Abstract
Routing protocols are responsible for discovering and maintaining energy-efficient routes in wireless sensor networks (WSNs) to make reliable and efficient communication. The main aim of the routing protocol design is collecting data of the sensor field efficiently. In general, routing in WSNs can be classified into three groups: flat routing, hierarchical routing, and location routing. According to the literature, hierarchical routing has more advantages compared to other types, for example, hierarchical routing reduces the redundant data transmission and balances the load among the sensor nodes in an efficient way. Recently, many intelligent-based hierarchical routing protocols are developed for controlling the consumption power of WSNs. Selecting an appropriate routing protocol for specific applications is an important and difficult task for the designer of WSNs. Therefore, this chapter presents a comprehensive survey of the recently intelligent-based hierarchical routing protocols that are developed based on Particle Swarm Optimization, Ant Colony Optimization, Fuzzy Logic, Genetic Algorithm, and Artificial Immune Algorithm. These protocols will review in detail according to different metrics such as WSN type, node deployment, control manner, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated here depending on delay, network size, energy efficiency, and scalability with mentioning the advantages and drawbacks of each protocol.
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References
Rahman MA, Anwar S, Pramanik MI, Rahman MF (2013) A survey on energy efficient routing techniques in wireless sensor network. In: 2013 15th international conference on advanced communication technology (ICACT), pp 200–205
Yu J, Qi Y, Wang G, Gu X (2012) A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU—Int J Electron Commun 66:54–61
Sabet M, Naji HR (2015) A decentralized energy efficient hierarchical cluster-based routing algorithm for wireless sensor networks. AEU—Int J Electron Commun 69:790–799
Do sung K, Hyun soo C, Seungwha Y (2012) Improve far-zone LEACH protocol for energy conserving. In: 2012 8th international conference on wireless communications, networking and mobile computing (WiCOM), Shanghai, pp 1–4
Hui L, Xiaoguang Z, Lijun L (2013) A hybrid deployment algorithm based on clonal selection and artificial physics optimization for WSN. Inf Technol 12:917–925
Wang J, Cao Y-Q, Li B, Lee S-Y, Kim J-U (2015) A glowworm swarm optimization based clustering algorithm with mobile sink support for wireless sensor networks. J Internet Technol 16:825–832
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc.
Kumar G, Singh J (2013) Energy efficient clustering scheme based on grid optimization using genetic algorithm for wireless sensor networks. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, pp 1–5
Sabor N, Ahmed SM, Abo-Zahhad M, Sasaki S (2018) ARBIC: an adjustable range based immune hierarchy clustering protocol supporting mobility of wireless sensor networks. Pervasive Mob Comput 43:27–48
Das SK, Tripathi S (2019) Energy-efficient routing formation algorithm for hybrid ad-hoc network: a geometric programming approach. Peer-to-Peer Netw Appl 12:102–128
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:e3340
Xiaobing W, Guihai C, Das SK (2006) On the energy hole problem of nonuniform node distribution in wireless sensor networks. In: 2006 IEEE international conference on mobile adhoc and sensor systems (MASS), Vancouver, BC, pp 180–187
Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mob Comput 2:483–502
Pushpa RA, Vallimayil A, Dhulipala VRS (2011) Impact of mobility models on mobile sensor networks. In: 3rd international conference on electronics computer technology (ICECT), Kanyakumari, pp 102–106
Jayakumar G, Ganapathi G (2008) Reference point group mobility and random waypoint models in performance evaluation of MANET routing protocols. J Comput Syst Netw Commun 2008
Abdollahzadeh S, Navimipour NJ (2016) Deployment strategies in the wireless sensor network: a comprehensive review. Comput Commun 91–92:1–16
Liu X (2012) A survey on clustering routing protocols in wireless sensor networks. Sensors (Basel) 12:11113–11153
Gupta SK, Jain N, Sinha P (2013) Clustering protocols in wireless sensor networks: a survey. Int J Appl Inf Syst 5:41–50
Abo-Zahhad M, Ahmed SM, Sabor N, Sasaki S (2014) A new energy-efficient adaptive clustering protocol based on genetic algorithm for improving the lifetime and the stable period of wireless sensor networks. Int J Energy Inf Commun 5:47–72
Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1:660–670
Do-Seong K, Yeong-Jee C (2006) Self-organization routing protocol supporting mobile nodes for wireless sensor network. In: First international multi-symposiums on computer and computational sciences, 2006, IMSCCS’06, Hanzhou, Zhejiang, pp 622–626
Awwad SAB, Ng CK, Noordin NK, Rasid MFA (2011) Cluster-based routing protocol for mobile nodes in wireless sensor network. Wireless Pers Commun 61:251–281
Cakici S, Erturk I, Atmaca S, Karahan A (2014) A novel cross-layer routing protocol for increasing packet transfer reliability in mobile sensor networks. Wireless Pers Commun 77:2235–2254
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948
Yarushkina NG (2002) Genetic algorithms for engineering optimization: theory and practice. In: 2002 IEEE international conference on artificial intelligence systems, 2002 (ICAIS 2002), pp 357–362
Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13
Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2:353–373
Zhang Z (2007) Immune optimization algorithm for constrained nonlinear multiobjective optimization problems. Appl Soft Comput 7:840–857
Jiang C-J, Shi W-R, Xiang M, Tang X-L (2010) Energy-balanced unequal clustering protocol for wireless sensor networks. J China Univ Posts Telecommun 17:94–99
Zhang R, Jia Z, Li X, Han D (2011) Double cluster-heads clustering algorithm for wireless sensor networks using PSO. In: 2011 6th IEEE conference on industrial electronics and applications (ICIEA), Beijing, pp 763–766
Singh B, Lobiyal DK (2012) Energy-aware cluster head selection using particle swarm optimization and analysis of packet retransmissions in WSN. Procedia Technol 4:171–176
Singh B, Lobiyal D (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Hum-Centric Comput Inf Sci 2:1–18
Xia L, Wang G, Liu Z, Zhang Y (2013) An energy-efficient routing protocol based on particle swarm clustering algorithm and inter-cluster routing algorithm for WSN. In: 2013 25th Chinese control and decision conference (CCDC), Guiyang, pp 4029–4033
Elhabyan RS, Yagoub MCE (2014) Particle swarm optimization protocol for clustering in wireless sensor networks: a realistic approach. In: 2014 IEEE 15th international conference on information reuse and integration (IRI), Redwood City, CA, pp 345–350
Elhabyan RS, Yagoub MCE (2014) PSO-HC: particle swarm optimization protocol for hierarchical clustering in wireless sensor networks. In: 2014 international conference on collaborative computing: networking, applications and worksharing (CollaborateCom), Miami, FL, pp 417–424
RejinaParvin J, Vasanthanayaki C (2015) Particle swarm optimization-based clustering by preventing residual nodes in wireless sensor networks. IEEE Sens J 15:4264–4274
Elhabyan RSY, Yagoub MCE (2015) Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J Netw Comput Appl 52:116–128
Latiff NAA, Latiff NMA, Ahmad RB (2011) Prolonging lifetime of wireless sensor networks with mobile base station using particle swarm optimization. In: 2011 4th international conference on modeling, simulation and applied optimization (ICMSAO), Kuala Lumpur, pp 1–6
Deepa O, Suguna J (2017) An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks. J King Saud Univ—Comput Inf Sci
Tabibi S, Ghaffari A (2019) Energy-efficient routing mechanism for mobile sink in wireless sensor networks using particle swarm optimization algorithm. Wirel Pers Commun 104:199–216
Bayraklı S, Erdogan SZ (2012) Genetic algorithm based energy efficient clusters (GABEEC) in wireless sensor networks. Procedia Comput Sci 10:247–254
Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56
Elhoseny M, Yuan X, Yu Z, Mao C, El-Minir H, Riad A (2014) Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Commun Lett 19:2194–2197
Gupta S, Jana P (2015) Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Pers Commun 83:2403–2423
Baranidharan B, Santhi B (2015) GAECH: genetic algorithm based energy efficient clustering hierarchy in wireless sensor networks. J Sens 2015:8
Sarangi S, Kar S (2011) Genetic algorithm based mobility aware clustering for energy efficient routing in wireless sensor networks. In: 2011 17th IEEE international conference on networks, Singapore, pp 1–6
Attea BA, Khalil EA, Cosar A (2015) Multi-objective evolutionary routing protocol for efficient coverage in mobile sensor networks. Soft Comput 19:2983–2995
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
Rady A, Sabor N, Shokair M, El-Rabaie EM (2018) Mobility based genetic algorithm hierarchical routing protocol in mobile wireless sensor networks. In: 2018 international Japan-Africa conference on electronics, communications and computations (JAC-ECC), pp 83–86
Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13:1741–1749
Mirsadeghi M, Mahani A, Shojaee M (2014) A novel distributed clustering protocol using fuzzy logic. Procedia Technol 17:742–748
Logambigai R, Kannan A (2015) Fuzzy logic based unequal clustering for wireless sensor networks. Wireless Netw 1–13
Srivastava JR, Sudarshan TSB (2015) A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP). Appl Soft Comput 37:863–886
Tamandani YK, Bokhari MU (2016) SEPFL routing protocol based on fuzzy logic control to extend the lifetime and throughput of the wireless sensor network. Wireless Netw 22:647–653
Arjunan S, Sujatha P (2018) Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Appl Intell 48:2229–2246
Mazinani A, Mazinani SM, Mirzaie M (2019) FMCR-CT: an energy-efficient fuzzy multi cluster-based routing with a constant threshold in wireless sensor network. Alex Eng J 58:127–141
Ghosh S, Mondal S, Biswas U (2016) Fuzzy C means based hierarchical routing protocol in WSN with ant colony optimization. In: 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT), pp 348–354
Mondal S, Ghosh S, Biswas U (2016) ACOHC: ant colony optimization based hierarchical clustering in wireless sensor network. In: 2016 international conference on emerging technological trends (ICETT), pp 1–7
Rhim H, Tamine K, Abassi R, Sauveron D, Guemara S (2018) A multi-hop graph-based approach for an energy-efficient routing protocol in wireless sensor networks. Hum-Centric Comput Inf Sci 8:1–21
Jingyi W, Yuhao J, Xiaotong Z, Hongying B (2016) Clustering protocol based on immune optimization algorithms for wireless sensor networks. In: 2016 2nd IEEE international conference on computer and communications (ICCC), pp 2272–2276
Abo-Zahhad M, Ahmed SM, Sabor N, Sasaki S (2015) Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sens J 15:4576–4586
Sabor N, Abo-Zahhad M, Sasaki S, Ahmed SM (2016) An unequal multi-hop balanced immune clustering protocol for wireless sensor networks. Appl Soft Comput (in Progress)
Smaragdakis G, Matta I, Bestavros A (2004) SEP: a stable election protocol for clustered heterogeneous wireless sensor networks. In: Second international workshop on sensor and actor network protocols and applications (SANPA 2004), pp 1–11
Mahapatra RP, Yadav RK (2015) Descendant of LEACH based routing protocols in wireless sensor networks. Procedia Comput Sci 57:1005–1014
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Sabor, N., Abo-Zahhad, M. (2020). A Comprehensive Survey of Intelligent-Based Hierarchical Routing Protocols for Wireless Sensor Networks. 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_10
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DOI: https://doi.org/10.1007/978-981-15-2125-6_10
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