Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks

  • Y. Harold Robinson
  • E. Golden Julie
  • Raghvendra Kumar
  • Le Hoang SonEmail author


In this paper, we propose a new power-aware routing protocol for Wireless Sensor Network (WSN) based on the threshold rate and fuzzy logic for improving energy efficiency. The cluster heads are elected based on the probability values of every node in WSN, which are calculated from the remaining energy of every node. Cumulative remaining node energy is used to calculate mean energy of the whole network of the current phase. The nodes with high probability will have more chances to be selected as the cluster head, which gathers packets from the cluster member via single hop communication. The cluster head forwards the gathered data to sink by using fuzzy control with multi-hop communication. Fuzzy control takes three parameters namely queue length of a node, the distance of a node from the base station, and the remaining energy of node. The evidence from experiments suggest that the proposed energy efficient cluster-based routing protocol method (called MLSEEP) gives better results than the existing protocols by the supplement of those techniques.


Wireless sensor network Fuzzy control Cluster head Fuzzy multipath routing Network lifetime 


Compliance with ethical standards

Conflict of interests

The authors declare that they do not have any conflict of interests. This research does not involve any human or animal participation. All authors have checked and agreed the submission.


  1. 1.
    Djenouri D, Bagaa M (2017) Energy-aware constrained relay node deployment for sustainable wireless sensor networks. IEEE Transactions on Sustainable Computing 2(1):30–42Google Scholar
  2. 2.
    Manjeshwar A, Agrawal DP (2001) TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In: Null (p. 30189a). IEEEGoogle Scholar
  3. 3.
    Getu TM, Ajib W, Yeste-Ojeda OA (2017) Tensor-Based Efficient Multi-Interferer RFI Excision Algorithms for SIMO Systems. IEEE Trans Commun 65(7):3037–3052Google Scholar
  4. 4.
    Wang Z, Zhang L, Zheng Z, Wang J (2018) Energy balancing RPL protocol with multipath for wireless sensor networks. Peer-to-Peer Networking and Applications 11(5):1085–1100Google Scholar
  5. 5.
    Nguyen TG, So-In C, Nguyen NG, Phoemphon S (2017) A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks. Peer-to-Peer Networking and Applications 10(3):519–536Google Scholar
  6. 6.
    Kumar D (2013) Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems 4(1):9–16Google Scholar
  7. 7.
    Zakariayi S, Babaie S (2018) DEHCIC: A distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks. Peer-to-Peer Networking and Applications:1–16Google Scholar
  8. 8.
    Gholami M, Panahi A (2014) Enhancing nodes lifetime optimum protocol for dissemination of information in WSN. International Journal of Computers Communications & Control 9(3):276–283Google Scholar
  9. 9.
    Lee H, Jang M, Chang JW (2014) A new energy-efficient cluster-based routing protocol using a representative path in wireless sensor networks. International Journal of Distributed Sensor Networks 10(7):527928Google Scholar
  10. 10.
    Farooq MO, Dogar AB, Shah GA (2010) MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy. In Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on (pp. 262–268). IEEEGoogle Scholar
  11. 11.
    Khelifi M, Djabelkhir A (2012) LMEEC: Layered multi-hop energy efficient cluster-based routing protocol for wireless sensor networks. arXiv preprint arXiv:1201.0725 Google Scholar
  12. 12.
    Yoon M, Kim YK, Chang JW (2013) An energy-efficient routing protocol using message success rate in wireless sensor networks. JoC 4(1):15–22Google Scholar
  13. 13.
    Saini P, Sharma AK (2010) Energy efficient scheme for clustering protocol prolonging the lifetime of heterogeneous wireless sensor networks. Int J Comput Appl 6(2):48–53Google Scholar
  14. 14.
    Lindsey S, Raghavendra CS (2002) PEGASIS: Power-efficient gathering in sensor information systems. In: Aerospace conference proceedings, 2002. IEEE (Vol. 3, pp. 3–3). IEEEGoogle Scholar
  15. 15.
    Nikolidakis SA, Kandris D, Vergados DD, Douligeris C (2013) Energy efficient routing in wireless sensor networks through balanced clustering. Algorithms 6(1):29–42MathSciNetzbMATHGoogle Scholar
  16. 16.
    Saravanan T, Saritha G, Srinivsan V (2014) A analysis of flat routing protocols in sensor N/W. Middle-East J Sci Res 20(12):2566–2570Google Scholar
  17. 17.
    Tam NT, Hai DT, Son LH, Vinh LT (2018) Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel Netw:1–14Google Scholar
  18. 18.
    Ha YG, Kim H, Byun YC (2012) Energy-efficient fire monitoring over cluster-based wireless sensor networks. International Journal of Distributed Sensor Networks 8(2):460754Google Scholar
  19. 19.
    Yong Z, Pei Q (2012) A energy-efficient clustering routing algorithm based on distance and residual energy for wireless sensor networks. Procedia Engineering 29:1882–1888Google Scholar
  20. 20.
    Sarkar A, Murugan TS (2017) Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wirel Netw:1–18Google Scholar
  21. 21.
    Mao X, Tang S, Li X (2011) Energy Efficient Opportunistic Routing in Wireless Sensor Networks. IEEE Transactions and Parallel And Distributed System 15(2):551–591Google Scholar
  22. 22.
    Zhan G, Shi W (2011) Design and Implementation of TARF: A Trust Aware Routing Framework for Wireless Sensor Networks. IEEE Transactions on Dependable And Secure Computing 9(2):158–162Google Scholar
  23. 23.
    Li Q, Gao G (2012) Mitigating Routing Misbehaviour in Disruption Tolerant networks. IEEE Transactions on Information Forensics and Security 7(2):192–201MathSciNetGoogle Scholar
  24. 24.
    Lee J, Cheng W (2012) Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication. IEEE Sensors J 12(9):58–63Google Scholar
  25. 25.
    Ahras E, Pourmoslemi A (2009) FEAR: A Fuzzy-based Energy-Aware Routing protocol for Wireless Sensor Networks. The International Arab Journal of Information Technology 6(2):69–72Google Scholar
  26. 26.
    Singh S, Meenaxi A (2013) A Survey On Energy Efficient Routing in Wireless Sensor Networks. International Journal of Advance Research in Computer Science and Software Engineering 3(7):169–175Google Scholar
  27. 27.
    AlMomani IM, Saadeh MA (2011) FEAR: Fuzzy-Based Energy Aware Routing Protocol for Wireless Sensor Network. Int’I of Communications, Network and System Sciences 4:403–415Google Scholar
  28. 28.
    Jha S, Son LH, Kumar R, Priyadarshini I, Smarandache F, Long HV (2019) Neutrosophic Image Segmentation with Dice Coefficients. Measurement 134:762–772Google Scholar
  29. 29.
    Nguyen GN, Son LH, Ashour AS, Dey N (2019) A survey of the State-of-the-arts on Neutrosophic Sets in Biomedical Diagnoses. Int J Mach Learn Cybern 10(1):1–13Google Scholar
  30. 30.
    Kapoor R, Gupta R, Kumar R, Son LH, Jha S (2019) New Scheme for Underwater Acoustically Wireless Transmission Using Direct Sequence Code Division Multiple Access in MIMO Systems. Wirel Netw.
  31. 31.
    Son LH, Jha S, Kumar R, Chatterjee JM, Khari M (2019) Collaborative handshaking approaches between Internet of Computing and Internet of Things towards a Smart World: A review from 2009 – 2017. Telecommun Syst.
  32. 32.
    Son LH, Fujita H (2019) Neural-Fuzzy with Representative Sets for Prediction of Student Performance. Appl Intell 49(1):172–187Google Scholar
  33. 33.
    Saravanan K, Aswini S, Kumar R, Son LH (2019) How to Prevent Maritime Border Collision for Fisheries?-A Design of Real-Time Automatic Identification System. Earth Sci Inf.
  34. 34.
    Long HV, Ali M, Son LH, Khan M, Doan Ngoc T (2019) A novel approach for Fuzzy Clustering based on Neutrosophic Association Matrix. Comput Ind Eng.
  35. 35.
    Harold Robinson Y, Golden Julie E, Saravanan K, Kumar R, Son LH (2019) FD-AOMDV: Fault-Tolerant Disjoint Ad-hoc On-Demand Multipath Distance Vector Routing algorithm in Mobile Ad-hoc Networks. J Ambient Intell Humaniz Comput.
  36. 36.
    Singh N, Son LH, Chiclana F, Magnot J-P (2019) A New Fusion of Salp Swarm with Sine Cosine for Optimization of Non-linear Functions. Eng Comput.
  37. 37.
    Sumit K, Bansal RK, Mittal M, Goyal LM, Kaur I, Verma A, Son LH (2019) Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. Journal of the Indian Society of Remote Sensing.
  38. 38.
    Son PH, Son LH, Jha S, Kumar R, Chatterjee JM (2019) Governing Mobile Virtual Network Operators in Developing Countries. Util Policy 56:169–180Google Scholar
  39. 39.
    Garg R, Mittal M, Son LH (2019) Reliability and Energy Efficient Workflow Scheduling in Cloud Environment. Clust Comput.
  40. 40.
    Kapoor R, Gupta R, Son LH, Jha S, Kumar R (2019) Adaptive Technique with Cross Correlation for Lowering Signal-to-Noise Ratio Wall in Sensor Networks. Wirel Pers Commun.
  41. 41.
    Kapoor R, Gupta R, Son LH, Kumar R (2019) Iris Localization for Direction and Deformation Independence based on Polynomial Curve Fitting and Singleton Expansion. Multimed Tools Appl.
  42. 42.
    Son LH, Tuan TM, Fujita H, Dey N, Ashour AS, Ngoc VTN, Anh LQ, Chu D-T (2018) Dental Diagnosis from X-Ray Images: An Expert System based on Fuzzy Computing. Biomedical Signal Processing and Control 39C:64–73Google Scholar
  43. 43.
    Ali M, Son LH, Khan M, Tung NT (2018) Segmentation of Dental X-ray Images in Medical Imaging using Neutrosophic Orthogonal Matrices. Expert Syst Appl 91:434–441Google Scholar
  44. 44.
    Tam NT, Hai DT, Son LH, Vinh LT (2018) Improving lifetime and network connections of 3D Wireless Sensor Networks based on fuzzy clustering and particle swarm optimization. Wirel Netw 24(5):1477–1490Google Scholar
  45. 45.
    Ngan RT, Ali M, Son LH (2018) Delta-Equality of Intuitionistic Fuzzy Sets: A New Proximity Measure and Applications in Medical Diagnosis. Appl Intell 48(2):499–525Google Scholar
  46. 46.
    Ali M, Dat LQ, Son LH, Smarandache F (2018) Interval Complex Neutrosophic Set: Formulation and Applications in Decision-Making. International Journal of Fuzzy Systems 20(3):986–999Google Scholar
  47. 47.
    Jude Hemanth D, Anitha J, Popescu DE, Son LH (2018) A Modified Genetic Algorithm for Performance Improvement of Transform based Image Steganography Systems. J Intell Fuzzy Syst 35(1):197–209Google Scholar
  48. 48.
    Ali M, Son LH, Thanh ND, Van Minh N (2018) A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures. Appl Soft Comput 71:1054–1071Google Scholar
  49. 49.
    Giap CN, Son LH, Chiclana F (2018) Dynamic Structural Neural Network. J Intell Fuzzy Syst 34:2479–2490Google Scholar
  50. 50.
    Kapoor R, Gupta R, Son LH, Jha S, Kumar R (2018) Detection of Power Quality Event using Histogram of Oriented Gradients and Support Vector Machine. Measurement 120:52–75Google Scholar
  51. 51.
    Singh K, Singh K, Son LH, Aziz A (2018) Congestion Control in Wireless Sensor Networks by Hybrid Multi-Objective Optimization Algorithm. Comput Netw 138:90–107Google Scholar
  52. 52.
    Pham BT, Son LH, Hoang T-A, Nguyen D-M, Bui DT (2018) Prediction of Shear Strength of Soft Soil Using Machine Learning Methods. Catena 166:181–191Google Scholar
  53. 53.
    Jude Hemanth D, Anitha J, Son LH (2018) Brain Signal based Human Emotion Analysis by Circular Back Propagation and Deep Kohonen Neural Networks. Comput Electr Eng 68:170–180Google Scholar
  54. 54.
    Ngan RT, Son LH, Cuong BC, Ali M (2018) H-max distance measure of intuitionistic fuzzy sets in decision making. Appl Soft Comput 69:393–425Google Scholar
  55. 55.
    Son LH, Chiclana F, Kumar R, Mittal M, Khari M, Chatterjee JM, Baik SW (2018) ARM-AMO: An Efficient Association Rule Mining Algorithm Based on Animal Migration Optimization. Knowl-Based Syst 154:68–80Google Scholar
  56. 56.
    Kapoor R, Gupta R, Son LH, Jha S, Kumar R (2018) Boosting Performance of Power Quality Event Identification with KL Divergence Measure and Standard Deviation. Measurement 126:134–142Google Scholar
  57. 57.
    Le T, Son LH, Vo MT, Lee MY, Baik SW (2018) A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset. Symmetry-Basel 10:250–262Google Scholar
  58. 58.
    Khan M, Son LH, Ali M, Chau HTM, Na NTN, Smarandache F (2018) Systematic Review of Decision Making Algorithms in Extended Neutrosophic Sets. Symmetry-Basel 10:314–342Google Scholar
  59. 59.
    Saravanan K, Anusuya E, Kumar R, Son LH (2018) Real-time water quality monitoring using Internet of Things in SCADA. Environ Monit Assess 190:556–572Google Scholar
  60. 60.
    Dey A, Le Hoang S, Kishore Kumar PK, Selvachandran G, Quek SG (2018) New Concepts on Vertex and Edge Coloring of Simple Vague Graphs. Symmetry-Basel 10(9):373–391Google Scholar
  61. 61.
    Doss S, Anand N, Suseendran G, Tanwar S, Khanna A, Son LH, Thong PH (2018) APD-JFAD: Accurate Prevention and Detection of Jelly Fish Attack in MANET. IEEE Access 6:56954–56965Google Scholar
  62. 62.
    Ali M, Khan H, Son LH, Florentin S, Vasantha Kandasamy WB (2018) New Soft Set Based Class of Linear Algebraic Codes. Symmetry-Basel 10(10):510–520Google Scholar
  63. 63.
    Jude Hemanth D, Anitha J, Son LH, Mittal M (2018) Diabetic Retinopathy Diagnosis from Retinal Images using Modified Hopfield Neural Network. J Med Syst 42:247–253Google Scholar
  64. 64.
    Ngan TT, Lan LTH, Ali M, Tamir D, Son LH, Tuan TM, Rishe N, Kandel A (2018) Logic Connectives of Complex Fuzzy Sets. Romanian Journal of Information Science and Technology 21(4):344–358Google Scholar
  65. 65.
    Jain R, Jain N, Kapania S, Son LH (2018) Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction. Symmetry-Basel 10(12):768–791Google Scholar
  66. 66.
    Jude H, Anitha J, Naaji A, Geman O, Popescu D, Son LH (2018) A Modified Deep Convolutional Neural Network for Abnormal Brain Image Classification. IEEE Access 7(1):4275–4283Google Scholar
  67. 67.
    Son LH, Thong PH (2017) Soft computing methods for WiMax Network Planning on 3D Geographical Information Systems. J Comput Syst Sci 83(1):159–179MathSciNetzbMATHGoogle Scholar
  68. 68.
    Phong PH, Son LH (2017) Linguistic Vector Similarity Measures and Applications to Linguistic Information Classification. Int J Intell Syst 32(1):67–81Google Scholar
  69. 69.
    Son LH, Thong PH (2017) Some Novel Hybrid Forecast Methods Based On Picture Fuzzy Clustering for Weather Nowcasting from Satellite Image Sequences. Appl Intell 46(1):1–15Google Scholar
  70. 70.
    Son LH, Tuan TM (2017) Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints. Eng Appl Artif Intell 59:186–195Google Scholar
  71. 71.
    Hai DT, Son LH, Vinh LT (2017) Novel Fuzzy Clustering Scheme for 3D Wireless Sensor Networks. Appl Soft Comput 54:141–149Google Scholar
  72. 72.
    Son LH, Van Viet P, Van Hai P (2017) Picture Inference System: A New Fuzzy Inference System on Picture Fuzzy Set. Appl Intell 46(3):652–669Google Scholar
  73. 73.
    Son LH (2017) Measuring Analogousness in Picture Fuzzy Sets: From Picture Distance Measures to Picture Association Measures. Fuzzy Optim Decis Making 16(3):359–378MathSciNetzbMATHGoogle Scholar
  74. 74.
    Thanh ND, Ali M, Son LH (2017) A Novel Clustering Algorithm in a Neutrosophic Recommender System for Medical Diagnosis. Cogn Comput 9(4):526–544Google Scholar
  75. 75.
    Son LH, Tien ND (2017) Tune up Fuzzy C-Means for Big Data: Some novel hybrid clustering algorithms based on Initial Selection and Incremental Clustering. International Journal of Fuzzy Systems 19(5):1585–1602MathSciNetGoogle Scholar
  76. 76.
    Ali M, Son LH, Deli I, Tien ND (2017) Bipolar Neutrosophic Soft Sets and Applications in Decision Making. J Intell Fuzzy Syst 33:4077–4087Google Scholar
  77. 77.
    Son LH, Tuan TM (2016) A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl 46:380–393Google Scholar
  78. 78.
    Son LH (2016) Dealing with the New User Cold-Start Problem in Recommender Systems: A Comparative Review. Inf Syst 58:87–104Google Scholar
  79. 79.
    Wijayanto AW, Purwarianti A, Son LH (2016) Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population. Appl Intell 44(2):377–398Google Scholar
  80. 80.
    Thong PH, Son LH (2016) Picture Fuzzy Clustering: A New Computational Intelligence Method. Soft Comput 20(9):3549–3562zbMATHGoogle Scholar
  81. 81.
    Son LH, Van Hai P (2016) A novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. International Journal of Fuzzy Systems 18(5):894–903MathSciNetGoogle Scholar
  82. 82.
    Tuan TM, Ngan TT, Son LH (2016) A Novel Semi-Supervised Fuzzy Clustering Method based on Interactive Fuzzy Satisficing for Dental X-Ray Image Segmentation. Appl Intell 45(2):402–428Google Scholar
  83. 83.
    Son LH, Louati A (2016) Modeling Municipal Solid Waste Collection: A Generalized Vehicle Routing Model with Multiple Transfer Stations, Gather Sites and Inhomogeneous Vehicles in Time Windows. Waste Manag 52:34–49Google Scholar
  84. 84.
    Son LH, Phong PH (2016) On the performance evaluation of intuitionistic vector similarity measures for medical diagnosis. J Intell Fuzzy Syst 31:1597–1608zbMATHGoogle Scholar
  85. 85.
    Son LH (2016) Generalized Picture Distance Measure and Applications to Picture Fuzzy Clustering. Appl Soft Comput 46:284–295Google Scholar
  86. 86.
    Thong PH, Son LH (2016) A Novel Automatic Picture Fuzzy Clustering Method Based On Particle Swarm Optimization and Picture Composite Cardinality. Knowl-Based Syst 109:48–60Google Scholar
  87. 87.
    Thong PH, Son LH (2016) Picture Fuzzy Clustering for Complex Data. Eng Appl Artif Intell 56:121–130Google Scholar
  88. 88.
    Ngan TT, Tuan TM, Son LH, Minh NH, Dey N (2016) Decision making based on fuzzy aggregation operators for medical diagnosis from dental X-ray images. J Med Syst 40(12):1–7Google Scholar
  89. 89.
    Son LH (2015) DPFCM: A Novel Distributed Picture Fuzzy Clustering Method on Picture Fuzzy Sets. Expert Syst Appl 42(1):51–66Google Scholar
  90. 90.
    Son LH, Thong NT (2015) Intuitionistic Fuzzy Recommender Systems: An Effective Tool for Medical Diagnosis. Knowl-Based Syst 74:133–150Google Scholar
  91. 91.
    Thong NT, Son LH (2015) HIFCF: An Effective Hybrid Model between Picture Fuzzy Clustering and Intuitionistic Fuzzy Recommender Systems for Medical Diagnosis. Expert Syst Appl 42(7):3682–3701Google Scholar
  92. 92.
    Son LH (2015) HU-FCF++: A Novel Hybrid Method for the New User Cold-Start Problem in Recommender Systems. Eng Appl Artif Intell 41:207–222Google Scholar
  93. 93.
    Son LH (2015) A Novel Kernel Fuzzy Clustering Algorithm for Geo-Demographic Analysis. Inf Sci 317:202–223Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSCAD College of Engineering and TechnologyTirunelveliIndia
  2. 2.Department of Computer Science and EngineeringAnna University Regional CampusTirunelveliIndia
  3. 3.CSE DepartmentLNCT Group of CollegeJabalpurIndia
  4. 4.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam

Personalised recommendations