M2M Routing Protocol for Energy Efficient and Delay Constrained in IoT Based on an Adaptive Sleep Mode

  • Wasan Twayej
  • H. S. Al-Raweshidy
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)


In recent years, the number of machine-to-machine (M2M) networks that do not require direct human intervention has been increasing at a rapid pace. However, the need for a wireless platform to control and monitor these M2M networks, one with both a vast coverage area and a low network deployment cost, continues to be unmet. Wireless Sensor Networks (WSNs) with energy efficiency routing protocols in M2M environments are emerging to meet the challenges of such communication through network convergence. M2M communication is considered as the core of the Internet of Things (IoT). IoT refers to a network of billions of objects that can send and receive data. Energy efficiency, delay are a critical issue in M2M and there is a shortfall in IP addresses in IoT. In this chapter, an energy efficient routing protocol for Wireless Sensor Networks (WSN) is presented, which provides a platform to control and M2M networks. Inefficient energy consumption caused by nodes being active all the time is tackled using an adaptive sleep mode solution to maintain high levels of Network Performance (N.P). Firstly, a Multilevel Clustering Multiple Sink (MLCMS) with IPv6 protocol over Low Wireless Personal Area Networks (6LoWPAN) is promoted using a sophisticated mathematical equation for electing cluster heads (CH) for each level, so as to prolong network lifetime. Secondly, enhanced N.P that prolongs the life time of the system and maximises the reduction of delay is achieved through an adaptive sleep mode scheme. The sensor field is divided into quarters with different levels of cluster heads (CHs) and two optimal location sinks. The performance of the MLCMS protocol is evaluated and compared with the multi-hop low-energy adaptive clustering hierarchy (M-LEACH) protocol. MLCMS performs 62% better than M-LEACH and 147% more effectively regarding energy efficiency. Next, 6LoWPAN for the proposed model is constructed, and its impact on the performance of MLCMS by Network Simulator (NS3) simulation is evaluated. This increases the packets received by the system by 7% more than using MLCMS without 6LoWPAN and it improves the flexibility of the proposed model. Subsequently, an adaptive sleep mode scheme, based on CH’s residual energy for the active period time, is introduced for MLCMS and a comparative analysis establishes that it extends the lifetime of the system twice as much as the evaluated MLCMS without the adaptive sleep mode algorithm. Furthermore, with the sleep mode algorithm, this reduces the delay by a half and increases the delivery by 10%.




  1. 1.
    Ekbatanifard, G., et al.: An energy efficient data dissemination scheme for distributed storage in the internet of things. Comput. Knowl. Eng. 1(2), 1–8 (2017)Google Scholar
  2. 2.
    Razzaque, M.A., Milojevic-Jevric, M., Palade, A., Clarke, S.: Middleware for internet of things: a survey. IEEE Internet Things J. 3(1), 70–95 (2016)CrossRefGoogle Scholar
  3. 3.
    Palattella, M.R, Accettura, N., Vilajosana, X., Watteyne, T., Grieco, L.A., Boggia, G., Dohler, M.: Standardized protocol stack for the internet of (important) things. IEEE Commun. Surv. Tutorials 15(3), 1389–1406 (2013)Google Scholar
  4. 4.
    Jung, S.-J., Chung, W.-Y.: Non-Intrusive Healthcare System in Global Machine-to-Machine Networks. Sensors Journal, IEEE 13(12), 4824–4830 (2013)CrossRefGoogle Scholar
  5. 5.
    Miao, G., Azari, A., Hwang, T.: E 2-MAC: energy efficient medium access for massive M2M communications. IEEE Trans. Commun. 64(11) 4720–4735 (2016)Google Scholar
  6. 6.
    Al-Khatib, O., Hardjawana, W., Vucetic, B.: Traffic modeling for Machine-to-Machine (M2M) last mile wireless access networks. In: 2014 IEEE Global Communications Conference (GLOBECOM), pp. 1199–1204 (2014)Google Scholar
  7. 7.
    Huang, P., Xiao, L., Soltani, S., Mutka, M.W., Xi, N.: The evolution of MAC protocols in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15(1), 101–120 (2013)Google Scholar
  8. 8.
    Park, I., Kim, D., Har, D.: MAC achieving low latency and energy efficiency in hierarchical M2M networks with clustered nodes. Sens. J. IEEE 15(3), 1657–1661 (2015)CrossRefGoogle Scholar
  9. 9.
    Farouk, F., Rizk, R., Zaki, F.W.: Multi-level stable and energy-efficient clustering protocol in heterogeneous wireless sensor networks. Wirel. Sens. Syst. IET 4(4), 159–169 (2014)CrossRefGoogle Scholar
  10. 10.
    Wang, X.: Multicast for 6LoWPAN wireless sensor networks. Sens. J. IEEE 15(5), 3076–3083 (2015)CrossRefGoogle Scholar
  11. 11.
    Kodali, R.K., Aravapalli, N.K.: Multi-level LEACH protocol model using NS-3. In: 2014 IEEE International Advance Computing Conference (IACC), pp. 375–380 (2014)Google Scholar
  12. 12.
    Lindsey, S., Raghavendra, C.S.: PEGASIS: power-efficient gathering in sensor information systems. In: 2002 Aerospace Conference Proceedings, vol. 3, pp. 3–1125–3–1130. IEEE (2002)Google Scholar
  13. 13.
    Manjeshwar, A., Agrawal, D.P.: TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In: Parallel and Distributed Processing Symposium., Proceedings 15th International, pp. 2009–2015 (2001)Google Scholar
  14. 14.
    Manjeshwar, A., Agrawal, D.P.: APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless. In: Parallel and Distributed Processing Symposium., Proceedings International, IPDPS 2002, Abstracts and CD-ROM, p. 8 (2002)Google Scholar
  15. 15.
    Lee, D., Chung, J.M., Garcia, R.C.: Machine-to-machine communication standardization trends and end-to-end service enhancements through vertical handover technology. In: Midwest Symposium on Circuits and Systems, pp. 840–844 (2012)Google Scholar
  16. 16.
    Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., Chai, M.: The impact of rank attack on network topology of routing protocol for low-power and lossy networks. Sens. J. IEEE 13(10), 3685–3692 (2013)CrossRefGoogle Scholar
  17. 17.
    Buratti, C., Stajkic, A., Gardasevic, G., Milardo, S., Abrignani, M.D., Mijovic, S., Morabito, G., Verdone, R.: Testing protocols for the internet of things on the EuWIn platform. IEEE Internet Things J. 3(1), 124–133 (2016)CrossRefGoogle Scholar
  18. 18.
    Kumar, N.H., Karthikeyan, P., Deeksha, B., Mohandas, T.: Enhanced routing over sleeping nodes in 6LoWPAN network. In: 2014 International Conference on Future Internet of Things and Cloud (FiCloud), pp. 272–279 (2014)Google Scholar
  19. 19.
    Roberts, R., Goodwin, P.: Weight approximations in multiattribute decision models. J. Multi-Criteria Decis. Anal. 303(June), 291–303 (2002)CrossRefzbMATHGoogle Scholar
  20. 20.
    Mahmood, D., Javaid, N., Mahmood, S., Qureshi, S., Memon, A.M., Zaman, T.: MODLEACH: a variant of LEACH for WSNs. In: 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), pp. 158–163 (2013)Google Scholar
  21. 21.
    Chen, Y.-L., Shih, Y.-N., Lin, J.-S.: A four-layers hierarchical clustering topology architecture with sleep mode in a wireless sensor network. In: 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 335–339 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Electronic and Computer Engineering, College of Engineering, Design and Physical SciencesBrunel University LondonLondonUK

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