Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (11): 938-945.doi: 10.23940/ijpe.21.11.p4.938945
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Bandarupalli Rakesha and H. Parveen Sultanab
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*E-mail address: hparveensultana@vit.ac.in
Bandarupalli Rakesh and H. Parveen Sultana. A Review on Enhanced Routing Solutions in RPL Protocol [J]. Int J Performability Eng, 2021, 17(11): 938-945.
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1. Atzori L., Iera A., andMorabito G,The Internet of Things: A Survey. 2. Council N.Disruptive Civil Technologies: Six Technologies with Potential Impacts on Us Interests Out to 2025; Conference Report CR; SRI Consulting Business Intelligence: Menlo Park, CA, USA, 2008. 3. Yuan L., Cheng H., andWangshun C,Building Energy Consumption Data Index Method in Cloud Computing Environment. 4. Winter T., Thubert P., Brandt A., Hui J.W., Kelsey R., Levis P., Pister K., Struik R., Vasseur J.P., andAlexander R.K,RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks. 5. Iova O., Theoleyre F., andNoel T.Using Multiparent Routing in RPL to increase the Stability and the Lifetime of the Network. 6. Levis P., Clausen T., Hui J., Gnawali O., andKo J,The Trickle Algorithm (RFC 6206). In 7. Mohamed, B. and Mohamed, F.QoS Routing RPL for Low Power and Lossy Networks. 8. Gara F., Saad L.B., Ayed R.B., andTourancheau B,RPL Protocol Adapted for Healthcare and Medical Applications. In 9. Vasseur J.P., Kim M., Pister K., Dejean N., andBarthel D,Routing Metrics used for Path Calculation in Low-power and Lossy networks. In 10. Goyal M., Baccelli E., Philipp M., Brandt A., andMartocci J.Reactive Discovery of Point-to-point Routes in Low Power and Lossy Networks. 11. Barriquello C.H., Denardin G.W., andCampos A.A Geographic Routing Approach for IPv6 in Large-Scale Low-power and Lossy Networks. 12. Zhao, M., Ho, I.W.H., and Chong, P.H.J. An Energy-efficient Region-based RPL Routing Protocol for Low-power and Lossy Networks. 13. Anamalamudi S., Zhang M., Sangi A.R., Perkins C.E., andAnand S., 14. Kelsey, R.,Hui, J.Multicast Protocol for Low-power and Lossy Networks (MPL). 15. Oikonomou G., Phillips I., andTryfonas T.IPv6 Multicast Forwarding in RPL-based Wireless Sensor Networks. 16. Abdel Fadeel, K.Q., and El Sayed, K, ESMRF: Enhanced Stateless Multicast RPL Forwarding for IPv6-based Low-power and Lossy Networks. In 17. Lorente G.G., Lemmens B., Carlier M., Braeken A., andSteenhaut K,BMRF: Bidirectional Multicast RPL Forwarding. 18. Gaddour O., Koubäa A., Rangarajan R., Cheikhrouhou O., Tovar E., andAbid M,Co-RPL: RPL Routing for Mobile Low Power Wireless Sensor Networks using Corona Mechanism. In 19. Gaddour O., Koubâa A., andAbid M.Quality-of-service Aware Routing for Static and Mobile IPv6-based Low-power and Lossy Sensor Networks using RPL. 20. Fotouhi H., Moreira D., andAlves, M. mRPL: Boosting Mobility in the Internet of Things. 21. Bouaziz M., Rachedi A., andBelghith A.EKF-MRPL: Advanced Mobility Support Routing Protocol for Internet of Mobile Things: Movement Prediction Approach. 22. Bouaziz M., Rachedi A., Belghith A., Berbineau M., andAl-Ahmadi, S, EMA-RPL: Energy and Mobility aware Routing for the Internet of Mobile Things. 23. Ko, J. Jeong, J. Park, J. Jun, J.A.; Gnawali, O.Paek, J,x DualMOP-RPL: Supporting Multiple Modes of Downward Routing in a Single RPL Network. ACM Trans. Sens. Netw. (TOSN), 2015. 24. Kim H.S., Cho H., Kim H., andBahk S,DT-RPL: Diverse Bidirectional Traffic Delivery through RPL Routing Protocol in Low Power and Lossy Networks. 25. Taghizadeh S., Bobarshad H., andElbiaze H,CLRPL: Context-aware and Load Balancing RPL for IoT Networks under Heavy and Highly Dynamic Load. 26. Ancillotti E., Bruno R., andConti M.Reliable Data Delivery with the IETF Routing Protocol for Low-power and Lossy Networks. 27. Mohamed, B. and Mohamed, F, QoS Routing RPL for Low Power and Lossy Networks. 28. Iova O., Theoleyre F., andNoel T.Using Multiparent Routing in RPL to increase the Stability and the Lifetime of the Network. 29. Abreu, C., Ricardo, M,Mendes, P.M, Energy-aware Routing for Biomedical Wireless Sensor Networks. 30. Capone S., Brama R., Accettura N., Striccoli D., andBoggia G,An Energy Efficient and Reliable Composite Metric for RPL Organized Networks. In 31. Gaddour O., Koubâa A., Baccour N., andAbid M,OF-FL: QoS-aware Fuzzy Logic Objective Function for the RPL Routing Protocol. In 32. Gaddour O., Koubâa A., andAbid M.Quality-of-service Aware Routing for Static and Mobile IPv6-based Low-power and Lossy Sensor Networks using RPL. 33. Kamgueu P.O., Nataf E., andDjotio T.N.On Design and Deployment of Fuzzy-based Metric for Routing in Low-power and Lossy Networks. In 34. Araujo H.D.S., Rodrigues, J.J., Rabelo, R.D.A., Sousa, N.D.C., and Sobral, J.V, A Proposal for IoT Dynamic Routes Selection based on Contextual Information. 35. Chen Y., Chanet J.P., Hou K.M., Shi H., andDe Sousa, G, A Scalable Context-aware Objective Function (SCAOF) of Routing Protocol for Agricultural Low-power and Lossy Networks (RPAL). 36. Gozuacik, N. and Oktug, S, Parent-aware Routing for Iot Networks. In 37. Airehrour D., Gutierrez J.A., andRay S.K,SecTrust-RPL: A Secure Trust-aware RPL routing Protocol for Internet of Things. 38. Mehta, R. and Parmar, M.M, Trust based Mechanism for Securing Iot Routing Protocol rpl Against Wormhole &Grayhole Attacks. In 39. Shafique U., Khan A., Rehman A., Bashir F., andAlam M,Detection of Rank Attack in Routing Protocol for Low Power and Lossy Networks. |
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