Skip to main content
SpringerLink
Log in

Intelligent Routing in Wireless Sensor Network Based on African Buffalo Optimization

  • Chapter
  • First Online:
Nature Inspired Computing for Wireless Sensor Networks

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Applications of wireless sensor network (WSN) have experienced a rapid growth recently due to the heterogeneous nature of network topology. At different levels, different entities such as source node, sink node, hop nodes and base station (BS) in WSN are positioned at remote locations to perform specific assigned operations. Since each sensor node in WSN employs battery having limited capacity, it is imperative to determine optimal routing which otherwise may lead to network transmission failure. This present work aims to introduce a new approach based on the African buffalo optimization (ABO) routing in the WSN. ABO is a nature-inspired combinatorial optimization technique based on the behavior of African buffalos. Here, ABO acts as the main controller of the WSN and manages all the sensor nodes in correspondence with the BS. It also helps to transfer data packets from the source node to the sink node efficiently. Further, it enhances the network lifetime and improves other performance metrics of the WSN.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Watt AJ, Phillips MR, Campbell CA, Wells I, Hole S (2019, June 1) Wireless sensor networks for monitoring underwater sediment transport. Sci Tot Env 667:160–165

    Article  Google Scholar 

  2. Skiadopoulos K, Tsipis A, Giannakis K, Koufoudakis G, Christopoulou E, Oikonomou K, Stavrakakis I (2019, June 1) Synchronization of data measurements in wireless sensor networks for IoT applications. Ad Hoc Netw 89:47–57

    Article  Google Scholar 

  3. Fong S, Li J, Song W, Tian Y, Wong RK, Dey N (2018, Aug) Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. J Ambient Intell Hum Comput 9(4):1197–1221

    Article  Google Scholar 

  4. Jain PK, Quamer W, Pamula R (2018) Electricity consumption forecasting using time series analysis. In: International conference on advances in computing and data sciences. Springer, Singapore, pp 327–335

    Google Scholar 

  5. Karati A, Biswas GP (2019) Provably secure and authenticated data sharing protocol for IoT-based crowdsensing network. Trans Emerg Telecommun Technol 30(4):e3315, 1–22

    Google Scholar 

  6. Karati A, Islam SH, Karuppiah M (2018) Provably secure and lightweight certificateless signature scheme for IIoT environments. IEEE Trans Ind Inf 14(8):3701–3711

    Article  Google Scholar 

  7. Panda SK, Jana PK (2019) An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Clust Comput 22(2):509–527

    Article  Google Scholar 

  8. Panda SK, Jana PK (2018) Normalization-based task scheduling algorithms for heterogeneous multi-cloud environment. Inf Syst Front 20(2):373–399

    Article  Google Scholar 

  9. Panda SK, Pande SK, Das S (2018) Task partitioning scheduling algorithms for heterogeneous multi-cloud environment. Arab J Sci Eng 43(2):913–933

    Google Scholar 

  10. Karati A, Amin R, Islam SH, Choo KKR (2018, May 8) Provably secure and lightweight identity-based authenticated data sharing protocol for cyber-physical cloud environment. IEEE Trans Cloud Comput, IEEE 1–14. https://doi.org/10.1109/TCC.2018.2834405

  11. Karati A, Islam SH, Biswas GP (2018) A pairing-free and provably secure certificateless signature scheme. Inf Sci 450:378–391

    MathSciNet  Google Scholar 

  12. Jain PK, Pamula R (2019) Two-step anomaly detection approach using clustering algorithm. International conference on advanced computing networking and informatics. Springer, Singapore, pp 513–520

    Chapter  Google Scholar 

  13. Mishra G, Agarwal S, Jain PK, Pamula R (2019) Outlier detection using subset formation of clustering based method. International conference on advanced computing networking and informatics. Springer, Singapore, pp 521–528

    Google Scholar 

  14. Kumari P, Jain PK, Pamula R (2018) An efficient use of ensemble methods to predict students academic performance. In: 4th international conference on recent advances in information technology (RAIT), IEEE, pp 1–6

    Google Scholar 

  15. Punam K, Pamula R, Jain PK (2018) A two-level statistical model for big mart sales prediction. In: 2018 international conference on computing, power and communication technologies (GUCON), IEEE, pp 617–620

    Google Scholar 

  16. Das SP, Padhy S (2018) A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting. Int J Mach Learn Cybernet 9(1):97–111

    Google Scholar 

  17. Das SP, Padhy S (2017) Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index. Memet Comput 9(4):333–346

    Article  Google Scholar 

  18. Das SP, Padhy S (2017) A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing. Neural Comput Appl 28(12):4061–4077

    Google Scholar 

  19. Curry RM, Smith JC (2016) A survey of optimization algorithms for wireless sensor network lifetime maximization. Comput Ind Eng 101:145–166

    Google Scholar 

  20. Yan Z, Goswami P, Mukherjee A, Yang L, Routray S, Palai G (2019) Low-energy PSO-based node positioning in optical wireless sensor networks. Optik 181:378–382

    Google Scholar 

  21. Yu X, Zhou L, Li X (2019) A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization. Comput Netw 154:73–78

    Article  Google Scholar 

  22. Phoemphon S, So-In C, Niyato DT (2018) A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization. Appl Soft Comput 65:101–120

    Article  Google Scholar 

  23. Ravi G, Kashwan KR (2015) A new routing protocol for energy efficient mobile applications for ad hoc networks. Comput Electr Eng 48:77–85

    Article  Google Scholar 

  24. Sun Z, Liu Y, Tao L (2018) Attack localization task allocation in wireless sensor networks based on multi-objective binary particle swarm optimization. J Netw Comput Appl 112:29–40

    Article  Google Scholar 

  25. Cao B, Zhao J, Lv Z, Liu X, Kang X, Yang S (2018) Deployment optimization for 3D industrial wireless sensor networks based on particle swarm optimizers with distributed parallelism. J Netw Comput Appl 103:225–238

    Article  Google Scholar 

  26. Das SK, Tripathi S (2018) Adaptive and intelligent energy efficient routing for transparent heterogeneous ad-hoc network by fusion of game theory and linear programming. Appl Intell 48(7):1825–1845

    Article  Google Scholar 

  27. Das SK, Yadav AK, Tripathi S (2017) IE2M: design of intellectual energy efficient multicast routing protocol for ad-hoc network. Peer-to-Peer Netw Appl 10(3):670–687

    Article  Google Scholar 

  28. Yadav AK, Das SK, Tripathi S (2017) EFMMRP: design of efficient fuzzy based multi-constraint multicast routing protocol for wireless ad-hoc network. Comput Netw 118:15–23

    Article  Google Scholar 

  29. Das SK, Tripathi S (2018) Intelligent energy-aware efficient routing for MANET. Wirel Netw 24(4):1–21

    Article  Google Scholar 

  30. Das SK, Tripathi S (2019) Energy efficient routing formation algorithm for hybrid ad-hoc network: a geometric programming approach. Peer-to-Peer Netw Appl 12(1):102–128

    Article  Google Scholar 

  31. 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 

  32. Das SK, Tripathi S (2017) Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques. Int J Commun Syst 30(16):e3340

    Article  Google Scholar 

  33. Zahedi ZM, Akbari R, Shokouhifar M, Safaei F, Jalali A (2016) Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Syst Appl 55:313–328

    Article  Google Scholar 

  34. Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput 30:1–10

    Article  Google Scholar 

  35. Azharuddin M, Jana PK (2016) Particle swarm optimization for maximizing lifetime of wireless sensor networks. Comput Electr Eng 51:26–42

    Article  Google Scholar 

  36. Sridhar S, Baskaran R, Chandrasekar P (2013) Energy supported AODV (EN-AODV) for QoS routing in MANET. Proc Soc Behav Sci 73:294–301

    Article  Google Scholar 

  37. Ouchitachen H, Hair A, Idrissi N (2017) Improved multi-objective weighted clustering algorithm in wireless sensor network. Egypt Inf J 18(1):45–54

    Google Scholar 

  38. Gholipour M, Haghighat AT, Meybodi MR (2017) Hop-by-hop congestion avoidance in wireless sensor networks based on genetic support vector machine. Neurocomputing 223:63–76

    Google Scholar 

  39. Bhatia T, Kansal S, Goel S, Verma AK (2016) A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Comput Electr Eng 56:441–455

    Google Scholar 

  40. Ray A, De D (2016) An energy efficient sensor movement approach using multi-parameter reverse glowworm swarm optimization algorithm in mobile wireless sensor network. Simul Model Pract Theor 62:117–136

    Article  Google Scholar 

  41. Taherian M, Karimi H, Kashkooli AM, Esfahanimehr A, Jafta T, Jafarabad M (2015) The design of an optimal and secure routing model in wireless sensor networks by using PSO algorithm. Proc Comput Sci 73:468–473

    Article  Google Scholar 

  42. Barekatain B, Dehghani S, Pourzaferani M (2015) An energy-aware routing protocol for wireless sensor networks based on new combination of genetic algorithm & k-means. Proc Comput Sci 72:552–560

    Article  Google Scholar 

  43. Dhivya M, Sundarambal M (2012) Lifetime maximization in wireless sensor networks using Tabu swarm optimization. Proc Eng 38:511–516

    Article  MATH  Google Scholar 

  44. Das SK, Samanta S, Dey N, Kumar R (2020) Design frameworks for wireless networks. Lecture notes in networks and systems. Springer, Singapore, pp 1–439. ISBN: 978-981-13-9573-4

    Google Scholar 

  45. Samantra A, Panda A, Das SK, Debnath S (2020) Fuzzy petri nets-based intelligent routing protocol for Ad Hoc network. In: Design frameworks for wireless networks, Springer, Singapore, pp 417–433

    Google Scholar 

  46. Das SK, Sachin T (2020) A nonlinear strategy management approach in software-defined ad hoc network. In: Design frameworks for wireless networks. Springer, Singapore, pp 321–346

    Google Scholar 

  47. Dey N, Ashour AS, Bhattacharyya S (2019) Applied nature-inspired computing: algorithms and case studies, pp 1–275

    Google Scholar 

  48. Dey N, Ashour AS, Shi F, Fong SJ, Sherratt RS (2017) Developing residential wireless sensor networks for ECG healthcare monitoring. IEEE Trans Consum Electron 63(4):442–449

    Article  Google Scholar 

  49. Elhayatmy G, Dey N, Ashour AS (2018) Internet of things based wireless body area network in healthcare. In: Internet of things and big data analytics toward next-generation intelligence. Springer, Cham, pp 3–20

    Google Scholar 

  50. Mukherjee A, Dey N, Kausar N, Ashour AS, Taiar R, Hassanien AE (2019) A disaster management specific mobility model for flying ad-hoc network. In: Emergency and disaster management: concepts, methodologies, tools, and applications. IGI Global, pp 279–311

    Google Scholar 

  51. Roy S, Karjee J, Rawat US, Dey N (2016) Symmetric key encryption technique: a cellular automata based approach in wireless sensor networks. Proc Comput Sci 78:408–414

    Article  Google Scholar 

  52. Das SK, Tripathi S, Burnwal AP (2015) Intelligent energy competency multipath routing in wanet. In: Information systems design and intelligent applications. Springer, New Delhi, pp 535–543

    Google Scholar 

  53. Odili JB, Kahar MNM (2016) African buffalo optimization. Int J Soft Eng Comput Syst 2(1):28–50

    Article  Google Scholar 

  54. Paul JD, Roberts GG, White N (2014) The African landscape through space and time. Tectonics 33(6):898–935

    Article  Google Scholar 

  55. Lorenzen ED, Heller R, Siegismund HR (2012) Comparative phylogeography of African savannah ungulates 1. Mol Ecol 21(15):3656–3670

    Article  Google Scholar 

  56. Odili JB, Kahar MNM, Anwar S, Ali M (2017) Tutorials on African buffalo optimization for solving the travelling salesman problem. Int J Softw Eng Comput Syst 3(3):120–128

    Google Scholar 

  57. Odili JB, Kahar MNM, Anwar S (2015) African buffalo optimization: a swarm-intelligence technique. Proc Comput Sci 76:443–448

    Article  Google Scholar 

  58. Mokshin AV, Mokshin VV, Sharnin LM (2019) Adaptive genetic algorithms used to analyze behavior of complex system. Commun Nonlinear Sci Numer Simul 71:174–186

    Article  MathSciNet  Google Scholar 

  59. Franzin A, Stützle T (2019) Revisiting simulated annealing: a component-based analysis. Comput Oper Res 104:191–206

    Article  MathSciNet  MATH  Google Scholar 

  60. Jia ZH, Wang Y, Wu C, Yang Y, Zhang XY, Chen HP (2019, May) Multi-objective energy-aware batch scheduling using ant colony optimization algorithm. Comput Ind Eng 131:41–56

    Article  Google Scholar 

  61. Latchoumi TP, Balamurugan K, Dinesh K, Ezhilarasi TP (2019) Particle swarm optimization approach for waterjet cavitation peening. Measurement 141:184–189

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Studies, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India

    Samiran Bera

  2. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Institute Park, Pallur Hills, Berhampur, Odisha, 761008, India

    Santosh Kumar Das

  3. Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung, 80424, Taiwan

    Arijit Karati

Authors
  1. Samiran Bera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santosh Kumar Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arijit Karati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santosh Kumar Das .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University, Kolkata, West Bengal, India

    Debashis De

  2. Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India

    Amartya Mukherjee

  3. School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India

    Santosh Kumar Das

  4. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bera, S., Das, S.K., Karati, A. (2020). Intelligent Routing in Wireless Sensor Network Based on African Buffalo Optimization. In: De, D., Mukherjee, A., Kumar Das, S., Dey, N. (eds) Nature Inspired Computing for Wireless Sensor Networks. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2125-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2125-6_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2124-9

  • Online ISBN: 978-981-15-2125-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation