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Mobility prediction for random walk mobility model using ARIMA in mobile ad hoc networks

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Abstract

This study offers an autoregressive integrated moving average (ARIMA)-based mobility prediction model for forecasting a node's future mobility speed in mobile ad hoc networks (MANETs). The route discovery process among the nodes depends on the network connectivity of the nodes in the MANETs. The selection of mobility nodes to offer reliable routing is facilitated by the presented mobility prediction algorithm. To predict node mobility, the proposed ARIMA model employs autocorrelation to forecast node mobility in the future with the random walk (RW) model. For the predicted mobility model, the Akaike information criterion (AIC) and auto-correlation function (ACF) are assessed. The error performance metric mean square error (MSE) is very lower for the ARIMA-predicted mobility datasets as compared with the RNN-predicted mobility datasets. Different scenarios of speed values are examined for the proposed models. The error metrics for the walking scenario at the speed 1 and 1.5 m/s; the running scenario at the speed 4 and 10 m/s; and the cycling scenario at the speed 5 and 15 m/s are analyzed in the presented work. The proposed ARIMA model yields a higher futuristic mobility prediction precision rate of 3–24% as compared with existing works. Further, the proposed ARIMA has 88–99% of futuristic mobility prediction accuracy.

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References

  1. Zhang H, Dai L (2018) Mobility prediction: a survey on state-of-the-art schemes and future applications. IEEE Access 10(7):802–822

    Google Scholar 

  2. Hua EY, Haas ZJ (2009) An algorithm for prediction of link lifetime in MANET based on unscented Kalman filter. IEEE Commun Lett 16(13):782–794

    Article  Google Scholar 

  3. Cong Y, Zhou X, Kennedy RA (2015) Interference prediction in mobile ad hoc networks with a general mobility model. IEEE Trans Wirel Commun 14(8):4277–4290

    Article  Google Scholar 

  4. Prasannavenkatesan T, Raja R, Ganeshkumar P (2014) PDA-misbehaving node detection & prevention for MANETs. In: 2014 International Conference on Communication and Signal Processing. IEEE, pp 1163–1167

  5. Karaoglu B, Heinzelman W (2014) Cooperative load balancing and dynamic channel allocation for cluster-based mobile ad hoc networks. IEEE Trans Mobile Comput 14(5):951–963

  6. Sánchez-Casado L, Maciá-Fernández G, Garcia-Teodoro P, Magán-Carrión R. A model of data forwarding in MANETs for lightweight detection of malicious packet dropping. Comput Network 87:44–58

  7. Prasannavenkatesan T, Menakadevi T (2019) Futuristic speed prediction using auto-regression and neural networks for mobile ad hoc networks. Int J Commun Syst 32(9):e3951

    Article  Google Scholar 

  8. Pushpalatha M, Venkataraman R, Ramarao T (2009) Trust based energy aware reliable reactive protocol in mobile ad hoc networks. World Acad Sci Eng Technol Proc 56:381–384

    Google Scholar 

  9. Samundiswary P (2012) Trust based energy aware reactive routing protocol for wireless sensor networks. Int J Comput Appl 43(21):37–40

    Google Scholar 

  10. Gu C, Zhu Q (2014) An energy-aware routing protocol for mobile ad hoc networks based on route energy comprehensive index. Wirel Pers Commun 79(2):1557–1570

    Article  Google Scholar 

  11. Shivashankar R, Suresh HN, Golla V, Jayanthi G (2014) Designing energy routing protocol with power consumption optimization in MANET. IEEE Trans Emerg Top Comput 2(2):192–197

    Article  Google Scholar 

  12. Gopal DG, Saravanan R (2015) Fuzzy based energy aware routing protocol with trustworthiness for MANET. Int J Electron Inf Eng 3(2):67–80

    Google Scholar 

  13. Sengathir J, Manoharan R (2015) A futuristic trust coefficient-based semi-Markov prediction model for mitigating selfish nodes in MANETs. EURASIP J Wirel Commun Netw 158:1–13

    Google Scholar 

  14. Verma R, Kothari A (2015) Mining historical traffic data using back propagation neural network for accurate location estimation. Int J Comput Appl 110(9):17–21

    Google Scholar 

  15. Senthilkumar R, Manikandan P (2018) Enhancement of AODV protocol based on energy level in MANETs. Int J Pure Appl Math 118(7):425–430

    Google Scholar 

  16. Babatunde HA, Olalekan AS (2018) Link state prediction in mobile ad hoc network using Markov renewal process. Int J ICT Manag 7:26–43

    Google Scholar 

  17. Akhtar MA, Sahoo G (2015) Enhancing cooperation in MANET using the Backbone Group model (An application of Maximum Coverage Problem). Proc Comput Sci 46:1022–1031

  18. Wang X, Li J (2013) Improving the network lifetime of MANETs through cooperative MAC protocol design. IEEE Trans Parallel Distribut Syst 26(4):1010–1020

  19. Hernandez-Orallo E, Olmos MD, Cano JC, Calafate CT, Manzoni P (2014) CoCoWa: A collaborative contact-based watchdog for detecting selfish nodes. IEEE Trans Mobile Comput 14(6):1162–1175

  20. Chang JM, Tsou PC, Woungang I, Chao HC, Lai CF (2014) Defending against collaborative attacks by malicious nodes in MANETs: a cooperative bait detection approach. IEEE Syst J 9(1):65–75

  21. Yayeh Y, Lin HP, Berie G, Adege AB, Yen L, Jeng SS (2018) Mobility prediction in mobile ad-hoc network using deep learning. In: 2018 IEEE international conference on applied system invention (ICASI). IEEE, pp 1203–1206

  22. Duraipandian M (2019) Performance evaluation of routing algorithm for Manet based on the machine learning techniques. J Trends Comput Sci Smart Technol TCSST 1(01):25–38

    Article  Google Scholar 

  23. Bagirathan K, Palanisamy A (2022) Opportunistic routing protocol based EPO–BES in MANET for optimal path selection. Wirel Pers Commun 123(1):473–494

    Article  Google Scholar 

  24. Mahajan S, HariKrishnan R, Kotecha K (2022) Prediction of network traffic in wireless mesh networks using hybrid deep learning model. IEEE Access

  25. The Network Simulator-ns-2 (2019) https://www.isi.edu/nsnam/ns/

  26. System Identification Tool, Matlab, Mathworks (2019) Commonwealth of Massachusetts, United States of America. https://in.mathworks.com/products/sysid.html/

  27. Theerthagiri P, Thangavelu M (2018) FMPM: Futuristic mobility prediction model for mobile adhoc networks using auto-regressive integrated moving average. Acta Graph 29(2):7–17. https://doi.org/10.25027/agj2017.28.v29i2.136

    Article  Google Scholar 

  28. Prasannavenkatesan T (2019) COFEE: context-aware futuristic energy estimation model for sensor nodes using Markov model and auto-regression. Int J Commun Syst 52:e4248

    Google Scholar 

  29. Theerthagiri P (2020) FUCEM: futuristic cooperation evaluation model using Markov process for evaluating node reliability and link stability in mobile ad hoc network. Wireless networks. Springer

  30. Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mob Comput 2(5):483–502

    Article  Google Scholar 

  31. Prabhdeep S, Gurpreet S (2014) Mobility models and performance of MANET routing protocols: a review. Int J Comput Sci Technol 5(1):99–104

    Google Scholar 

  32. Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 29(27):1–22

    Google Scholar 

  33. Casals J, Garcia-Hiernaux A, Jerez M, Sotoca S, Trindade AA (2018) State-space methods for time series analysis: theory, applications and software. Chapman and Hall/CRC, London

    Book  Google Scholar 

  34. Prez M (2014) Time Series analysis with MATLAB: ARIMA and ARIMAX models. Create Space Independent Publishing Platform, California, USA

  35. Bengio S, Vinyals O, Jaitly N, Shazeer N (2015) Scheduled sampling for sequence prediction with recurrent neural networks. Adv Neural Inf Process Syst 28:854

    Google Scholar 

  36. Bianchi FM, Maiorino E, Kampffmeyer MC, Rizzi A, Jenssen R (2017) Recurrent neural networks for short-term load forecasting: an overview and comparative analysis. Springer, Switzerland

    Book  Google Scholar 

  37. Prasannavenkatesan T, Menakadevi T (2019) Resource-based routing protocol for mobile adhoc networks. Songklanakarin J Sci Technol 42(4) (in press)

  38. BonnMotion—a mobility scenario generation and analysis tool (2016) Documentation, University of Osnabruck. https://sys.cs.uos.de/bonnmotion/doc/README.pdf

  39. Banaezadeh F (201) ARIMA-modeling based prediction mechanism in object tracking sensor networks. In: Proceedings of the Information and Knowledge Technology. IEEE, pp 1–5

  40. Kim TH, Yang Q, Lee JH, Park SG, Shin YS (2007) A mobility management technique with simple handover prediction for 3G LTE systems. In: Proceedings of IEEE 66th Vehicular Technology Conference. IEEE, pp 259–263

  41. Lee JH, Kim HW, Choi YH, Chung YU, Rhee SH (2010) IP mobility performance enhancement using link-layer prediction. In: Proceedings of Future Generation Information Technology. Lecture Notes in Computer Science, vol 6485. Springer, Berlin

  42. Lee JH, Kim HW, Choi YH, Park J, Lee K (2010) ‘Predictive L3 handover in IEEE 802.16 vehicular networks. In: Proceedings of 5th International Conference on Ubiquitous Information Technologies and Applications. IEEE, pp 1–6

  43. Pal A, Singh JP, Dutta P (2014) Path length prediction in MANET under AODV routing: comparative analysis of ARIMA and MLP model. Egypt Inform J 16:103–111

    Article  Google Scholar 

  44. Singh JP, Dutta P (2010) Temporal modeling of node mobility in mobile ad hoc network. J Comput Inf Technol 18(1):19–29

    Article  Google Scholar 

  45. Lahyani I, Jmaiel M, Chassot C (2016) Analytical decisional model for latency aware publish/subscribe systems on MANET. J Syst Softw 122:484–495

    Article  Google Scholar 

  46. Guo Z, Malakooti S, Sheikh S, Al-Najjar C, Malakooti B (2011) Multi-objective OLSR for proactive routing in MANET with delay, energy, and link lifetime predictions. Appl Math Model 35(3):1413–1426

    Article  Google Scholar 

  47. Theerthagiri P, Gopala KC (2000) Vehicular multihop intelligent transportation framework for effective communication in vehicular ad-hoc networks. Concurr Comput Pract Exp 52:e6833

    Google Scholar 

  48. Jehangiri AI, Maqsood T, Umar AI, Shuja J, Ahmad Z, Dhaou IB, Alsharekh MF (2022) LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing. Clust Comput 11:1–9

    Google Scholar 

  49. Gopala Krishnan C, Nishan AH, Theerthagiri P (2022) k-means clustering based energy and trust management routing algorithm for mobile ad-hoc networks. Int J Commun Syst 52:e5138

    Google Scholar 

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  1. Department of Computer Science Engineering, GITAM School of Technology, GITAM University Bengaluru, Doddaballapura, India

    Prasannavenkatesan Theerthagiri

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Theerthagiri, P. Mobility prediction for random walk mobility model using ARIMA in mobile ad hoc networks. J Supercomput 78, 16453–16484 (2022). https://doi.org/10.1007/s11227-022-04503-6

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