Aiming at the problem of unreliable data quality caused by sensing node uncertainty in mobile crowd sensing, a cross-domain collaborative filtering trusted sensing node recommendation method based on SDN is proposed. Firstly, SDN is introduced to decouple the service surface and the control surface, and it is convenient to manage sensing nodes and reduce the burden of server for task allocation. Then, through cross-domain collaborative filtering method, find sensing nodes which show similar credibility in the historical task allocation and complete some similar tasks with target sensing nodes. Finally, the recommendation value of the sensing node in the target task is obtained though the current ability of sensing nodes, and their distance from target tasks, and similar sensing nodes’ credibility in the target task and time decay, at last, the trusted sensing node is selected. Simulation experiments verify that when selecting a trusted sensing node, the method proposed in this paper has better recommendation accuracy, and the time is shorter. In addition, it also proves that when the sensing data of the same data quality is obtained, the incentive cost is lower.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Ajoudanian S, Abadeh MN (2019) Recommending human resources to project leaders using a collaborative filtering-based recommender system: case study of GitHub [J]. IET Softw 13(5):379–385
Cheng J, Gong J, Yang W et al (2018) Research on network intrusion tracking and response system based on SDN technology [J]. J Commun 39(S1):250–256
Dong W, Chen GL, Cao CH et al (2017) Towards a software-defined architecture for wireless sensor networks [J]. Chinese J Comput 8:57–75
Goncslves J, Feldman M, Hu S, et al. (2017) Task Routing and Assignment in Crowdsourcing Based on Cognitive Abilities [C]. World Wide Web, 1023–1031
Guo B, Yu Z W, Zhang D Q, et al. (2014) From Participatory Sensing to Mobile Crowd Sensing[C].Pervasive Computing &Communications Workshops, Budapest, 593–598
Guo B, Wang Z, Yu ZW et al (2015) Mobile Crowd Sensing and Computing:The Review of an Emerging Human-Powered Sensing Paradigm[J]. ACM Comput Surv 48(1):7
Huang K L, Kanhere S S, Hu W (2010) Are You Contributing Trustworthy Data?The Case for A Reputation System in Participatory Sensing[C]. Modeling, analysis, and simulation of wireless and mobile systems, Bodrum 14–22
Huang MG, Huang YC, Yu B et al (2018) Software-defined wireless sensor networks: a research survey[J]. J Softw 29(9):2733–2752
Kantarci B, Carr KG, Pearsall CD (2016) SONATA: social network assisted trustworthiness assurance in smart city crowdsensing. Intl J Distrib Syst Technol 7(1):59–78
Li K, Long Y, Lan H et al (2018) A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering [J]. Sensors 18(5):1556
Lian DF, Ge Y, Zhang FZ et al (2018) Scalable content-aware collaborative filtering for location recommendation [J]. IEEE Trans Knowl Data Eng 30(6):1122–1135
Mckeown N. (2009) Software-defined networking [C]. INFOCOM Key Note, IEEE
Pouryazdan M, Kantarci B, Soyata T et al (2017) Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing [J]. IEEE Access 5:1382–1397
Qiu W W, Zheng Z B, Wang X Y, et al. (2013) Reputation-Aware QoS Value Prediction of Web Services [C]. Services Computing, Santa Clara, 41–48
Su K, Xiao B, Liu B et al (2016) TAP:A Personalized Trust-Aware QoS Prediction Approach for Web Service Recommendation [J]. Knowl-Based Syst 115:55–65
Tajiki M M, Shojafar M, Akvari B, et al. (2019) Joint Failure Recovery, Fault Prevention, and Energy-efficient Resource Management for Real-time SFC in Fog-supported SDN [J]. Comput Netw, 1–24
Wang W, Gao H, Harold LC et al (2016) Credible and Energy-Aware Participant Selection with Limited Task Budget for Mobile Crowd Sensing [J]. Ad Hoc Netw S1(43):56–70
Xu MH, Liu SH (2019) Semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation in event-based social networks [J]. IEEE Access 7:17493–17502
Xu J, Zheng Z, Lyu MR (2016) Web Service Personalized Quality of Service Prediction via Reputation-Based Matrix Factorization [J]. IEEE Transactions on Reliability 65:28–37
Zhang XL, Yang Z, Sun W et al (2017) Incentives for Mobile Crowd Sensing:A Survey[J]. IEEE Commun Surv Tutor 18(1):54–67
Zhang Y, Meng K, Kong W et al (2019) Collaborative filtering-based electricity plan recommender system [J]. IEEE Trans Ind Inf 15(3):1393–1404
Zhou T, Cai Z, Wu K et al (2017) FIDC:A Framework for Improving Data Credibility in Mobile Crowdsensing [J]. Computer Networks 120:157–169
This present research work was supported by the National Science and Technology Major Project (2016ZX03001023-005), the National Natural Science Foundation of China (61403109), the China Postdoctoral Science Foundation (2019 M651263), the Heilongjiang Natural Science Foundation (LH2020F034) and the Scientific Research Fund of Heilongjiang Provincial Education Department (12541169).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Zhao, Z., Wang, Y. & Wang, H. SDN-based cross-domain cooperative method for trusted nodes recommendation in Mobile crowd sensing. Peer-to-Peer Netw. Appl. (2021). https://doi.org/10.1007/s12083-021-01217-z
- Mobile crowd sensing
- Trusted nodes recommendation
- Cross-domain collaborative filtering