A Green Routing Mathematical Model for IoT Networks in Critical Energy Environments

  • Carlos Lozano-Garzon Universidad de los Andes
  • Germán Adolfo Montoya Universidad de los Andes
  • Yezid Donoso Universidad de los Andes

Abstract

In this paper, we propose a multi-objective mathematical optimization model that is the underlying support for the proposal of a new routing algorithm that aims to extend the lifetime in IoT networks for applications in critical energy environment. The network lifetime is evaluated for three approaches: the Hop Count approach, the Energy Consumption approach, and the Multiobjective approach based on Free Space Loss and the battery energy level of the IoT nodes. After this evaluation, we compared the different approaches in terms of how many transmissions were possible to do under a particular approach until none path cannot be found from an origin node to a destination node. Finally, we conclude that the Multi-objective method was the best strategy for extending the network lifetime since building short distance paths and considering battery level of the IoT nodes every time is, in the long run, a better strategy than just building paths considering nodes with a high battery level or building paths minimizing the number of network hops.

Author Biographies

Carlos Lozano-Garzon, Universidad de los Andes
Assistant Professor
Germán Adolfo Montoya, Universidad de los Andes
Postdoctoral Assistant
Yezid Donoso, Universidad de los Andes
Associate Professor Director of the postgraduate program in Information Security

References

[1] Akyildiz, I.F.; Vuran, M.C. (2010). Wireless Sensor Networks, John Wiley & Sons Ltd, 2010.
https://doi.org/10.1002/9780470515181

[2] Alvi, S. A.; Shah, G. A.; Mahmood, W. (2015). Energy efficient green routing protocol for Internet of Multimedia Things, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 1-6, 2015.
https://doi.org/10.1109/ISSNIP.2015.7106958

[3] Cho, Y; Kim, M; Woo, S. (2018). Energy Efficient IoT based on Wireless Sensor Networks for Healthcare, 20th International Conference on Advanced Communication Technology (ICACT, Chuncheon-si Gangwon-do, Korea (South), 294-299, 2018.
https://doi.org/10.23919/ICACT.2018.8323730

[4] Dong, Y.; Wang, J.; Shim, B.; Kim, D.I. (2016). DEARER: A Distance-and-Energy-Aware Routing With Energy Reservation for Energy Harvesting Wireless Sensor Networks, IEEE Journal on Selected Areas in Communications, 34(12), 3798-3813, 2016.
https://doi.org/10.1109/JSAC.2016.2621378

[5] Elbassiouny, S.O.; Hassan, A.M. (2015). Energy-efficient routing technique for Wireless sensor Networks under energy constraints, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), Dubrovnik, 647-652, 2015.
https://doi.org/10.1109/IWCMC.2015.7289159

[6] Farhan, L.; Kharel, R.; Kaiwartya, O.; Quiroz-Castellanos, M.; Raza, U.; Teay, S.H. (2018). LQOR: Link Quality-Oriented Route Selection on Internet of Things Networks for Green Computing, 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), Budapest, 1-6, 2018.
https://doi.org/10.1109/CSNDSP.2018.8471884

[7] Hasan, M.Z.; Al-Turjman, F.; Al-Rizzo, H. (2018). Analysis of Cross-Layer Design of Qualityof- Service Forward Geographic Wireless Sensor Network Routing Strategies in Green Internet of Things, IEEE Access, 6, 20371-20389, 2018.
https://doi.org/10.1109/ACCESS.2018.2822551

[8] Hu, J.; Luo, J.; Zheng, Y.; Li, K. (2019). Graphene-Grid Deployment in Energy Harvesting Cooperative Wireless Sensor Networks for Green IoT, IEEE Transactions on Industrial Informatics, 15(3), 1820-1829, 2019.
https://doi.org/10.1109/TII.2018.2871183

[9] Kumar, N.; Vidyarthi, D.P. (2018). A Green Routing Algorithm for IoT-Enabled Software Defined Wireless Sensor Network,IEEE Sensors Journal, 18(22), 9449-9460, 2018.
https://doi.org/10.1109/JSEN.2018.2869629

[10] Liu, X; Ansari, N. (2018). Dual-Battery Enabled Green Proximal M2M Communications in LPWA for IoT, 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, 1-6, 2018.
https://doi.org/10.1109/ICC.2018.8422203

[11] Noje, D.; Tarca, R.; Dzitac, I.; Pop, N. (2019). IoT Devices Signals Processing based on Multidimensional Shepard Local Approximation Operators in Riesz MV-algebras, International Journal of Computers Communications & Control, 14(1), 56-62, 2019.
https://doi.org/10.15837/ijccc.2019.1.3490

[12] Noje, D.; Dzitac, I.; Pop, N.; Tarca, R.(2020). IoT Devices Signals Processing Based on Shepard Local Approximation Operators Defined in Riesz MV-Algebras, Informatica, 31(1), 131-142, 2020.
https://doi.org/10.15388/20-INFOR395

[13] Voloshin, V. (2009). Introduction to Graph Theory. Nova Science Publishers, Inc. 2009.

[14] Wang, D.; Wang, X.; Liang, Y.; Wang, Z. (2017). A Service Oriented Routing Scheme for Internet of Things, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, 683-688, 2017.
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.107

[15] Zheng, J.; Jamalipour, A. (2009) Wireless Sensor Networks: A Networking Perspective, John Wiley & Sons Ltd, 2009.
https://doi.org/10.1002/9780470443521
Published
2020-06-08
How to Cite
LOZANO-GARZON, Carlos; MONTOYA, Germán Adolfo; DONOSO, Yezid. A Green Routing Mathematical Model for IoT Networks in Critical Energy Environments. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 4, june 2020. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3914>. Date accessed: 12 july 2020. doi: https://doi.org/10.15837/ijccc.2020.4.3914.

Keywords

Mathematical Optimization Model, Green Routing Algorithm, Internet of Things