Abstract
The principles of physics and system sciences are increasingly used in the field of network engineering to design network protocols. This work proposes an energy and congestion aware routing (ECAR) algorithm inheriting the concepts of the potential field. It uses depth and time-variant network parameters to forward the data packets through low congestion and an energy-balanced path. We define a novel forward aware energy density as a decision metric along with residual energy and queue-length for forwarding data packets. It results in network-wide balanced residual energy and enhanced network lifetime. The proposed ECAR algorithm is evaluated for the transmission rounds before the first dead node (FDN) is detected. It is found that in typical traffic conditions, there was an average increment of 45% transmission rounds till the FDN appeared. Moreover, the simulated and theoretical findings are compared using statistical measures that justify its energy and congestion awareness.
Similar content being viewed by others
Notes
Origin address of a packet is the address of the node which generates that packet.
Path vector (\(\pi\)) of the packet is set of nodes through which packet visited in earlier transmission route.
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4), 2347–2376.
Anand, V., Jain, A., Pattanaik, K. K., & Kumar, A. (2018). Traffic aware field-based routing for wireless sensor networks. Telecommunication Systems, 1–15.
Basu, A., Lin, A., Ramanathan, S. (2003). Routing using potentials: A dynamic traffic-aware routing algorithm. In Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, (pp. 37–48). ACM.
Bertsekas, D. P. (2014). Constrained optimization and Lagrange multiplier methods. London: Academic press.
Bhattacharjee, S., & Bandyopadhyay, S. (2013). Lifetime maximizing dynamic energy efficient routing protocol for multi hop wireless networks. Simulation Modelling Practice and Theory, 32, 15–29.
Boukerche, A., Cheng, X., Linus, J. (2003). Energy-aware data-centric routing in microsensor networks. In Proceedings of the 6th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems (pp. 42–49). ACM.
Chen, S., & Nahrstedt, K. (1999). Distributed quality-of-service routing in ad hoc networks. IEEE Journal on Selected Areas in Communications, 17(8), 1488–1505.
Chen, T. -W., Tsai, J. T., & Gerla, M. (1997). Qos routing performance in multihop, multimedia, wireless networks. In 1997 IEEE 6th international conference on universal personal communications record, 1997. Conference Record, (Vol. 2, pp. 557–561). IEEE.
Dahiya, R., Arora, A. K., & Singh, V. R. (2015). Modelling the energy efficient sensor nodes for wireless sensor networks. Journal of The Institution of Engineers (India): Series B, 96(3), 305–309.
Efthymiou, C., Nikoletseas, S., & Rolim, J. (2006). Energy balanced data propagation in wireless sensor networks. Wireless Networks, 12(6), 691–707.
Fourty, N., Van Den Bossche, A., & Val, T. (2012). An advanced study of energy consumption in an ieee 802.15. 4 based network: Everything but the truth on 802.15. 4 node lifetime. Computer Communications, 35(14), 1759–1767.
Gnuplot documentation. http://www.gnuplot.info/.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000. (pp. 10–pp). IEEE.
Heinzelman, W. R., Kulik, J., & Balakrishnan, H. (1999). Adaptive protocols for information dissemination in wireless sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking (pp. 174–185). ACM.
Hong, Z., Wang, R., & Li, X. (2016). A clustering-tree topology control based on the energy forecast for heterogeneous wireless sensor networks. IEEE/CAA Journal of Automatica Sinica, 3(1), 68–77.
Liu, Y., Peng, Y., Wang, B., Yao, S., & Liu, Z. (2017). Review on cyber-physical systems. IEEE/CAA Journal of Automatica Sinica, 4(1), 27–40.
Mamei, M., & Zambonelli, F.. (2006). Field-based coordination for pervasive multiagent systems. Berlin: Springer.
Ns documentation. http://www.isi.edu/nsnam/ns/ns-documentation.html.
Olariu, S., & Stojmenovic, I. (2006). Design guidelines for maximizing lifetime and avoiding energy holes in sensor networks with uniform distribution and uniform reporting. In INFOCOM (pp. 1–12).
Ren, F., He, T., Das, S. K., & Lin, C. (2011). Traffic-aware dynamic routing to alleviate congestion in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(9), 1585–1599.
Rodoplu, V., & Meng, T. H. (1999). Minimum energy mobile wireless networks. IEEE Journal on Selected Areas in Communications, 17(8), 1333–1344.
Singh, M., & Prasanna, V. K. (2003). Energy-optimal and energy-balanced sorting in a single-hop wireless sensor network. In Proceedings of the first IEEE international conference on pervasive computing and communications, 2003.(PerCom 2003), IEEE.
Sohraby, K., Minoli, D., & Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. New York: Wiley.
Song, J., An, W., Hu, Y., Zhang, Y., Zhou, X., & Xu, Z. (2015). Balancing harvesting energy consumption with potential field in wireless sensor networks. In 2015 IEEE 26th annual international symposium on personal, indoor, and mobile radio communications (PIMRC) (pp. 2014–2019).
Tang, L., Feng, S., Hao, J., & Zhao, X. (2015). Energy-efficient routing algorithm based on multiple criteria decision making for wireless sensor networks. Wireless Personal Communications, 80(1), 97–115.
Tudose, D., Gheorghe, L., & Tapus, N. (2013). Radio transceiver consumption modeling for multi-hop wireless sensor networks. UPB Scientific Bulletin: Series C, Electrical Engineering, 75(1), 17–26.
Urgaya, C. L., & Savarapu, P. R. (2016). Genetic algorithm inspired energy efficient balanced clustering for sensor networks. In International conference on wireless communications, signal processing and networking (WiSPNET), (pp. 627–633). IEEE.
Xu, Y., Heidemann, J., & Estrin, D. (2001). Geography-informed energy conservation for ad hoc routing. In Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 70–84). ACM.
Yan, J., Zhou, M., & Ding, Z. (2016). Recent advances in energy-efficient routing protocols for wireless sensor networks: A review. IEEE Access, 4, 5673–5686.
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), 366–379.
Zhang, J., Ren, F., Gao, S., Yang, H., & Lin, C. (2015). Dynamic routing for data integrity and delay differentiated services in wireless sensor networks. IEEE Transactions on Mobile Computing, 14(2), 328–343.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
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
Jain, A., Pattanaik, K.K., Kumar, A. et al. Energy and congestion aware routing based on hybrid gradient fields for wireless sensor networks. Wireless Netw 27, 175–193 (2021). https://doi.org/10.1007/s11276-020-02439-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-020-02439-4