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An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks

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Abstract

Along with the development of the technologies of various kinds, wireless sensor networks are gradually entering into the practicalization phase. Therefore, whether its performance is good or not will directly affect the use of WSNs. Routing is one of the most important technologies. In addition, the routing performance is a significant part of the evaluation index system of wireless sensor networks. Energy efficiency is focused on in the traditional routing protocols, while quality of service (QoS) (i.e., delay, reliability, robustness) becomes important in practical application. Routing protocols should not only ensure the energy efficiency, but also realize QoS performances. As a consequence how to achieve the equilibrium between energy efficiency and other performances is the issue to be solved. In this paper, some performance trade-off mechanisms and methods that have existed in the routing protocols of wireless sensor networks are analyzed and summarized. At the same time, the related technical features of the routing protocols are mentioned, the purpose of which is to provide a better understanding of the current research issues within the field of performance trade-off mechanisms and to guide the researcher to develop the routing protocols with better performance in order to fulfill the actual requirements.

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References

  1. Zheng, Y., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for internet of things. Journal of Network and Computer Applications, 42, 120–134.

    Article  Google Scholar 

  2. Li, M., & Vasilakos, A. V. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  3. Culler, D., Estrin, D., & Srivastava, M. (2004). Overview of sensor networks. IEEE Computer Magazine, 37(8), 41–49.

    Article  Google Scholar 

  4. Anastasi, G., Conti, M., Francesco, M. D., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568.

    Article  Google Scholar 

  5. Wei, G. Y., Ling, Y., Guo, B. F., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  6. Zeng, Y. Y., Li, D. S., & Vasilakos, A. V. (2013). Real-time data report and task execution in wireless sensor and actuator networks using self-aware mobile actuators. Computer Communications, 36(9), 988–997.

    Article  Google Scholar 

  7. Vasilakos, A., Zhang, Y., & Spyropoulos, T. V. (2012). Delay tolerant networks protocols and applications. Baco Raton, FL: CRC Press.

    Google Scholar 

  8. Liu, X. Y., Zhu, Y. M., Kong, L. H., Liu, C., Gu, Y., Vasilakos, A. V., & Wu, M. Y. (2014). CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems (in press). doi:10.1109/TPDS.2014.2345257.

  9. Xiang, L., Luo, J., & Vasilakos, A. V. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks, SECON 2011 (pp. 46–54).

  10. Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors. Article ID 134165.

  11. Santi, P. (2005). Topology control in wireless ad hoc and sensor networks. ACM Computing Surveys, 37(2), 164–194.

    Article  Google Scholar 

  12. Zeng, Y. Y., Xiang, K., Li, D. S., & Vasilakos, A. V. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  13. Chehri, A., & Mouftah, H. T. (2012). QoS aware green routing protocol for wireless sensor networks. In 2012 25th IEEE Canadian conference on electrical and computer engineering (CCECE), Montreal, QC, Canada, Apr 29–May 2. doi:10.1109/CCECE.2012.6335028.

  14. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  15. Al-Karaki, J. N., & Kamal, A. E. (2004). A taxonomy of routing techniques in wireless sensor networks. In M. Ilyas & I. Mahgoub (Eds.), Handbook of sensor networks: Compact wireless and wired sensing systems (pp. 116–139). Baco Raton, FL: CRC Press.

    Google Scholar 

  16. Chen, Y., Zhang, S. Q., Xu, S. G., & Li, G. Y. (2011). Fundamental trade-offs on green wireless networks. IEEE Communications Magazine, 49(6), 30–37.

    Article  Google Scholar 

  17. He, G. N., Zhang, S. Q., Chen, Y., & Xu, S. G. (2012). Fundamental tradeoffs and evaluation methodology for future green wireless networks. In 2012 1st IEEE international conference on communications in China workshops, ICCC 2012 (pp. 74–78). doi:10.1109/ICCCW.2012.6316478.

  18. Wang, X. F., Vasilakos, A. V., Chen, M., Liu, Y. H., & Kwon, T. T. (2012). A survey of green mobile networks: Opportunities and challenges. Mobile Networks and Applications, 17(1), 4–20.

    Article  Google Scholar 

  19. Incebacak, D., Bicakci, K., & Tavli, B. (2010). Investigating the tradeoffs between spatial granularity and energy requirements in wireless sensor networks. In UKSim fourth European modelling symposium on computer modelling and simulation, EMS 2010 (pp. 414–419). doi:10.1109/EMS.2010.75.

  20. Xu, X., Ansari, R., Khokhar, A., & Vasilakos, A. V. (2015). Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3). Article 45.

  21. Khan, M. A., Tembine, H., & Vasilakos, A. V. (2012). Game dynamics and cost of learning in heterogeneous 4G networks. IEEE Journal on Selected Areas in Communications, 30(1), 198–213.

    Article  Google Scholar 

  22. Busch, C., Kannan, R., & Vasilakos, A. V. (2012). Approximating congestion + dilation in networks via quality of routing games. IEEE Transaction on Computers, 61(9), 1270–1283.

    Article  MathSciNet  Google Scholar 

  23. Youssef, M., Ibrahim, M., Abdelatif, M., Chen, L., & Vasilakos, A. V. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  Google Scholar 

  24. Han, K., Luo, J., Liu, Y., & Vasilakos, A. V. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

    Article  Google Scholar 

  25. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey. IEEE Wireless Communications, 11(6), 6–27.

    Article  Google Scholar 

  26. Yao, Y. J., Cao, Q., & Vasilakos, A. V. (2014). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking. doi:10.1109/TNET.2014.23065920.

  27. Bouabdallah, F., Bouabdallah, N., & Boutaba, R. (2008). Analysis of the latency-lifetime tradeoff in wireless sensor networks. In AICCSA 08–6th IEEE/ACS international conference on computer systems and applications (pp. 1076–1081). doi:10.1109/AICCSA.2008.4493681.

  28. Rahman, M. A., Anwar, S., Pramanik, M. I., & Rahman, M. F. (2013). A survey on energy efficient routing techniques in wireless sensor network. In 15th international conference on advanced communication technology: Smart services with internet of things, ICACT 2013, PyeongChang, Republic of Korea (pp. 200–205).

  29. Sheng, Z. G., Yang, S. S., Yu, Y. F., Vasilakos, A. V., McCann, J. A., & Leung, K. K. (2013). A survey on the IETF protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98.

    Article  Google Scholar 

  30. Kumar, V., Jain, S., & Tiwari, S. (2011). Energy efficient clustering algorithms in wireless sensor networks: A survey. International Journal of Computer Science Issues, 8(5), 259–268.

    Google Scholar 

  31. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (Vol. 2, No. 10, pp. 3005–3014).

  32. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of the 4th IEEE conference on mobile and wireless communications networks (pp. 368–372).

  33. Younis, O., & Fahmy, S. (2004). Heed: A hybrid, energy-efficient, distributed clustering approach for ad-hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 660–669.

    Article  Google Scholar 

  34. Tong, M. (2010). LEACH-B: An improved LEACH protocol for wireless sensor network. In 6th international conference on wireless communications networking and mobile computing (WiCOM), Shanghai, China. doi:10.1109/WICOM.2010.5601113.

  35. Fan, X. N., & Song, Y. L. (2007). Improvement on LEACH protocol of wireless sensor network. In 2007 international conference on sensor technologies and applications, Sensor Communications (pp. 260–264). doi:10.1109/SENSORCOMM.2007.4394931.

  36. Naeimi, S., Ghafghazi, H., Chow, C. O., & Ishii, H. (2012). A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors, 12(6), 7350–7409.

    Article  Google Scholar 

  37. Gao, T., Jin, R. C., Song, J. Y., Xu, T. B., & Wang, L. D. (2012). Energy-efficient cluster head selection scheme based on multiple criteria decision making for wireless sensor networks. Wireless Personal Communications, 63(4), 871–894.

    Article  Google Scholar 

  38. Vasilakos, A., Ricudis, C., Anagnostakis, K., Pedrycz, W., & Pitsillides, A. (1998). Evolutionary-fuzzy prediction for strategic QoS routing in broadband networks. In The 1998 IEEE international conference on fuzzy systems proceedings (Vol. 2, pp. 1488–1493).

  39. Yang, J., Lin, Y., Xiong, W. L., & Xu, B. G. (2009). Ant colony-based multi-path routing algorithm for wireless sensor networks. In 2009 international workshop on intelligent systems and applications, ISA 2009, Wuhan, China, May 23–24. doi:10.1109/IWISA.2009.5072737.

  40. Xia, S. Z., Wu, S., & Ni, J. (2010). A new energy-efficient routing algorithm based on ant colony system for wireless sensor networks. In 4th international conference on internet computing for science and engineering, ICICSE 2009 (pp. 176–180). doi:10.1109/ICICSE.2009.27.

  41. Ren, X. L., & Wang, Y. (2008). Multipath routing based on ant colony system in wireless sensor networks. In International conference on computer science and software engineering, CSSE 2008 (Vol. 3, pp. 202–205). doi:10.1109/CSSE.2008.1140.

  42. Sha, K. W., Gehlot, J., & Greve, R. (2013). Multipath routing techniques in wireless sensor networks: A survey. Wireless Personal Communications, 70(2), 807–829.

    Article  Google Scholar 

  43. Muni Venkateswarlu, K., Chandra Sekaran, K., & Kandasamy, A. (2011). Node-link disjoint multipath routing protocols for wireless sensor networks—A survey and conceptual modeling. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 7135, pp. 405–414).

  44. Agrakhed, J., Biradar, G. S., & Mytri, V. D. (2012). A new QoS adaptive multipath routing in wireless multimedia sensor network. In 2012 fourth international conference on computational intelligence and communication networks, CICN 2012 (pp. 69–73). doi:10.1109/CICN.2012.23.

  45. Agrakhed, J., Biradar, G. S., & Mytri, V. D. (2012). Adaptive multi constraint multipath routing protocol in wireless multimedia sensor network. In 2012 international conference on computing sciences, ICCS 2012 (pp. 326–331). doi:10.1109/ICCS.2012.9.

  46. Houngbadji, T., & Pierre, S. (2010). QoSNET: An integrated QoS network for routing protocols in large scale wireless sensor networks. Computer Communications, 33(11), 1334–1342.

    Article  Google Scholar 

  47. Yen, Y. S., Chao, H. C., Chang, R. S., & Vasilakos, A. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11–12), 2238–2250.

    Article  Google Scholar 

  48. Chatterjee, M., Das, S. K., & Turgut, D. (2002). WCA: A weighted clustering algorithm for mobile ad hoc networks. Journal of Cluster Computing, 5(2), 193–204.

    Article  Google Scholar 

  49. Choi, W. C., & Woo, M. (2006). A distributed weighted clustering algorithm for mobile ad hoc networks. In Advanced international conference on telecommunications and international conference on internet and web applications and services (AICT-ICIW06) (pp. 73–78). doi:10.1109/AICT-ICIW.2006.11.

  50. Huang, H. Q., Yao, D. Y., Shen, J., Ma, K., & Liu, H. T. (2008). A multi-weight based clustering algorithm for wireless sensor networks. Journal of Electronics and Information Technology, 30(6), 1489–1492.

    Article  Google Scholar 

  51. Sohrabi, K., & Pottie, J. (2000). Protocols for self-organization of a wirless sensor network. IEEE Personal Communications, 7(5), 16–27.

    Article  Google Scholar 

  52. Kim, M., Jeong, E., Bang, Y. C., Hwang, S., & Kim, B. (2008). Multipath energy-aware routing protocol in wireless sensor networks. In 5th international conference on networked sensing systems, INSS 2008 (pp. 127–130). doi:10.1109/INSS.2008.4610913.

  53. Kim, S. (2012). Adaptive online sensor clustering and routing algorithms for QoS provisioning and energy efficiency. Wireless Personal Communications, 63(4), 965–975.

    Article  Google Scholar 

  54. Li, Y. Q., Li, L. Y., & Wang, C. Y. (2008). A multipath routing algorithm based on link multi-metrics for wireless sensor networks. In ISECS international colloquium on computing, communication, control, and management, CCCM 2008 (Vol. 2, pp. 567–571). doi:10.1109/CCCM.2008.206.

  55. Yahya, B., & Ben-Othman, J. (2009). An energy efficient and QoS aware multipath routing protocol for wireless sensor networks. In 2009 IEEE 34th conference on local computer networks (LCN) (pp. 93–100). doi:10.1109/LCN.2009.5355184.

  56. Heikalabad, S. R., Rasouli, H., Nematy, F., & Rahmani, N. (2011). Qempar: Qos and energy aware multi-path routing algorithm for real-time applications in wireless sensor networks. International Journal of Computer Science Issues, 8(1), 466–471.

    Google Scholar 

  57. Wan, X. X., & Wu, G. M. (2012). Multipath routing algorithm with heterogeneous sensor network for beach environment monitoring system. In 2012 international conference on computer science and service system, CSSS 2012 (pp. 918–921). doi:10.1109/CSSS.2012.233.

  58. Wang, Z. J., Bulut, E., & Szymanski, B. K. (2009). Energy efficient collision aware multipath routing for wireless sensor networks. In IEEE 2009 IEEE international conference on communications, ICC 2009 (Vol. 1–8, pp. 91–95). doi:10.1109/ICC.2009.5198989.

  59. Ben-Othman, J., & Yahya, B. (2010). Energy efficient and QoS based routing protocol for wireless sensor networks. Journal of Parallel and Distributed Computing, 70(8), 849–857.

    Article  MATH  Google Scholar 

  60. Alwan, H., & Agarwal, A. (2013). Multi-objective QoS routing for wireless sensor networks. In 2013 international conference on computing, networking and communications, ICNC 2013 (pp. 1074–1079). doi:10.1109/ICCNC.2013.6504241.

  61. Valentini, G., Abbas, C. J. B., Villalba, L. J. G., & Astorga, L. (2010). Dynamic multi-objective routing algorithm: A multi-objective routing algorithm for the simple hybrid routing protocol on wireless sensor networks. IET Communications, 4(14), 1732–1741.

    Article  Google Scholar 

  62. Banerjee, D., De, T., & Choudhury, P. (2011). Best route selection for energy efficient multipath routing in ad hoc sensor network. In 2011 3rd international conference on electronics computer technology, ICECT 2011 (Vol. 1, pp. 345–349). doi:10.1109/ICECTECH.2011.5941620.

  63. Yang, J., Xu, M., & Xu, B. G. (2009). A multipath routing algorithm based on parametric probability for wireless sensor networks. In 2009 2nd international conference on intelligent computing technology and automation, ICICTA 2009 (Vol. 2, pp. 417–420). doi:10.1109/ICICTA.2009.336.

  64. Masoudi, S., Rahmani, A. M., Eghbali, A. N., & Khademzadeh, A. (2008). GMPR: A Greedy multi-path routing algorithm for wireless sensor networks. In Proceedings of the 2008 2nd international conference on future generation communication and networking, FGCN 2008 (Vol. 1, pp. 25–30). doi:10.1109/FGCN.2008.217.

  65. Ortiz, A. M., Royo, F., Olivares, T., Castillo, J. C., Orozco-Barbosa, L., & Marron, P. J. (2013). Fuzzy-logic based routing for dense wireless sensor networks. Telecommunication System, 52(4), 2687–2697.

    Article  Google Scholar 

  66. Ahvar, E., Ortiz, A. M., & Crespi, N. (2013). Improving decision-making for fuzzy logic-based routing in wireless sensor networks. In Ubiquitous intelligence and computing, 2013 IEEE 10th international conference on and 10th international conference on autonomic and trusted computing (UIC/ATC) (pp. 583–588). doi:10.1109/UIC-ATC.2013.87.

  67. Misra, S., Roy, S., Obaidat, M. S., & Mohanta, D. (2009). A fuzzy logic-based energy efficient packet loss preventive routing protocol. In 2009 international symposium on performance evaluation of computer and telecommunication systems (SPECTS’09) (pp. 185–192).

  68. Minhas, M. R., Gopalakrishnan, S., & Leung, V. C. M. (2009). Multiobjective routing for simultaneously optimizing system lifetime and source-to-sink delay inwireless sensor networks. In 2009 29th IEEE international conference on distributed computing systems workshops (pp. 123–129).

  69. Barolli, L., Wang, Q., Kulla, E., Kamo, B., Xhafa, F., & Younas, M. (2012). A fuzzy-based simulation system for cluster-head selection and sensor speed control in wireless sensor networks. In 2012 third international conference on emerging intelligent data and web technologies (pp. 16–22). doi:10.1109/EIDWT.2012.14.

  70. Li, G. X., Wang, L. C., & Li, H. Z. (2008). Multiple-objective fuzzy decision making based routing protocol for wireless sensor networks. In 2008 IEEE international conference on networking, sensing and control, ICNSC 2008 (pp. 1273–1278). doi:10.1109/ICNSC.2008.4525413.

  71. Sakthidevi, I., & Srievidhyajanani, E. (2013). Secured fuzzy based routing framework for dynamic wireless sensor networks. In Proceedings of IEEE international conference on circuit, power and computing technologies, ICCPCT 2013 (pp. 1041–1046). doi:10.1109/ICCPCT.2013.6529032.

  72. Annoa, J., Barollib, L., Durresic, A., Xhafad, F., & Koyamae, A. (2008). Performance evaluation of two fuzzy-based cluster head selection systems for wireless sensor networks. Mobile Information Systems, 4, 297–312.

    Article  Google Scholar 

  73. Annoa, J., Barolli, L., Xhafa, F., & Durresi, A. (2007). A cluster head selection method for wireless sensor networks based on fuzzy logic. In IEEE region 10 annual international conference, TENCON 2007 (Vol. 1–3, pp. 833–836). doi:10.1109/TENCON.2007.4428982.

  74. Yin, Y. Y., Shi, J. W., Li, Y. N., & Zhang, P. (2006). Cluster head selection using analytical hierarchy process for wireless sensor networks. In IEEE international symposium on personal, indoor and mobile radio communications, PIMRC 2006 (Vol. 1–5, pp. 11–14). doi:10.1109/PIMRC.2006.254181.

  75. Wang, M., & Li, S. N. (2010). An energy-efficient load-balanceable multipath routing algorithm based on AHP for wireless sensor networks. In 2010 IEEE international conference on intelligent computing and intelligent systems, ICIS 2010 (Vol. 2, pp. 251–256). doi:10.1109/ICICISYS.2010.5658758.

  76. Gao, T., Jin, R. C., Qin, J. Y., & Wang, L. D. (2010). A novel node-disjoint multipath routing protocol for wireless multimedia sensor networks. In 2010 the 2nd international conference on signal processing systems, ICSPS 2010 (Vol. 1, pp. 790–794). doi:10.1109/ICSPS.2010.5555252.

  77. Gao, W. Q., & Kang, F. J. (2012). Scheduling algorithm of wireless sensor cluster head based on multi-dimensional QoS. Knowledge Discovery and Data Mining, 135, 709–716.

    Article  Google Scholar 

  78. Bucur, D., Iacca, G., Squillero, G., & Tonda, A. (2014). The tradeoffs between data delivery ratio and energy costs in wireless sensor networks: A multi-objective evolutionary framework for protocol analysis. In 2014 genetic and evolutionary computation conference, GECCO 2014 (pp. 1071–1078). doi:10.1145/2576768.2598384.

  79. Rao, L., Liu, X., Kang, K. D., Liu, W. Y., Liu, L., & Chen, Y. (2011). Optimal joint multi-path routing and sampling rates assignment for real-time wireless sensor networks. In 2011 IEEE international conference on communications, ICC 2011. doi:10.1109/icc.2011.5963245.

  80. Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In 2012 Proceedings IEEE INFOCOM, INFOCOM 2012 (pp. 100–108). doi:10.1109/INFCOM.2012.6195456.

  81. Shah-Mansouri, V., & Wong, V. W. S. (2010). Lifetime-resource tradeoff for multicast traffic in wireless sensor networks. IEEE Transactions on Wireless Communications, 9(6), 1924–1934.

    Article  Google Scholar 

  82. Chou, P. A., Wu, Y. N., & Jain, K. (2003). Practical network coding. In Proceedings of the 41st annual Allerton conference on communication, control and computing.

  83. Li, S. S., Zhu, P. D., Liao, X. K., Cheng, W. F., & Peng, S. L. (2006). Energy efficient multipath routing using network coding in wireless sensor networks. Lecture notes in computer science (Vol. 4104, pp. 114–127).

  84. Guo, Z., Wang, B., & Cui, J. H. (2007). Efficient error recovery using network coding in underwater sensor networks. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 4479, pp. 227–238).

  85. Wang, L., Yang, Y. W., & Zhao, W. (2012). Network coding-based multipath routing for energy efficiency in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking. Article 115.

  86. Yang, Y. W., Zhong, C. S., Sun, Y. M., & Yang, J. Y. (2012). Energy efficient reliable multi-path routing using network coding for sensor network. International Journal of Computer Science and Network Security, 8(12), 329–338.

    Google Scholar 

  87. Ghaffari, A., & Babazadeh, S. (2013). Multi-path routing based on network coding in wireless sensor networks. World Applied Sciences Journal, 21(11), 1657–1663.

    Google Scholar 

  88. Wei, M., Wu, C., Huang, L. Q., Wang, F., & Zhu, Y. G. (2012). Network coding based energy-efficient multi-path routing for wireless sensor network. In 10th international conference on advances in mobile computing and multimedia, MoMM 2012 (pp. 240–244). doi:10.1145/2428955.2428999.

  89. Deb, B., Bhatnagar, S., & Nath, B. (2003). ReInForM: Reliable information forwarding using multiple paths in sensor networks. In 28th IEEE international conference on local computer networks (LCN) (pp. 406–415).

  90. Li, P., Guo, S., Yu, S., & Vasilakos, A. V. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.

    Article  Google Scholar 

  91. Hou, I. H., Tsai, Y. E., Abdelzaher, T., & Gupta, I. (2008). AdapCode: Adaptive network coding for code updates in wireless sensor networks. In 27th IEEE communications society conference on computer communications, INFOCOM 2008 (Vol. 1–5, pp. 2189–2197). doi:10.1109/INFOCOM.2007.211.

  92. Yang, Z. Y., Li, M., & Lou, W. J. (2011). R-Code: Network coding-based reliable broadcast in wireless mesh networks. Ad Hoc Networks, 9(5), 788–798.

    Article  Google Scholar 

  93. Vidhyapriya, R., & VanathiEnergy, P. T. (2007). Efficient adaptive multipath routing for wireless sensor networks. IAENG International Journal of Computer Science, 34(1), 2007.

    Google Scholar 

  94. Akkaya, K., & Younis, M. (2005). Energy and QoS aware routing in wireless sensor networks. Cluster Computing, 8(2–3), 179–188.

    Article  Google Scholar 

  95. Yao, L., Wen, W. J., & Gao, F. X. (2008). A real-time and energy aware QoS routing protocol for multimedia wireless sensor networks. In Proceedings of the world congress on intelligent control and automation, WCICA 2008 (pp. 3304–3309). doi:10.1109/WCICA.2008.4594494.

  96. Liu, L., Song, Y. N., Zhang, H. Y., Ma, H. D., & Vasilakos, A. V. (2015). Physarum optimization: A biology-inspired algorithm for the steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.

    MathSciNet  Google Scholar 

  97. Saxena, N., Roy, A., & Shin, J. (2007). A multi-objective genetic algorithmic approach for QoS-based energy-efficient sensor routing protocol. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 4773, pp. 523–526).

  98. Dong, W. S., Ke, Z. W., Chen, N.S., & Sun, Q. (2009). QoS routing algorithm for wireless multimedia sensor networks. In 4th international symposium on intelligence computation and applications, ISICA 2009 (Vol. 5821, pp. 517–524). doi:10.1007/978-3-642-04843-2_55.

  99. Cao, T., Wang, Y. H., Xiong, X. M., & Hao, Y. (2013). Cluster-based routing performance optimization constraint of energy, delay and connectivity metrics in wireless sensor network. International Journal on Smart Sensoring and Intellegent Systems, 6(5), 2103–2118.

    Google Scholar 

  100. Ozdemir, S., Attea, B. A., & Khalil, O. A. (2013). Multi-objective clustered-based routing with coverage control in wireless sensor networks. Soft Computing, 17(9), 1573–1584.

    Article  Google Scholar 

  101. Ekbatanifard, G. H., Monsefi, R., Akbarzade, T., Mohammad, R., & Yaghmaee, M. H. (2010). A multi-objective genetic algorithm based approach for energy efficient QoS-routing in two-tiered wireless sensor networks. In IEEE 5th international symposium on wireless pervasive computing 2010, ISWPC 2010 (pp. 80–85). doi:10.1109/ISWPC.2010.5483775.

  102. Carlos, L. G., & Donoso, Y. (2011). A multi-objective routing protocol for a wireless sensor network using a SPEA2 approach. In Recent advances in computers, communications, applied social science and mathematics and proceedings of ICANCM’11, ICDCC’11, IC-ASSSE-DC’11 (pp. 39–44).

  103. Sengupta, S., Das, S., Nasir, Md, Vasilakos, A. V., & Pedrycz, W. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(6), 1093–1102.

    Article  Google Scholar 

  104. Barbancho, J., Len, C., Molina, F. J., & Barbancho, A. (2007). A new QoS routing algorithm based on self-organizing maps for wireless sensor networks. Telecommunication System, 36(1–3), 73–83.

    Article  Google Scholar 

  105. Yao, Y. J., Cao, Q., & Vasilakos, A. V. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In 2013 IEEE 10th international conference on mobile ad-hoc and sensor systems, MASS 2013, Hangzhou, China, October 14–16 (pp. 182–190).

  106. Cao, X., Wang, R. C., Huang, H. P., Sun, L. J., & Xiao, F. (2012). Multi-path routing algorithm for video stream in wireless multimedia sensor networks. Ruan Jian Xue Bao/Journal of Software, 23(1), 108–121.

    Google Scholar 

  107. Zuo, Y., Ling, Z. H., & Yuan, Y. F. (2013). A hybrid multi-path routing algorithm for industrial wireless mesh networks. EURASIP Journal on Wireless Communications and Networking. Article 82.

  108. Kumar, S., Dave, M., & Dahiya, S. (2014). ACO based QoS aware routing for wireless sensor networks with heterogeneous nodes. Emerging Trends in Computing and Communication Lecture Notes in Electrical Engineering, 298(18), 157–168.

    Article  Google Scholar 

  109. Duarte, P. B. F., Fadlullah, Z. M., Vasilakos, A. V., & Kato, N. (2012). On the partially overlapped channel assignment on wireless mesh network backbone: A game theoretic approach. IEEE Journal on Selected Areas in Communications, 30(1), 119–127.

    Article  Google Scholar 

  110. Cai, W. Y., Jin, X. Y., Zhang, Y., Chen, K. S., & Wang, R. (2006). ACO based QoS routing algorithm for wireless sensor networks. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 4159, pp. 419–428).

  111. Peng, S. H., Yang, S. X., Gregori, S., & Tian, F. C. (2008). An adaptive QoS and energy-aware routing algorithm for wireless sensor networks. In 2008 IEEE international conference on information and automation, ICIA 2008 (pp. 578–583). doi:10.1109/ICINFA.2008.4608066.

  112. Yu, X. H., Luo, J. X., & Huang, J. W. (2011). An ant colony optimization-based QoS routing algorithm for wireless multimedia sensor networks. In 2011 IEEE 3rd international conference on communication software and networks, ICCSN 2011 (pp. 37–41). doi:10.1109/ICCSN.2011.6013656.

  113. Shang, F. J., & Wang, Y. (2010). An ant system optimization QoS routing algorithm for wireless sensor networks. In 3rd international workshop on advanced computational intelligence, IWACI 2010 (pp. 339–343). doi:10.1109/IWACI.2010.5585117.

  114. Bennis, I., Zytoune, O., & Aboutajdine, D. (2013). Enhanced AntNet protocol for wireless multimedia sensor networks. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 7853, pp. 316–320).

  115. Cobo, L., Quintero, A., & Pierre, S. (2010). Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics. Computer Networks, 54(17), 2991–3010.

    Article  Google Scholar 

  116. Cheng, H. J., Xiong, N. X., Vasilakos, A. V., Yang, L. T., Chen, G. L., & Zhuang, X. F. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.

    Article  Google Scholar 

  117. Zhang, X. H., & Xu, W. B. (2006). QoS based routing in wireless sensor network with particle swarm optimization. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 408, pp. 602–607).

  118. Liu, M., Xu, S. J., & Sun, S. Y. (2012). An agent-assisted QoS-based routing algorithm for wireless sensor networks. Journal of Network and Computer Applications, 35(1), 29–36.

    Article  Google Scholar 

  119. He, X., Shi, W. R., Wang, X. G., & Deng, Z. F. (2012). A DPSO-based QoS routing algorithm for wireless sensor networks. Transducer and Microsystem Technologies, 31(4), 123–126.

    Google Scholar 

  120. Shafigh, A. S., & Niyati, M. (2011). A heuristic multi criteria routing protocol in wireless sensor networks. In International conference on advances in computing, communication and control (Vol. 125, pp. 306–317). doi:10.1007/978-3-642-18440-6_39.

  121. Villaverde, B. C., Rea, S., & Pesch, D. (2012). InRoutCA QoS aware route selection algorithm for industrial wireless sensor networks. Ad Hoc Networks, 10(3), 458–478.

    Article  Google Scholar 

  122. Ammari, H. M. (2013). On the energy-delay trade-off in geographic forwarding in always-on wireless sensor networks: A multi-objective optimization problem. Computer Networks, 57(9), 1913–1935.

    Article  Google Scholar 

  123. Sengul, C., Miller, M. J., & Gupta, I. (2008). Adaptive probability-based broadcast forwarding in energy-saving sensor networks. ACM Transactions on Sensor Networks, 4(2). Article 6.

  124. Haas, Z. J., Halpern, J. Y., & Li, L. (2013). Gossip-based ad hoc routing. IEEE/ACM Transactions on Networking, 14(3), 479–491.

    Article  Google Scholar 

  125. Ghosh, A., Incel, O. D., Anil Kumar, V. S., & Krishnamachari, B. (2011). Multichannel scheduling and spanning trees: Throughput-delay tradeoff for fast data collection in sensor networks. IEEE/ACM Transactions on Networking, 19(6), 1731–1744.

    Article  Google Scholar 

  126. Pervin, S., Kamruzzaman, J., & Karmakar, G. (2012). Delay-aware query routing tree for wireless sensor networks. In IEEE 11th international symposium on network computing and applications, NCA 2012 (pp. 105–110). doi:10.1109/NCA.2012.39.

  127. Varma, S., Tiwary, U. S., Jain, A., & Sharma, T. (2008). Statistical energy efficient multipath routing protocol. In International conference on information networking, ICOIN 2008 (pp. 1–5). doi:10.1109/ICOIN.2008.4472773.

  128. Lu, Y. M., & Wong, V. W. S. (2007). An energy-efficient multipath routing protocol for wireless sensor networks. International Journal of Communication Systems, 20(7), 747–766.

    Article  Google Scholar 

  129. Galluccio, L., Leonardi, A., Morabito, G., & Palazzo, S. (2005). Tradeoff between energy-efficiency and timeliness of neighbor discovery in self-organizing ad hoc and sensor networks. In 38th Hawaii international conference on system sciences.

  130. Konstantinidis, A., Charalambous, C., Zhou, A. M., & Zhang, Q. F. (2010). Multi-objective mobile agent-based sensor network routing using MOEA/D. In 2010 IEEE world congress on computational intelligence, WCCI 2010–2010 IEEE congress on evolutionary computation, CEC 2010, July 18–23. doi:10.1109/CEC.2010.5586431.

  131. Rajagopalan, R., Mohan, C. K., Mehrotra, K. G., & Varshney, P. K. (2008). Emoca: An evolutionary multi-objective crowding algorithm. Journal of Intelligent Systems, 17(1–3), 107–123.

    Google Scholar 

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Acknowledgments

This work was funded in part by a Grant from National Natural Science Foundation of China No. 5307012. This work was also partly supported by the fund of the general program of Liaoning Provincial Department of Education Science Research, No. L2013210 and the Dalian Polytechnic University Youth Grants, No. QNJJ201307.

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Gao, T., Song, JY., Zou, JY. et al. An overview of performance trade-off mechanisms in routing protocol for green wireless sensor networks. Wireless Netw 22, 135–157 (2016). https://doi.org/10.1007/s11276-015-0960-x

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