G.Chen et al. Computer Networks 190 (2021)107952 converges faster and remains more stable.The MAE is always less [12]J.Laurikkala,M.Juhola,E.Kentala,N.Lavrac,S.Miksch,B.Kavsek,Informal than 0.05 when the proportion of malicious nodes is from 10%to identification of outliers in medical data,in:Fifth International Workshop on 70%.Experimental results further verify that our adaptive trust model Intelligent Data Analysis in Medicine and Pharmacology,Vol.1,Citeseer,2000, enables fast and accurate trust evaluation and resists trust related Pp.20-24. [13]F.T.Liu,K.M.Ting,Z.-H.Zhou,Isolation forest,in:2008 Eighth IEEE attacks in the dynamically hostile environment. International Conference on Data Mining,IEEE,2008,pp.413-422. However,there are still some limitations in our work.At present, [14]M.M.Breunig,H.-P.Kriegel,R.T.Ng,J.Sander,LOF:identifying density-based TTPs are fixed in our system architecture.So the trust model based on local outliers,in:Proceedings of the 2000 ACM SIGMOD international conference our architecture needs to select TTPs in advance.In the future work,we on Management of data,2000,pp.93-104. will study how to design a TTP designation algorithm to automatically [15]M.Ester,H.-P.Kriegel,J.Sander,X.Xu,et al.,A density-based algorithm for determine TTPs based on factors such as the trust value of IoT objects, discovering clusters in large spatial databases with noise,in:Kdd,Vol.96,1996. the remaining energy and the computing power.Furthermore,we plan pp.226-231. to further improve the resistance to trust related attacks of our trust [16]M.A.Wong,J.Hartigan,Algorithm as 136:A k-means clustering algorithm,J. model so that it will still accurately evaluate the IoT objects when the R.Stat.Soc.Ser.C Appl.Stat.28 (1)(1979)100-108. proportion of malicious nodes exceeds 70%.Then,we will design and [17]J.Guo,R.Chen,J.J.Tsai,A survey of trust computation models for service management in internet of things systems,Comput.Commun.97(2017)1-14. implement a trust management system based on our proposed model. [18]D.Chen,G.Chang,D.Sun,J.Li,J.Jia,X.Wang,TRM-IoT:A trust management Therefore,the feedback information used in trust evaluation will be model based on fuzzy reputation for internet of things,Comput.Sci.Inf.Syst.8 generated based on the actual service provided by nodes.We will also (4)(2011)1207-1228. validate our trust model in a real loT environment. [19]M.Nitti,R.Girau,L Atzori,Trustworthiness management in the social internet of things,IEEE Trans.Knowl.Data Eng.26 (5)(2013)1253-1266. CRediT authorship contribution statement [20]Y.B.Saied,A.Olivereau,D.Zeghlache,M.Laurent,Trust management system design for the Intemnet of Things:A context-aware and multi-service approach, Guozhu Chen:Conceptualization,Methodology,Software,Writing Comput.Secur.39(2013)351-365. [21]H.Xia,F.Xiao,S.s.Zhang,C.-q.Hu,X.-z.Cheng.Trustworthiness inference original draft.Fanping Zeng:Writing-review editing,Resources, framework in the social Internet of Things:A context-aware approach,in:IEEE Supervision.Jian Zhang:Writing-review editing.Tingting Lu: Conference on Computer Communications (INFOCOM),IEEE,2019,pp.838-846. Writing-review editing.Jingfei Shen:Writing-review editing. [22]Z.Lin,L.Dong,Clarifying trust in social internet of things,IEEE Trans.Knowl. Wenjuan Shu:Writing-review editing. Data Eng.30(2)(2017刀234-248. [23]H.Xia,B.Li,S.Zhang,S.Wang.X.Cheng,A novel recommendation-based Declaration of competing interest trust inference model for MANETs,in:International Conference on Wireless Algorithms,Systems,and Applications,Springer,2018,pp.893-906. The authors declare that they have no known competing finan- [24]U.Jayasinghe,G.M.Lee,T.-W.Um,Q.Shi,Machine learning based trust computational model for IoT services,IEEE Trans.Sustain.Comput.4 (1)(2018) cial interests or personal relationships that could have appeared to 39-52. influence the work reported in this paper. [25]J.Caminha,A.Perkusich,M.Perkusich,A smart trust management method to detect on-off attacks in the internet of things,Secur.Commun.Netw.(2018). Acknowledgments [26]M.Bahutair,A.Bougeuttaya,A.G.Neiat,Adaptive trust:Usage-based trust in crowdsourced IoT services,in:2019 IEEE International Conference on Web This work is supported partly by the National Key R&D Program Services (ICWS),IEEE,2019,pp.172-179. of China 2018YFB2100300 and 2018YFB0803400,the National Key [27]C.Boudagdigue,A.Benslimane,A.Kobbane,M.Elmachkour,A distributed advanced analytical trust model for IoT,in:2018 IEEE International Conference Basic Research (973)Program of China (2014CB340701)and National on Communications (ICC),IEEE,2018,Pp.1-6. Natural Science Foundation of China (NSFC)under grant 61772487. [28]A.Josang,R.Ismail,The beta reputation system,in:Proceedings of the 15th Bled Electronic Commerce Conference,Vol.5,2002,pp.2502-2511. References [29]H.Simaremare,A.Syarif,A.Abouaissa,R.F.Sari,P.Lorenz,Performance comparison of modified AODV in reference point group mobility and ran- [1]L Atzori,A.lera,G.Morabito,The internet of things:A survey,Comput.Netw dom waypoint mobility models,in:2013 IEEE International Conference on 54(15)(2010)2787-2805. Communications (ICC),IEEE,2013,pp.3542-3546. [2]X.Li,R.Lu,X.Liang.X.Shen,J.Chen,X.Lin,Smart community:an internet of things application,IEEE Commun.Mag.49 (11)(2011)68-75. [3]A.Altaf,H.Abbas,F.Iqbal,A.Derhab,Trust models of internet of smart things: A survey,open issues,and future directions,J.Netw.Comput.Appl.137(2019) Guozhu Chen:received his B.S.degree in Software Engi- 93-111. neering from Anhui University.Currently a Master student [4]T.Wang,G.Zhang,M.ZA.Bhuiyan,A.Liu,W.Jia,M.Xie,A novel trust in the School of Computer Science and Technology at mechanism based on fog computing in sensor-cloud system,Future Gener University of Science and Technology of China.His research Comput.Syst.. interests mainly include Internet of Things and Trust Model (5]A.M.Shabut,K.P.Dahal,S.K.Bista,LU.Awan,Recommendation based trust model with an effective defence scheme for MANETs,IEEE Trans.Mob.Comput. 14(10)(2014)2101-2115 [6]R.Chen,J.Guo,F.Bao,Trust management for SOA-based IoT and its application to service composition,IEEE Trans.Serv.Comput.9(3)(2014)482-495. [7]M.D.Alshehri,F.K Hussain,O.K.Hussain,Clustering-driven intelligent trust management methodology for the internet of things (CITM-IoT),Mob.Netw. Fanping Zeng:is an Associate Professor of the School of Appl.23(3)(2018)419-431. Computer Science and Technology,University of Science [8]Z.Gao,W.Zhao,C.Xia,K.Xiao,Z.Mo,Q.Wang,Y.Yang,A credible and lightweight multidimensional trust evaluation mechanism for service-oriented loT and Technology of China.He graduated from Harbin In- stitute of Technology with a bachelor's degree and received edge computing environment,in:2019 IEEE Interational Congress on Internet Ph.D.degree from the University of Science and Technology of Things (ICIOT),IEEE,2019,pp.156-164. of China in 2009.His main research interests include [9]R.Chen,F.Bao,J.Guo,Trust-based service management for social internet of things systems,IEEE Trans.Dependable Secure Comput.13(6)(2015)684-696. software testing,network and system security.Recently,he [10]H.Hellaoui,A.Bouabdallah,M.Koudil,TAS-loT:Trust-based Adaptive Security mainly focuses on the Intemet of things,studies the col- laborative optimization of edge and end resources,security in the IoT,in:2016 IEEE 41st Conference on Local Computer Networks (LCN), EEE,2016,pp.599602. analysis,security testing and evaluation [11]F.E.Grubbs,Procedures for detecting outlying observations in samples, Technometrics 11 (1)(1969)1-21. 12Computer Networks 190 (2021) 107952 12 G. Chen et al. converges faster and remains more stable. The MAE is always less than 0.05 when the proportion of malicious nodes is from 10% to 70%. Experimental results further verify that our adaptive trust model enables fast and accurate trust evaluation and resists trust related attacks in the dynamically hostile environment. However, there are still some limitations in our work. At present, TTPs are fixed in our system architecture. So the trust model based on our architecture needs to select TTPs in advance. In the future work, we will study how to design a TTP designation algorithm to automatically determine TTPs based on factors such as the trust value of IoT objects, the remaining energy and the computing power. Furthermore, we plan to further improve the resistance to trust related attacks of our trust model so that it will still accurately evaluate the IoT objects when the proportion of malicious nodes exceeds 70%. Then, we will design and implement a trust management system based on our proposed model. Therefore, the feedback information used in trust evaluation will be generated based on the actual service provided by nodes. We will also validate our trust model in a real IoT environment. CRediT authorship contribution statement Guozhu Chen: Conceptualization, Methodology, Software, Writing - original draft. Fanping Zeng: Writing - review & editing, Resources, Supervision. Jian Zhang: Writing - review & editing. Tingting Lu: Writing - review & editing. Jingfei Shen: Writing - review & editing. Wenjuan Shu: Writing - review & editing. Declaration of competing interest The authors declare that they have no known competing finan￾cial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work is supported partly by the National Key R&D Program of China 2018YFB2100300 and 2018YFB0803400, the National Key Basic Research (973) Program of China (2014CB340701) and National Natural Science Foundation of China (NSFC) under grant 61772487. References [1] L. Atzori, A. Iera, G. Morabito, The internet of things: A survey, Comput. Netw. 54 (15) (2010) 2787–2805. [2] X. Li, R. Lu, X. Liang, X. Shen, J. Chen, X. Lin, Smart community: an internet of things application, IEEE Commun. Mag. 49 (11) (2011) 68–75. [3] A. Altaf, H. Abbas, F. Iqbal, A. Derhab, Trust models of internet of smart things: A survey, open issues, and future directions, J. Netw. Comput. Appl. 137 (2019) 93–111. [4] T. Wang, G. Zhang, M.Z.A. Bhuiyan, A. Liu, W. Jia, M. Xie, A novel trust mechanism based on fog computing in sensor–cloud system, Future Gener. Comput. Syst.. [5] A.M. Shabut, K.P. Dahal, S.K. Bista, I.U. Awan, Recommendation based trust model with an effective defence scheme for MANETs, IEEE Trans. Mob. Comput. 14 (10) (2014) 2101–2115. [6] R. Chen, J. Guo, F. Bao, Trust management for SOA-based IoT and its application to service composition, IEEE Trans. Serv. Comput. 9 (3) (2014) 482–495. [7] M.D. Alshehri, F.K. Hussain, O.K. Hussain, Clustering-driven intelligent trust management methodology for the internet of things (CITM-IoT), Mob. Netw. Appl. 23 (3) (2018) 419–431. [8] Z. Gao, W. Zhao, C. Xia, K. Xiao, Z. Mo, Q. Wang, Y. Yang, A credible and lightweight multidimensional trust evaluation mechanism for service-oriented IoT edge computing environment, in: 2019 IEEE International Congress on Internet of Things (ICIOT), IEEE, 2019, pp. 156–164. [9] R. Chen, F. Bao, J. Guo, Trust-based service management for social internet of things systems, IEEE Trans. Dependable Secure Comput. 13 (6) (2015) 684–696. [10] H. Hellaoui, A. Bouabdallah, M. 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Xu, et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in: Kdd, Vol. 96, 1996, pp. 226–231. [16] M.A. Wong, J. Hartigan, Algorithm as 136: A k-means clustering algorithm, J. R. Stat. Soc. Ser. C Appl. Stat. 28 (1) (1979) 100–108. [17] J. Guo, R. Chen, J.J. Tsai, A survey of trust computation models for service management in internet of things systems, Comput. Commun. 97 (2017) 1–14. [18] D. Chen, G. Chang, D. Sun, J. Li, J. Jia, X. Wang, TRM-IoT: A trust management model based on fuzzy reputation for internet of things, Comput. Sci. Inf. Syst. 8 (4) (2011) 1207–1228. [19] M. Nitti, R. Girau, L. Atzori, Trustworthiness management in the social internet of things, IEEE Trans. Knowl. Data Eng. 26 (5) (2013) 1253–1266. [20] Y.B. Saied, A. Olivereau, D. Zeghlache, M. Laurent, Trust management system design for the Internet of Things: A context-aware and multi-service approach, Comput. Secur. 39 (2013) 351–365. [21] H. Xia, F. Xiao, S.-s. Zhang, C.-q. Hu, X.-z. Cheng, Trustworthiness inference framework in the social Internet of Things: A context-aware approach, in: IEEE Conference on Computer Communications (INFOCOM), IEEE, 2019, pp. 838–846. [22] Z. Lin, L. Dong, Clarifying trust in social internet of things, IEEE Trans. Knowl. Data Eng. 30 (2) (2017) 234–248. [23] H. Xia, B. Li, S. Zhang, S. Wang, X. Cheng, A novel recommendation-based trust inference model for MANETs, in: International Conference on Wireless Algorithms, Systems, and Applications, Springer, 2018, pp. 893–906. [24] U. Jayasinghe, G.M. Lee, T.-W. Um, Q. Shi, Machine learning based trust computational model for IoT services, IEEE Trans. Sustain. Comput. 4 (1) (2018) 39–52. [25] J. Caminha, A. Perkusich, M. Perkusich, A smart trust management method to detect on-off attacks in the internet of things, Secur. Commun. Netw. (2018). [26] M. Bahutair, A. Bougeuttaya, A.G. Neiat, Adaptive trust: Usage-based trust in crowdsourced IoT services, in: 2019 IEEE International Conference on Web Services (ICWS), IEEE, 2019, pp. 172–179. [27] C. Boudagdigue, A. Benslimane, A. Kobbane, M. Elmachkour, A distributed advanced analytical trust model for IoT, in: 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1–6. [28] A. Josang, R. Ismail, The beta reputation system, in: Proceedings of the 15th Bled Electronic Commerce Conference, Vol. 5, 2002, pp. 2502–2511. [29] H. Simaremare, A. Syarif, A. Abouaissa, R.F. Sari, P. Lorenz, Performance comparison of modified AODV in reference point group mobility and ran￾dom waypoint mobility models, in: 2013 IEEE International Conference on Communications (ICC), IEEE, 2013, pp. 3542–3546. Guozhu Chen: received his B.S. degree in Software Engi￾neering from Anhui University. Currently a Master student in the School of Computer Science and Technology at University of Science and Technology of China. His research interests mainly include Internet of Things and Trust Model. Fanping Zeng: is an Associate Professor of the School of Computer Science and Technology, University of Science and Technology of China. He graduated from Harbin In￾stitute of Technology with a bachelor’s degree and received Ph.D. degree from the University of Science and Technology of China in 2009. His main research interests include software testing, network and system security. Recently, he mainly focuses on the Internet of things, studies the col￾laborative optimization of edge and end resources, security analysis, security testing and evaluation