Abstract
Wireless Sensor Networks (WSN) is a part of particular case of mobile wireless network, which is provided with distinctive feature. The routing protocol of traditional wireless network is difficult to directly apply to WSN. High speed of node triggers dynamic changes of the networks topology, which leads to frequent communications link failures of WSN. Link reliability issue of high dynamic network has aroused wide public concern. Therefore, route reliability aiming at expressway WSN is analyzed, the evolving graph theory is expanded, extended—evolving graph model (EEGM) is established, and EEGM is adopted to obtain dynamic information of WSN topology so as to obtain the information of reliable routing in advance. On this basis, reliable routing protocol (EGRAODV) based on evolving graph theory is proposed. The simulations reveal that routing protocol proposed has improved in packet transmission rate, end-to-end transmission delay, routing requests ratio and number of link failures aspects compared to other similar protocol.







Similar content being viewed by others
References
Tsai, Y.R.: WSN04-5: coverage-preserving routing protocols for randomly distributed wireless sensor networks. IEEE Trans. Wirel. Commun. 6(4), 1240–1245 (2007)
Pan, M.S., Yeh, L.W., Chen, Y.A., et al.: A WSN-based intelligent light control system considering user activities and profiles. IEEE Sens. J. 8(10), 1710–1721 (2008)
Alippi, C., Camplani, R., Galperti, C., et al.: A robust, adaptive, solar-powered wsn framework for aquatic environmental monitoring. IEEE Sens. J. 11(1), 45–55 (2011)
Ostfeld, A., Uber, J.G., Salomons, E., et al.: The battle of the water sensor networks (BWSN): a design challenge for engineers and algorithms. J. Water Resour. Plann. Manag. 134(6), 556–568 (2008)
Shin, T.H., Chin, S., Yoon, S.W., et al.: A service-oriented integrated information framework for RFID/WSN-based intelligent construction supply chain management. Autom. Constr. 20(6), 706–715 (2011)
Seah, W.K.G., Zhi, A.E., Tan, H.: Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP)—survey and challenges. In: International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, 2009, pp. 1–5. Wireless Vitae (2009)
Lin, Y., Wong, V.W.S.: WSN01-1: frame aggregation and optimal frame size adaptation for IEEE 802.11n WLANs. In: IEEE Global Telecommunications Conference, pp. 1–6. IEEE (2006)
Wang, L., Xu, L.D., Bi, Z., et al.: Data cleaning for RFID and WSN integration. IEEE Trans. Industr. Inf. 10(1), 408–418 (2014)
Chi, Q., Yan, H., Zhang, C., et al.: A reconfigurable smart sensor interface for industrial WSN in IoT environment. IEEE Trans. Industr. Inf. 10(2), 1417–1425 (2014)
Fernando, L., Antonio-Javier, G.S., Felipe, G.S., et al.: A Comprehensive approach to WSN-based ITS applications: a survey. Sensors 11(11), 10220–10265 (2011)
Lv, Z., Halawani, A., Feng, S., et al.: Multimodal hand and foot gesture interaction for handheld devices. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 11(1s), 1–19 (2014)
Geng, Y., Chen, J., Fu, R., Bao, G., Pahlavan, K.: Enlighten wearable physiological monitoring systems: on-body RF characteristics based human motion classification using a support vector machine. PP(99), 1–16 (2015)
Lin, Y., Yang, J., Lv, Z., et al.: A self-assessment stereo capture model applicable to the internet of things. Sensors 15(8), 20925–20944 (2015)
Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recogn. Lett. 94, 112–117 (2017)
Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)
Arunkumar, N., Ram Kumar, K., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, Tsallis entropy and Kolmogorov complexity. J. Med. Imaging Health Inform. 6(2), 526–531 (2016)
Liu, S., Cai, C., Zhu, Q., Arunkumar, N.: A study of software pools for seismogeology-related software based on the Docker technique. Int. J. Comput. Appl. (2017). https://doi.org/10.1080/1206212X.2017.1396429
Hamza, R., Muhammad, K., Nachiappan, A., González, G.R.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017). https://doi.org/10.1109/ACCESS.2017.2762405
Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.07.002
Acknowledgement
The National Natural Science Foundation of China (Grant No. 60673185,61073197); The natural science foundation of Jiangsu Province (Grant No. BK2010548).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Han, Y., Bai, G. & Zhang, G. Power allocation algorithm based on mixed integer nonlinear programming in WSN. Cluster Comput 22 (Suppl 2), 4519–4525 (2019). https://doi.org/10.1007/s10586-018-2065-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2065-7