Long Range (LoRa) Communication Protocol with a Novel Scheduling Mechanism to Minimize the Energy in IoT


  • Senthilkumar Jagatheesan Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, No.42, Avadi-Vel Tech Road, Vel Nagar, Avadi, Chennai, Tamil Nadu, India.
  • Nargis Parveen Lecturer Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Kingdom of Saudi Arabia
  • Divya Mahajan Assistant Professor Satyawati college Delhi University,India
  • Sonali Nerkar Assistant Professor, Symbiosis School of Culinary Arts, Symbiosis International [Deemed] University, Hill Base Lavale, Pune.
  • Gurwinder Singh associate Professor, Department of AIT-CSE, Chandigarh University, Punjab, India.
  • Humera Khan Assistant Professor Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Kingdom of Saudi Arabia
  • Manoj Kumar School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India.


Energy, IoT, optimization, routing, Environmental Adaption Method


The proliferation of IoT devices has increased the concerns about their cohesive energy consumption. Many of these gadgets are portable, but there are also a sizable number that are permanently connected to the internet. Most battery-powered devices need a huge amount of power to operate, however not necessarily for all gadgets. The device has a finite lifespan due to the limitations of its power source. The main objective of this study is on developing a strategy for routing Internet of Things devices and selecting an appropriate frequency range. The unique communication technology LoRa (Long Range), designed for Internet of Things (IoT) devices, has been chosen as the frequency band routing. It is an optimization method based on the modified version of the Environmental Adaptation Method. Our proposed method minimizes the energy consumption as well as throughput of the routing mechanism. The average response time of proposed method is 0.09095 which is lower than other existing metaheuristic approaches such as ACO, GA, K-Means, PSO, DE are 0.11484, 11225, 0.15364, 0.12591, 0.12265 respectively.


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Y. Hu, Y. Ding, K. Hao, L. Ren, H. Han, An immune orthogonal learning particleswarm optimisation algorithm for routing recovery of wireless sensornetworks with mobile sink, Int. J. Syst. Sci. 45 (3) (2014) 337–350.

G.L. da Silva Fré, J. de Carvalho Silva, F.A. Reis, L.D.P. Mendes, Particle Swarmoptimization implementation for minimal transmission power providing afully-connected cluster for the internet of things, in International Workshopon Telecommunications (IWT), 2015, pp. 1–7.

L. Song, K.K. Chai, Y. Chen, J. Loo, S. Jimaa, J. Schormans, Qpso-based energyawareclustering scheme in the capillary networks for internet of thingssystems, in IEEE Wireless Communications and Networking Conference(WCNC), 2016, pp. 1–6.

W.-T. Sung, C.-C. Hsu, Iot system environmental monitoring using IPSOweight factor estimation, Sens. Rev. 33 (3) (2013) 246–256.

T. Kumrai, K. Ota, M. Dong, J. Kishigami, D.K. Sung, Multi-objectiveoptimization in cloud brokering systems for connected internet of things,IEEE Int. Things J. 4 (2) (2017) 404–413.

A.V. Dhumane, R.S. Prasad, J.R. Prasad, An optimal routing algorithm forinternet of things enabling technologies, Int. J. Rough Sets Data Anal. (IJRSDA)4 (3) (2017) 1–16.

J. Martins, A. Mazayev, N. Correia, G. Schütz, A. Barradas, Gacn: self-clusteringgenetic algorithm for constrained networks, IEEE Commun. Lett. 21 (3) (2017)628–631.

Khan, J. Sahoo, S. Han, R. Glitho, N. Crespi, A genetic algorithm-basedsolution for efficient in-network sensor data annotation in virtualizedwireless sensor networks, in 13th IEEE Annual Consumer Communications& Networking Conference (CCNC), 2016, pp. 321–322.

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjectivegenetic algorithm: NSGA-ii, IEEE Trans. Evol. Comput. 6 (2)(2002) 182–197.

Rodriguez, A. Ordóñez, H. Ordoñez, R. Segovia, Adapting NSGA-ii forhierarchical sensor networks in the IoT, Procedia Comput. Sci. 61 (2015) 355–360.

Ferreira, J.C.; Afonso, J.A.; Monteiro, V.; Afonso, J.L. An Energy Management Platform for Public Buildings.Electronics 2018, 7, 294.

Mataloto, B.; Ferreira, J.C.; Cruz, N. LoBEMS—IoT for Building and Energy Management Systems. Electronics 2019, 8, 763.

S Vimal, M Khari, N Dey, RG Crespo, YH Robinson, “Enhanced resourceallocation in mobile edge computing using reinforcement learning basedMOACO algorithm for IIOT”, Computer Communications 151, pp. 355-364, 2020

M. Khari, et al., “Performance analysis of six meta-heuristic algorithmsover automated test suite generation for path coverage-based optimization”,Soft Computing, In Press, 2019

S. Kumar, Z. Raza, “A K-Means Clustering Based Message ForwardingModel for Internet of Things (IoT)”, International Conference on CloudComputing, Data Science & Engineering (Confluence), IEEE, pp 604-609, 2018.

C. G. García, E. R. N. Valdez, V. G. Díaz, C. P. G.Bustelo, J. M. C. Lovelle,“A Review of Artificial Intelligence in the Internet of Things, InternationalJournal of Interactive Multimedia and Artificial Intelligence, Volume 5,Issue 4, pp 9-20, 2019.

F. Liu, P. Shu, H. Jin, L. Ding, J. Yu, D. Niu and B. Li, “Gearing resource-poor mobile devices with powerful clouds: Architectures, challenges, and applications,” IEEE Wirel. Commun., vol. 20, no. 3, pp.14–22, Jun. 2013.

W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, Oct. 2016.

W. Yu, F. Liang, X. He, Hatcher, W. G., C. Lu, J. Lin, and X. Yang. “A survey on the edge computing for the internet of things,” IEEE Access, vol. 6, no. 99, pp. 6900-6919, Mar. 2018.

R. Wan, N. Xiong and N. T. Loc, “An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks,” Hum. Cent. Comput. Inf. Sci., vol. 8, no. 1, pp. 18, Jun. 2018.

E. Ahmed and M. H. Rehmani, “Mobile Edge Computing: Opportunities, solutions, and challenges”, Future Gener. Comp. Sy., vol. 70, no. 2017, pp. 59-63, Sep. 2016.

V. Bhanumathi and C. P. Sangeetha, “A guide for the selection of routing protocols in WBAN for healthcare applications.” Hum. Cent. Comput. Inf. Sci., vol. 7, no. 24, Aug. 2017.

B. Kim, “A Distributed Coexistence Mitigation Scheme for IoT-Based Smart Medical Systems.” J. Inf. Process Syst., vol. 13, no. 6, pp. 1602-1612, Dec. 2017.

C. Kerang, H. Lee and H. Jung, “Task Management System According to Changes in the Situation Based on IoT.” J. Inf. Process Syst. vol. 13, no. 6, pp. 1459-1466, Dec. 2017.

S. Bu and F. R. Yu. “Green cognitive mobile networks with small cells for multimedia communications in the smart grid environment.” IEEE Trans. Veh. Technol., vol. 63, no. 5, pp. 2115-2126, Jun. 2014.

Singh, Priyanka, Pragya Dwivedi, and Vibhor Kant. "A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting." Energy 174 (2019): 460-477.




How to Cite

Jagatheesan, S. ., Parveen, N. ., Mahajan, D. ., Nerkar, S., Singh, G. ., Khan, H. ., & Kumar, M. . (2023). Long Range (LoRa) Communication Protocol with a Novel Scheduling Mechanism to Minimize the Energy in IoT. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 184–193. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4241



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