Skip to main content

Advertisement

SpringerLink
Log in

ReCoMM: resource-aware cooperation modelling using Markov process for effective routing in mobile ad hoc networks

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

In Mobile Ad hoc Networks (MANETs), the most essential factor for successful routing is the cooperation of nodes. The node’s non-cooperative behavior causes routing problems and lowers network performance. The non-cooperation is related to a mobile node’s resource restriction characteristics. The battery energy is a significant restriction for a node since it runs out after a certain amount of time. The mobility of nodes, on the other hand, has an impact on routing performance. As a result, the focus of this research is on assessing a node’s collaboration by exploring futuristic node mobility and energy of the node. This study proposes the Resource-aware Cooperation Modeling with Markov Process (ReCoMM) for assessing link stability of the node in order to design effective routing. Using a Markov process, the ReCoMM model investigates the factors that influence cooperation and node state change. The Markov process is used to modify node durability and connection stability. The Markov process aids in the determination of the higher and smaller limits of cooperation with the computation of the cooperation value. The proposed ReCoMM model has been simulated, and performances were assessed with various scenarios using the NS2 simulator. The results show that the suggested ReCoMM produces 13–21 percent of higher packet delivery ratio than the existing methods. In a higher mobility scenario, the nodes’ remaining energy increases to 6–7 percent as compared to previous methods. Furthermore, it considerably outperforms previous models by average end-to-end latency and routing overhead.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  1. Chai Y and Zeng X J 2021 The development of green wireless mesh network: a survey. J. Smart Environ. Green Comput. 1(1): 47–59

    Google Scholar 

  2. Li X and Da X L 2020 A review of Internet of Things—resource allocation. IEEE Internet Things J. 8: 8657–8666

    Article  Google Scholar 

  3. Zaidi S, Atiquzzaman M and Calafate C T 2020 Internet of Flying Things (IoFT): a survey. Comput. Commun. 165: 53–74

  4. Deng Y, Gou F and Wu J 2021 Hybrid data transmission scheme based on source node centrality and community reconstruction in opportunistic social networks. In: Peer-to-Peer Networking and Applications, pp. 1–13

  5. Wang Y, Wang J, Zhang W, Zhan Y, Guo S, Zheng Q and Wang X 2021 A survey on deploying mobile deep learning applications: a systemic and technical perspective. Digital Commun. Netw. Article in press

  6. Palani U, Suresh K C and Nachiappan A 2018 Mobility prediction in mobile ad hoc networks using eye of coverage approach. Cluster Comput. 22: 14991–14998

    Article  Google Scholar 

  7. Theerthagiri P 2019 COFEE: context-aware futuristic energy estimation model for sensor nodes using Markov model and auto-regression. Int. J. Commun. Syst. e4248 Article in press

  8. Prasannavenkatesan T and Menakadevi T 2016 Significance of scalability for on-demand routing protocols in MANETs. In: IEEE Proceedings Conference on Emerging Devices & Smart Systems (ICEDSS2016). Namakkal, March 4-5, pp. 76–82

  9. Shivashankar H, Suresh N, Golla V and Jayanthi G 2014 Designing energy routing protocol with power consumption optimization in MANET. IEEE Trans. Emerg. Top. Comput. 2: 192–197

    Article  Google Scholar 

  10. Rashid U, Waqar O and Kiani A K 2017 Mobility and energy aware routing algorithm for mobile adhoc networks. In: IEEE Explore, pp. 1–5

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

    Google Scholar 

  12. Dash R K, Barpanda N K, Tripathy P K and Tripathy C R 2012 Network reliability optimization problem of interconnection network under node-edge failure model. Appl. Soft Comput. 12(8): 2322–2328

    Article  Google Scholar 

  13. Sengathir J and 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

  14. Jayalakshmi V and Razak T A 2016 Trust-based power aware secure source routing protocol using fuzzy logic for mobile ad hoc network. IAENG Int. J. Comput. Sci. 43(1): 1–10

    Google Scholar 

  15. Khamayseh Y, Obiedat G and Yassin M B 2011 Mobility and load aware routing protocol for ad hoc networks. J. King Saud Univ. Comput. Inf. Sci. 23(2): 105–113

  16. Rango F D and Guerriero F 2012 Link-stability and energy-aware routing protocol in distributed wireless networks. IEEE Trans. Parallel Distrib. Syst. 23(4): 713–726

    Article  Google Scholar 

  17. Manoharan R and Sengathir J 2016 Erlang coefficient based conditional probabilistic model for reliable data dissemination in MANETs. J. King Saud Univ. Comput. Inf. Sci. 28(3): 289–302

  18. Gopal D G and Saravanan R 2015 Fuzzy-based energy aware routing protocol with trustworthiness for MANET. Int. J. Electron. Inf. Eng. 3(2): 67–80

    Google Scholar 

  19. Tan W C, Bose S K and Cheng T H 2012 Power and mobility aware routing in wireless ad hoc networks. Inst. Eng. Technol. 6(11): 1425–1437

    MathSciNet  Google Scholar 

  20. Macone D, Oddi G and Pietrabissa A 2012 MQ-routing: mobility-, GPS- and energy-aware routing protocol in MANETs for disaster relief scenarios. In: Ad Hoc Networks, pp. 1–18

  21. Gite P 2017 Link stability prediction for mobile Ad hoc network route stability. In: IEEE International Conference on Inventive Systems and Control (ICISC), pp. 1–5

  22. Prakash J, Dutta P and Pal A 2012 Delay prediction in mobile ad hoc network using artificial neural network. Procedia Technol. 4: 201–206

    Article  Google Scholar 

  23. Yassir A, Nasir G A and Roy P 2013 Mobile ad hoc networks location prediction by using artificial neural networks: considerations and future directions. Int. J. Comput. Technol. Appl. 4(1): 120–125

    Article  Google Scholar 

  24. Akinola S O and Hamzat A B 2018 Link state prediction in mobile ad hoc network using Markov renewal process. Int. J. ICT Manag. 7: 26–43

    Google Scholar 

  25. Prasannavenkatesan T and Menakadevi T 2020 Resource-based routing protocol for mobile adhoc networks. Songklanakarin J. Sci. Technol. 42(4): 889–896

    Google Scholar 

  26. Chaudhari S S and Biradar R C 2014 Resource prediction based routing using wavelet neural network in mobile ad hoc networks. In: International Conference on Circuits, Communication, Control, and Computing, pp. 273–276

  27. Sengathir J and Manoharan R 2015 Exponential reliability coefficient based reputation mechanism for isolating selfish nodes in MANETs. Egypt. Inform. J. 16(2): 231–241

    Article  Google Scholar 

  28. Theerthagiri P and 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 

  29. Chao G and Zhu Q 2014 An energy-aware routing protocol for mobile ad hoc networks based on route energy comprehensive index. Wirel. Pers. Commun. 79: 1557–1570

    Article  Google Scholar 

  30. Theerthagiri P 2020 FUCEM: futuristic cooperation evaluation model using Markov process for evaluating node reliability and link stability in mobile ad hoc network. Wirel Netw 26(6): 4173–4188

    Article  Google Scholar 

  31. Prasannavenkatesan T, Rajakumar P and Pitchaikkannu A 2014 An effective intrusion detection system for MANETs. Proc. Int. J. Comput. Appl. (IJCA) 3: 29–34

    Google Scholar 

  32. BonnMotion Tool. Retrieved from http://sys.cs.uos.de/bonnmotion/

  33. NS2 simulator. Retrieved from http://www.isi.edu/nsnam/ns/

  34. AWK programming script. Retrieved from https://www.gnu.org/software/gawk/ manual/gawk.html

  35. Gopinath S and Nagarajan N 2015 Energy based reliable multicast routing protocol for packet forwarding in MANET. J. Appl. Res. Technol. 13: 374–381

    Article  Google Scholar 

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

    Google Scholar 

  37. Manohar D, AnandhaMala G S and AnandKumar K M 2017 Fault tolerant topology control with mobility prediction in MANETs for clinical care data transmission. Biomedical Research; Special Section: Artificial Intelligent Techniques for Bio-Medical Signal Processing. Special Issue: S36–S43

Download references

Author information

Authors and Affiliations

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

    Prasannavenkatesan Theerthagiri

Authors
  1. Prasannavenkatesan Theerthagiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prasannavenkatesan Theerthagiri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Theerthagiri, P. ReCoMM: resource-aware cooperation modelling using Markov process for effective routing in mobile ad hoc networks. Sādhanā 46, 209 (2021). https://doi.org/10.1007/s12046-021-01743-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-021-01743-9

Keywords

Search

Navigation