Dynamic and comprehensive trust model for IoT and its integration into RPL

Ensuring security in IoT routing protocols is more challenging due to the fact that devices are mobile, resource constrained, and heterogeneous. The routing protocol for low-power and lossy networks (RPL) as the de facto routing protocol for IoT provides a little protection against routing attacks. On the other hand, the standard RPL because of the use of a single metric in routing has limitations that ultimately results in loss of network performance. To overcome the limitations of the use of a single metric and to prevent the consequences of routing attacks, we used the concept of trust and propose dynamic and comprehensive trust model for IoT (DCTM-IoT) and integrate it into RPL (DCTM-RPL). We provide a comprehensive hierarchical model for trusting of things in IoT, which has a multi-dimensional vision of trust. We put the combination of metrics and necessary activities to deal with attacks under the umbrella of trust level calculation. The performance of DCTM-RPL is compared with the standard RPL protocol in mobile environment and under routing major attacks. DCTM-RPL demonstrates its superior performance over the standard RPL protocol in the detection and isolation attacks. The DCTM-RPL, in addition to resistance mitigating routing attacks, improves network performance.

[1]  L. Mui,et al.  A computational model of trust and reputation , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[2]  Lynn Choi,et al.  DAG-based multipath routing for mobile sensor networks , 2011, ICTC 2011.

[3]  Tanir Ozcelebi,et al.  Trust-Based Neighbor Unreachability Detection for RPL , 2016, 2016 25th International Conference on Computer Communication and Networks (ICCCN).

[4]  Iwao Sasase,et al.  Secure parent node selection scheme in route construction to exclude attacking nodes from RPL network , 2015 .

[5]  Naixue Xiong,et al.  A novel trust management scheme based on Dempster–Shafer evidence theory for malicious nodes detection in wireless sensor networks , 2017, The Journal of Supercomputing.

[6]  A. Sojobi Evaluation of groundwater quality in a rural community in North Central of Nigeria , 2016, Environmental Monitoring and Assessment.

[7]  Anis Koubaa,et al.  A comparative simulation study of link quality estimators in wireless sensor networks , 2009, 2009 IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems.

[8]  Siarhei Kuryla,et al.  RPL: IPv6 Routing Protocol for Low power and Lossy Networks , 2010 .

[9]  Elmar Gerhards-Padilla,et al.  BonnMotion: a mobility scenario generation and analysis tool , 2010, SimuTools.

[10]  Li-Der Chou,et al.  A survey of black hole attacks in wireless mobile ad hoc networks , 2011, Human-centric Computing and Information Sciences.

[11]  Hangbae Chang,et al.  Security experts’ capability design for future internet of things platform , 2015, The Journal of Supercomputing.

[12]  Nabil Benamar,et al.  OF-EC: A novel energy consumption aware objective function for RPL based on fuzzy logic , 2018, J. Netw. Comput. Appl..

[13]  Ahmed Khattab,et al.  Fault-tolerant RPL through context awareness , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[14]  Djamel Tandjaoui,et al.  Trust-based RPL for the Internet of Things , 2015, 2015 IEEE Symposium on Computers and Communication (ISCC).

[16]  Gabi Dreo Rodosek,et al.  Towards a trust computing architecture for RPL in Cyber Physical Systems , 2013, Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).

[17]  Anis Koubaa,et al.  RPL in a nutshell: A survey , 2012, Comput. Networks.

[18]  Theodore B. Zahariadis,et al.  Design of primary and composite routing metrics for RPL-compliant Wireless Sensor Networks , 2012, 2012 International Conference on Telecommunications and Multimedia (TEMU).

[19]  Mohamed Abid,et al.  OF-FL: QoS-aware fuzzy logic objective function for the RPL routing protocol , 2014, 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[20]  Dominique Barthel,et al.  Routing Metrics Used for Path Calculation in Low-Power and Lossy Networks , 2012, RFC.

[21]  Mohsen Nickray,et al.  Fog-based energy-efficient routing protocol for wireless sensor networks , 2018, The Journal of Supercomputing.

[22]  Mohamed Abid,et al.  Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL , 2015, Ad Hoc Networks.

[23]  Antonio F. Gómez-Skarmeta,et al.  TACIoT: multidimensional trust-aware access control system for the Internet of Things , 2016, Soft Comput..

[24]  JeongYeon Kim,et al.  Enforcing high-level security policies for Internet of Things , 2017, The Journal of Supercomputing.

[25]  Andrew H. Kemp,et al.  A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications , 2018, IEEE Sensors Journal.

[26]  Sayan Kumar Ray,et al.  SecTrust-RPL: A secure trust-aware RPL routing protocol for Internet of Things , 2019, Future Gener. Comput. Syst..

[27]  Amol P. Bhondekar,et al.  Heterogeneity consideration in wireless sensor networks routing algorithms: a review , 2018, The Journal of Supercomputing.

[28]  Pascal Thubert,et al.  Objective Function Zero for the Routing Protocol for Low-Power and Lossy Networks (RPL) , 2012, RFC.

[29]  Kuei-Fang Hsiao,et al.  Integrating body language movements in augmented reality learning environment , 2011, Human-centric Computing and Information Sciences.

[30]  Remi Badonnel,et al.  A Taxonomy of Attacks in RPL-based Internet of Things , 2016, Int. J. Netw. Secur..

[31]  Imed Romdhani,et al.  New trust metric for the RPL routing protocol , 2017, 2017 8th International Conference on Information and Communication Systems (ICICS).