Abstract
With the advancements in sensor applications, wireless sensor networks (WSNs) become significant area of research. WSNs compose various tiny sensor nodes to sense an environment, depends upon the given application. However, these nodes are battery constrained (i.e., may become dead after passing certain iterations). Therefore, number of energy efficient protocols have been implemented in literature. However, selecting an optimal path between base station and sensor nodes is defined as an ill-posed problem. To overcome this issue, a Deep Q-routing based inter-cluster data aggregation is considered to improve the inter-cluster communication in WSNs. In every epoch, Recurrent neural network is considered to compute shortest path between cluster heads and sink. We have trained the network in such a way that it considers various features of WSNs and able to decide which sensor node will be selected as next-hop to establish a shortest path between elected cluster heads and sink. The non-cluster head nodes may also be considered while selecting a shortest path. Extensive experimental results show that the proposed technique outperforms the competitive energy efficient protocols.
Similar content being viewed by others
References
Zou, Y., Quan, L.: A new service-oriented grid-based method for aiot application and implementation. Mod. Phys. Lett. B 31(19–21), 1740064 (2017)
Wang, Y., Huang, S., Ji, Z.: Operation management of daily economic dispatch using novel hybrid particle swarm optimization and gravitational search algorithm with hybrid mutation strategy. Mod. Phys. Lett. B 31(19–21), 1740099 (2017)
Xu, B., Wang, Y., Ji, Z.: Knowledge network model of the energy consumption in discrete manufacturing system. Mod. Phys. Lett. B 31(19–21), 1740100 (2017)
Zhang, M., Ji, Z., Wang, Y.: Artificial bee colony algorithm with dynamic multi-population. Mod. Phys. Lett. B 31(19–21), 1740087 (2017)
Leu, J., Chiang, T., Yu, M., Su, K.: Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Commun. Lett. 19, 259–262 (2015)
Pal, V., Singh, G., Yadav, R.P.: Balanced cluster size solution to extend lifetime of wireless sensor networks. IEEE Internet Things J. 2, 399–401 (2015)
Chidean, M.I., Morgado, E., Sanroman-Junquera, M., Ramiro-Bargueno, J., Ramos, J., Caamano, A.J.: Energy efficiency and quality of data reconstruction through data-coupled clustering for self-organized large-scale wsns. IEEE Sens. J. 16, 5010–5020 (2016)
Lee, J., Kao, T.: An improved three-layer low-energy adaptive clustering hierarchy for wireless sensor networks. IEEE Internet Things J. 3, 951–958 (2016)
El Alami, H., Najid, A.: Ech: an enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access 7, 107142–107153 (2019)
Gharaei, N., Al-Otaibi, Y.D., Butt, S.A., Sahar, G., Rahim, S.: Energy-efficient and coverage-guaranteed unequal-sized clustering for wireless sensor networks. IEEE Access 7, 157883–157891 (2019)
Lin, H., Wang, L., Kong, R.: Energy efficient clustering protocol for large-scale sensor networks. IEEE Sens. J. 15, 7150–7160 (2015)
Chamam, A., Pierre, S.: On the planning of wireless sensor networks: energy-efficient clustering under the joint routing and coverage constraint. IEEE Trans. Mob. Comput. 8, 1077–1086 (2009)
Pachlor, R., Shrimankar, D.: Lar-ch: a cluster-head rotation approach for sensor networks. IEEE Sens. J. 18, 9821–9828 (2018)
Hong, Z., Wang, R., Li, X.: A clustering-tree topology control based on the energy forecast for heterogeneous wireless sensor networks. IEEE/CAA J. Autom. Sin. 3, 68–77 (2016)
Tarhani, M., Kavian, Y.S., Siavoshi, S.: Seech: scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sens. J. 14, 3944–3954 (2014)
Lee, W., Nguyen, M.V., Verma, A., Lee, H.S.: Schedule unifying algorithm extending network lifetime in s-mac-based wireless sensor networks. IEEE Trans. Wirel. Commun. 8, 4375–4379 (2009)
Lin, Chia-Hung, Tsai, Ming-Jer: A comment on “heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mob. Comput. 5, 1471–1472 (2006)
Wang, T., Zhang, G., Yang, X., Vajdi, A.: Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. J. Syst. Softw. 146, 196–214 (2018)
Ahmed, G., Zou, J., Fareed, M.M.S., Zeeshan, M.: Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Comput. Electr. Eng. 56, 385–398 (2016)
Shankar, T., Shanmugavel, S., Rajesh, A.: Hybrid hsa and pso algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol. Comput. 30, 1–10 (2016)
Goswami, P., Yan, Z., Mukherjee, A., Yang, L., Routray, S., Palai, G.: An energy efficient clustering using firefly and hml for optical wireless sensor network. Optik 182, 181–185 (2019)
Wang, Q., Xu, K., Takahara, G., Hassanein, H.: On lifetime-oriented device provisioning in heterogeneous wireless sensor networks: approaches and challenges. IEEE Netw. 20, 26–33 (2006)
Ding, X.-X., Ling, M., Wang, Z.-J., Song, F.-L.: Dk-leach: an optimized cluster structure routing method based on leach in wireless sensor networks. Wirel. Pers. Commun. 96(4), 6369–6379 (2017)
Han, R., Yang, W., Wang, Y., You, K.: Dce: a distributed energy-efficient clustering protocol for wireless sensor network based on double-phase cluster-head election. Sensors 17(5), 998 (2017)
Mann, P.S., Singh, S.: Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks. J. Netw. Comput. Appl. 83, 40–52 (2017)
Ha, I., Djuraev, M., Ahn, B.: An optimal data gathering method for mobile sinks in wsns. Wireless Pers. Commun. 97(1), 1401–1417 (2017)
You, X., Li, X., Xu, Y., Feng, H., Zhao, J.: Toward packet routing with fully-distributed multi-agent deep reinforcement learning. arXiv arXiv:1905.03494 (2019) (preprint)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest to this work
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Joshi, U., Kumar, R. A novel deep neural networks based path prediction. Cluster Comput 23, 2915–2924 (2020). https://doi.org/10.1007/s10586-020-03056-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-020-03056-8