Skip to main content

AGEN-AODV: an Intelligent Energy-Aware Routing Protocol for Heterogeneous Mobile Ad-Hoc Networks

Abstract

Mobile Ad-hoc Networks (MANETs) consist of mobile nodes that usually have limited energy resources. MANET routing protocols should consider the dynamics and energy constraints of the network, and this makes them an optimization problem. Various optimization-based MANET routing protocols have been proposed in literature and each of them consider different metrics and try to cope with specific problems. In this paper, a novel heterogeneous MANET routing protocol called “learning Automata and Genetic based Ad hoc On-Demand Distance Vector” (AGEN-AODV) is proposed, in which routes are rated based on energy, stability, traffic, and hop-count criteria. The Genetic Algorithm (GA) in conjunction with Learning Automata (LA) is used to select the optimal route. The LA runs concurrent to the GA and initializes, adjusts and optimizes its coefficients based on the network feedback, preventing the GA from divergence or sub-optimal convergence. Compared with related works, the throughput, packet delivery ratio (PDR), delay, network lifetime, and energy consumption are improved by at least 4%, 8%, 8%, 13%, and 30% respectively.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Bahaghighat M, Akbari L, Xin Q (2019) A machine learning-based approach for counting blister cards within drug packages. IEEE Access 7:83785–83796

    Article  Google Scholar 

  2. 2.

    Maadani M (2019) Reanalyzing a simplified Markov model for the low-density P2P wireless sensor and actuator networks. Telecommun Syst 70(2):159–169

    Article  Google Scholar 

  3. 3.

    Rahimi M, Songhorabadi M, Haghi Kashani M (2020) Fog-based smart homes: a systematic review. J Netw Comput Appl 153:102531. https://doi.org/10.1016/j.jnca.2020.102531

    Article  Google Scholar 

  4. 4.

    Nasrollahzadeh S, Maadani M, Pourmina MA (2021) Optimal motion sensor placement in smart homes and intelligent environments using a hybrid WOA-PSO algorithm. J Reliab Intell Environ Accepted:1–20

  5. 5.

    Esmaeili Kelishomi A, Garmabaki A, Bahaghighat M, Dong J (2019) Mobile user indoor-outdoor detection through physical daily activities. Sensors 19(3):511

    Article  Google Scholar 

  6. 6.

    Alimorad N, Maadani M, Mahdavi M (2021) REO: a reliable and energy efficient optimization algorithm for beacon-enabled 802.15.4–based wireless body area networks. IEEE Sensors J:1–8. https://doi.org/10.1109/JSEN.2021.3091768

  7. 7.

    Haghi Kashani M, Madanipour M, Nikravan M, Asghari P, Mahdipour E (2021) A systematic review of IoT in healthcare: applications, techniques, and trends. J Netw Comput Appl 192. https://doi.org/10.1016/j.jnca.2021.103164

  8. 8.

    Maadani M, Shabro M, Alavikia Z (2019) Analysis of demand-side business opportunities in Iran, as a digital transformation perspective. 2019 international power system conference (PSC): 46-51

  9. 9.

    Bahaghighat M, Motamedi SA, Xin Q (2019) Image transmission over cognitive radio networks for smart grid applications. Appl Sci 9(24):5498

    Article  Google Scholar 

  10. 10.

    Karimi Y, Haghi Kashani M, Akbari M, Mahdipour E (2021) Leveraging big data in smart cities: a systematic review. Concurr Comput Pract Experience. https://doi.org/10.1002/cpe.6379

  11. 11.

    Fathi M, Haghi Kashani M, Jameii SM, Mahdipour E (2021) Big data analytics in weather forecasting: a systematic review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-021-09616-4

  12. 12.

    Quy VK, Nam VH, Linh DM, Ban NT, Han ND (2021) A survey of QoS-aware routing protocols for the MANET-WSN convergence scenarios in IoT networks. Wirel Pers Commun:1–14

  13. 13.

    Mehboob U, Qadir J, Ali S, Vasilakos AJSC (2016) Genetic algorithms in wireless networking: techniques, applications, and issues. Soft Comput 20(6):2467–2501

    Article  Google Scholar 

  14. 14.

    Rezvanian A, Saghiri AM, Vahidipour SM, Esnaashari M, Meybodi MR (2018) Recent advances in learning automata, vol 754. Springer

    Book  Google Scholar 

  15. 15.

    Liu S, Wang S, Liu X, Gandomi AH, Daneshmand M (2021) Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans Multimedia Online First 23:2188–2198. https://doi.org/10.1109/TMM.2021.3065580

    Article  Google Scholar 

  16. 16.

    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 

  17. 17.

    Joshi RD, Rege PP (2018) Distributed energy efficient routing in ad hoc networks. In: 2008 Fourth International Conference on Wireless Communication and Sensor Networks: 16–21

  18. 18.

    Maadani M, Motamedi SA (2014) A simple and comprehensive saturation packet delay model for wireless industrial networks. Wirel Pers Commun 77(1):365–381

    Article  Google Scholar 

  19. 19.

    Maadani M, Motamedi SA (2014) A simple and closed-form access delay model for reliable IEEE 802.11-based wireless industrial networks. Wirel Pers Commun 75(4):2243–2268

    Article  Google Scholar 

  20. 20.

    Eissa T, Razak SA, Khokhar RH, Samian N (2013) Trust-based routing mechanism in MANET: design and implementation. Mob Netw Appl 18(5):666–677

    Article  Google Scholar 

  21. 21.

    Soltani MR, Motamedi SA, Ahmadi S, Maadani M (2011) Power-aware and void-avoidant routing protocol for reliable industrial wireless sensor networks. 7th international conference on wireless communications, networking and Mobile computing: 1-5

  22. 22.

    Liu S, Guo C, Al-Turjman F, Muhammad K, de Albuquerque VHC (2020) Reliability of response region: a novel mechanism in visual tracking by edge computing for IIoT environments. Mech Syst Signal Process 138:106537

    Article  Google Scholar 

  23. 23.

    Srinivasan P, Kamalakkannan P, Shantharajah S (2013) Stability and energy aware multipath routing for mobile ad hoc networks. Int J Comput Appl 74(16)

  24. 24.

    Prasad SK, Bhatia K (2014) RSAODV: a route stability based ad hoc on demand distance vector routing protocol for mobile ad hoc network. Int J Wirel Mob Netw 6(6):113

    Article  Google Scholar 

  25. 25.

    Kumar R, Gupta M (2014) Route stability and energy aware based AODV in MANET. International conference on high performance computing and applications (ICHPCA): 1-5

  26. 26.

    Sinwar D, Sharma N, Maakar SK, Kumar S (2020) Analysis and comparison of ant colony optimization algorithm with DSDV, AODV, and AOMDV based on shortest path in MANET. J Inf Optim Sci 41(2):621–632

    Google Scholar 

  27. 27.

    Nallusamy C, Sabari A (2019) Particle swarm based resource optimized geographic routing for improved network lifetime in MANET. Mob Netw Appl 24(2):375–385

    Article  Google Scholar 

  28. 28.

    Sarvizadeh R, Kashani MH, Zakeri FS, Jameii SM (2012) A novel bee colony approach to distributed systems scheduling. Int J Comput Appl 42(10):1–6

    Google Scholar 

  29. 29.

    Liu S, Wang S, Liu X, Lin C-T, Lv Z (2020) Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans Fuzzy Syst

  30. 30.

    Sajedi SN, Maadani M, Nesari Moghadam M (2021) F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems. J Supercomput:1–18. https://doi.org/10.1007/s11227-021-03890-6

  31. 31.

    Joshua CJ, Duraisamy R, Varadarajan V (2019) A reputation based weighted clustering protocol in VANET: a multi-objective firefly approach. Mob Netw Appl 24(4):1199–1209

    Article  Google Scholar 

  32. 32.

    Mao Y, Zhu P (2015) A game theoretical model for energy-aware DTN routing in MANETs with nodes’ selfishness. Mob Netw Appl 20(5):593–603

    Article  Google Scholar 

  33. 33.

    Seetaram J, Kumar PS (2016) An energy aware genetic algorithm multipath distance vector protocol for efficient routing. International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET): 1975–1980

  34. 34.

    Ahuja S, Kaur S (2014) An energy efficient approach for routing in MANETS using GA and ACO. Int J Sci Res 3(8):1044–1049

    Google Scholar 

  35. 35.

    Maadani M, Motamedi SA, Soltani M (2012) EDCA delay analysis of spatial multiplexing in IEEE802. 11-based wireless sensor and actuator networks. Int J Inf Electron Eng 2(3):318

    Google Scholar 

  36. 36.

    Singh SB, Ambhaikar A (2013) Optimization of routing protocol in MANET using GA. Citeseer,

  37. 37.

    Al Balas F, Khamayseh YM, Jazoh RMA, Awajan AW (2017) An improved approach for route selection in MANETs using genetic algorithm for smart grids applications. International conference on electrical and computing technologies and applications (ICECTA): 1-6

  38. 38.

    Kumar A, Dadheech P, Kumari R, Singh V (2019) An enhanced energy efficient routing protocol for VANET using special cross over in genetic algorithm. J Stat Manag Syst 22(7):1349–1364

    Google Scholar 

  39. 39.

    Nareshkumar R, Phanikumar S, Singh MK (2018) Intelligent routing in MANET using self-adaptive genetic algorithm. In: Advances in systems, Control and Automation. Springer, pp. 595–603

  40. 40.

    Zhang G, Wu M, Duan W, Huang X (2018) Genetic algorithm based QoS perception routing protocol for VANETs. Wirel Commun Mob Comput

  41. 41.

    Thamaraikannan N, Kamalraj S (2019) Utilization of compact genetic algorithm for optimal shortest path selection to improve the throughput in mobile ad-hoc networks. Clust Comput 22(2):3715–3726

    Article  Google Scholar 

  42. 42.

    Prasad A, Rayanki B (2019) A generic algorithmic protocol approaches to improve network life time and energy efficient using combined genetic algorithm with simulated annealing in MANET. Int J Intell Unmanned Syst

  43. 43.

    Dg Z, Liu S, Liu X, Zhang T, Yy C (2018) Novel dynamic source routing protocol (DSR) based on genetic algorithm-bacterial foraging optimization (GA-BFO). Int J Commun Syst 31(18):e3824

    Article  Google Scholar 

  44. 44.

    Preetha V, Chitra K (2018) ZBMRP: zone based MANET routing protocol with genetic algorithm and security enhancement using neural network learning. IJ Netw Secur 20(6):1115–1124

    Google Scholar 

  45. 45.

    Gurumoorthy K, Kumar AN (2018) Mutual constraint based GA suggested routing algorithm for improving QoS in clustered MANETS. Wirel Pers Commun 98(3):2975–2991

    Article  Google Scholar 

  46. 46.

    Ahmad M, Hameed A, Ullah F, Wahid I, Rehman SU, Khattak HA (2018) A bio-inspired clustering in mobile adhoc networks for internet of things based on honey bee and genetic algorithm. J Ambient Intell Humanized Comput:1–15

  47. 47.

    Bhardwaj A, El-Ocla H (2020) Multipath routing protocol using genetic algorithm in Mobile ad hoc networks. IEEE Access 8:177534–177548

    Article  Google Scholar 

  48. 48.

    Rajan C, Shanthi N (2015) Genetic based optimization for multicast routing algorithm for MANET. Sadhana 40(8):2341–2352

    MathSciNet  Article  Google Scholar 

  49. 49.

    KUMARAN NS, RAMASAMY A (2016) Energy efficient multiconstrained optimization using hybrid ACO and GA in MANET routing. Turk J Electr Eng Comput Sci 24(5):3698–3713

    Google Scholar 

  50. 50.

    Krishna SRKM, Ramanath MBNS, Prasad VK (2018) Optimal reliable routing path selection in MANET through hybrid PSO-GA optimisation algorithm. Int J Mob Netw Des Innov 8(4):195–206

    Google Scholar 

  51. 51.

    Jameii SM, Maadani M (2016) Intelligent dynamic connectivity control algorithm for cluster-based wireless sensor networks. 11th international conference for internet technology and secured transactions (ICITST): 416-420

  52. 52.

    Norouzi Shad M, Maadani M, Nesari Moghadam M (2021) GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer. Wirel Pers Commun Accepted:1–19

  53. 53.

    Perkins CE, Royer EM (1999) Ad-hoc on-demand distance vector routing. Proceedings WMCSA'99. Second IEEE workshop on Mobile computing systems and applications: 90-100

  54. 54.

    Wong W, Mok P, Leung S (2013) Optimizing apparel production systems using genetic algorithms. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): form production to retail Woodhead Publishing Series in Textiles:153–169

  55. 55.

    Lima JA, Gracias N, Pereira H, Rosa A (1996) Fitness function design for genetic algorithms in cost evaluation based problems. Proceedings of IEEE International Conference on Evolutionary Computation: 207–212

  56. 56.

    Kellegöz T, Toklu B, Wilson J (2008) Comparing efficiencies of genetic crossover operators for one machine total weighted tardiness problem. Appl Math Comput 199(2):590–598

    MathSciNet  MATH  Google Scholar 

  57. 57.

    Hassanat A, Almohammadi K, Alkafaween E, Abunawas E, Hammouri A, Prasath V (2019) Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach. Information 10(12):390

    Article  Google Scholar 

  58. 58.

    Lynch M (2010) Evolution of the mutation rate. Trends Genet 26(8):345–352

    Article  Google Scholar 

  59. 59.

    Maadani M, Motamedi SA (2016) A comprehensive DCF performance analysis in noisy industrial wireless networks. Int J Commun Syst 29(11):1720–1739

    Article  Google Scholar 

  60. 60.

    Maadani M, Motamedi SA, Safdarkhani H, Parsa M (2014) Saturated distributed coordination function Markov model for noisy soft-real-time industrial wireless networks. IET Commun 8(10):1724–1735

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mohsen Maadani.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nabati, M., Maadani, M. & Pourmina, M.A. AGEN-AODV: an Intelligent Energy-Aware Routing Protocol for Heterogeneous Mobile Ad-Hoc Networks. Mobile Netw Appl (2021). https://doi.org/10.1007/s11036-021-01821-6

Download citation

Keywords

  • Routing
  • Mobile ad hoc network (MANET)
  • Artificial intelligence
  • Genetic algorithm
  • Learning automata
  • Energy efficiency