D, Dai Y et al (2018) Personalized Search over Encrypted Data with Efficient and Secure Updates in Mobile Clouds. IEEE Transactions on Emerging Topics in Computing 6:97–109

    Article  Google Scholar 

  23. Zhou H, Wang H, Zhu C et al, Freshness-aware initial seed selection for traffic offloading through opportunistic mobile networks WCNC pp. 1–6 (2018)

  24. Xu G, Li H, Liu S et al (2020) VerifyNet: Secure and Verifiable Federated Learning. EEE Transactions on Information Forensics and Security 15:911–926

    Article  Google Scholar 

  25. Barua B, Khan Z, Han Z et al (2016) Incentivizing selected devices to perform cooperative content delivery: A carrier aggregation-based approach. IEEE TWC 15:5030–5045

    Google Scholar 

  26. Rebecchi F, de Amorim MD, Conan V (2016) Should I seed or should I not: On the remuneration of seeders in D2D offloading, WoWMoM, IEEE, pp. 1–9

  27. Xu G, Li H, Liu S, et al. (2019) Efficient and privacy-preserving truth discovery in mobile crowd sensing systems. IEEE Transactions on Vehicular Technology 68:3854–3865

    Article  Google Scholar 

  28. Chang Z, Gong J, Zhou Z et al (2015) Resource allocation and data offloading for energy efficiency in wireless power transfer enabled collaborative mobile clouds, INFOCOM WKSHPS, IEEE, 336–341

  29. Wang W, Wu X, Xie L, Lu S (2016) Joint storage assignment for D2D offloading systems. Computer Communications 83:45–55

    Article  Google Scholar 

  30. Ren H, Li H, Dai Y et al (2018) Querying in internet of things with privacy preserving: Challenges. Solutions and Opportunities 32:144–151

    Google Scholar 

  31. Lu X, Lio P, Hui P (2016) Distance-based opportunistic mobile data offloading. Sensors 878:16

    Article  Google Scholar 

  32. Wang N, Wu J (2016) Contact-aware optimal resource allocation for mobile data offloading in opportunistic vehicular networks, INFOCOM, IEEE, pp.1–9

  33. Wang N, Wu J (2018) Optimal data partitioning and forwarding in opportunistic mobile networks, WCNC, pp. 1–6

  34. Tang Y, Zhang Q, Lin W (2010) Artificial neural network based spectrum sensing method for cognitive radio. In: 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), pp. 1–4

  35. Komnios I, Tsapeli F, Gorinsky S (2015) Cost-effective multi-mode offloading with peer-assisted communications. Elsevier Ad Hoc Networks 25:370–382

    Article  Google Scholar 

  36. Wang L, Zhang D, Yan Z et al (2015) effSense: A novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploading. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45:1549–1563

    Article  Google Scholar 

  37. Phe-Neau T, De Amorim MD, Conan V (2013) The strength of vicinity annexation in opportunistic networking. INFOCOM, IEEE, pp. 3369–3374

  38. Lai Y, Gao X, Liao M et al (2016) Data gathering and offloading in delay tolerant mobile networks. Springer Wireless Networks 22:959–973

    Article  Google Scholar 

  39. Gao W, Cao G, La Porta T et al (2013) On exploiting transient social contact patterns for data forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing 12:151–165

    Article  Google Scholar 

  40. Zhang X, Cao G (2017) Transient community detection and its application to data forwarding in delay tolerant networks. IEEE/ACM TON 25:2829–2843

    Article  Google Scholar 

  41. Huan Zhou, Xin Chen, He S et al (2020) Freshness-aware seed selection for offloading cellular traffic through opportunistic mobile networks, IEEE Transactions on Wireless Communications

  42. Zhang Y, Li J, Li Y et al (2019) Cellular Traffic Offloading via Link Prediction in Opportunistic Networks. IEEE Access 7:39244–39252

    Article  Google Scholar 

  43. Dash SK, Dash S, Mishra J et al (2020) Opportunistic Mobile Data Offloading Using Machine Learning Approach. Wireless Personal Communications 110:125–139

    Article  Google Scholar 

  44. Li Y, Li J, Chen J et al (2019) Seed selection for data offloading based on social and interest graphs, Computers, Materials and Continua

  45. Belouanas SE, Thai KL et al (2019) Mobility-assisted offloading in centrally-coordinated cellular networks. Journal of Network and Computer Applications 128:1–10

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science, Beijing University of Technology, Beijing, China

    Qian Wang

  2. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing, China

    Zhipeng Gao

  3. State Grid Information and Telecommunications Branch, Beijing, China

    Zifan Li

  4. Department of Computer and Information Science Temple University, PA, 19122, USA

    Xiaojiang Du

  5. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Mohsen Guizani

Authors
  1. Qian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhipeng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zifan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojiang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohsen Guizani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Wang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Security and Privacy in Machine Learning Assisted P2P Networks

Guest Editors: Hongwei Li, Rongxing Lu and Mohamed Mahmoud

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Gao, Z., Li, Z. et al. An optimal uplink traffic offloading algorithm via opportunistic communications based on machine learning. Peer-to-Peer Netw. Appl. 13, 2285–2299 (2020). https://doi.org/10.1007/s12083-020-00904-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00904-7

Keywords

Search

Navigation