ENERGY-EFFICIENT AND LOW-LATENCY ROUTING PROTOCOLS IN IOT NETWORKS: A COMPREHENSIVE LITERATURE REVIEW AND CRITICAL ANALYSIS

Hitesh Parmar; Kamaljit I. Lakhtaria

doi:10.29121/shodhkosh.v5.i1.2024.6538

Authors

Hitesh Parmar Assistant Professor, K.S School of Business Management & Information Technology, Gujarat University, Ahmedabad, Gujarat, India
Dr. Kamaljit I. Lakhtaria Associate Professor, Department of Computer Science, Gujarat University, Ahmedabad, Gujarat, India

DOI:

https://doi.org/10.29121/shodhkosh.v5.i1.2024.6538

Keywords:

Internet of Things, Routing Protocols, Energy Efficiency, Latency Optimization, Wireless Sensor Networks, Performance Analysis, Machine Learning, Deep Reinforcement Learning

Abstract [English]

The rapid expansion of Internet of Things (IoT) networks has introduced significant challenges in the design of routing protocols, particularly in achieving a balance between energy efficiency and communication latency. This paper presents a thorough literature review of energy-efficient and low-latency routing protocols for IoT networks, examining more than 200 selected publications from 2020 to December 2023. We developed a systematic taxonomy that categorizes protocols into energy-efficient, latency-optimized, and balanced approaches, assesses their performance characteristics, and pinpoints critical research gaps. Our analysis indicates that while energy-efficient protocols can enhance the network lifetime by up to 60%, recent machine learning-driven methods show up to a 59% reduction in the busiest-node routing energy while maintaining over 99% reliability. Latency-optimized solutions have achieved up to a 35.5% reduction in end-to-end delays through opportunistic forwarding and deep reinforcement learning. However, significant challenges persist in terms of security integration, real-world validation, and standardization. We offer a comprehensive roadmap for future research directions, highlighting the integration of artificial intelligence, edge computing, and next-generation network technology. This review serves as a foundation for researchers and practitioners developing advanced IoT routing solutions.

References

S. Rahmani, H. Ahmadi, and M. R. Karami, “Optimized energy-efficient routing protocol based on machine learning and metaheuristic algorithms for wireless sensor networks,” Journal of Network and Computer Applications, vol. 207, p. 103396, 2023, doi: 10.1016/j.jnca.2022.103396.

B. V. Belyatsky, “Energy-efficient and balanced routing in low-power wireless sensor networks for data collection,” Ad Hoc Networks, vol. 127, p. 102766, 2022, doi: 10.1016/j.adhoc.2021.102766.

S. Dutt, S. Agrawal, and R. Vig, “Delay-Sensitive, Reliable, Energy-Efficient, Adaptive and Mobility-Aware (DREAM) Routing Protocol for WSNs,” Wireless Personal Communications, vol. 118, no. 4, pp. 2943-2976, 2021, doi: 10.1007/s11277-021-08528-7.

Z. Alansari, N. B. Anuar, A. Kamsin, and M. R. Belgaum, “RPLAD3: anomaly detection of blackhole, grayhole, and selective forwarding attacks in wireless sensor network-based Internet of Things,” PeerJ Computer Science, vol. 9, p. e1309, 2023, doi: 10.7717/peerj-cs.1309.

A. Farzaneh, M.-A. Badiu, and J. P. Coon, “LEAST: a Low-Energy Adaptive Scalable Tree-based routing protocol for Wireless Sensor Networks,” arXiv preprint arXiv:2211.09443, 2022.

T. Winter et al., “RPL: IPv6 routing protocol for low-power and lossy networks,” RFC 6550, 2012.

G. Kaur, P. Chanak, and M. Bhattacharya, “Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs,” IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11440-11449, 2021, doi: 10.1109/JIOT.2021.3051768.

R.-I. Chang, C.-H. Tsai, and C.-H. Wang, “Edge Computing of Online Bounded-Error Query for Energy-Efficient IoT Sensors,” Sensors, vol. 22, no. 13, p. 4799, 2022, doi: 10.3390/s22134799.

O. Cheikhrouhou, K. Mershad, F. Jamil, R. Mahmud, A. Koubaa, and S. R. Moosavi, “A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System,” arXiv preprint arXiv:2301.03551, Jan. 2023.

D. Godfrey, B. Suh, B. H. Lim, K.-C. Lee, and K.-I. Kim, “An energy-efficient routing protocol with reinforcement learning in software-defined wireless sensor networks,” *Sensors*, vol. 23, no. 20, p. 8435, Oct. 2023, doi: 10.3390/s23208435.

Rawat and M. Kalla, “An energy efficient technique for improved network lifetime in wireless sensor network through energy, distance, and density-based clustering,” Instrumentation Metrologia, vol. 22, no. 2, pp. 65–72, Apr. 2023, doi: 10.18280/i2m.220203.

R. Zagrouba and A. Kardi, “Comparative study of energy efficient routing techniques in wireless sensor networks,” Information, vol. 12, no. 1, p. 42, 2021, doi: 10.3390/info12010042.

D. Hemanand and C. Senthilkumar, “Analysis of power optimization and enhanced routing protocols for wireless sensor networks,” ICT Express, vol. 8, no. 2, pp. 246-253, 2022, doi: 10.1016/j.icte.2021.11.007.

R. Nagaraju, P. VC, S. B. Goyal, and C. Verma, “Secure routing-based energy optimization for IOT application with heterogeneous wireless sensor networks,” Energies, vol. 15, no. 13, p. 4777, 2022, doi: 10.3390/en15134777.

M. Al-Sadoon, et al., “A Secure Trust-Aware Protocol for Hierarchical Routing in Wireless Sensor Network (ST2A),” International Journal of Electrical and Computer Engineering (IJEC E), vol. 12, no. 4, pp. 3838-3849, Aug. 2022.

A. Doshi, K. K. Hiran, and R. Patel, “An intelligent energy efficient routing protocol for next‐generation application in the Internet of Things and wireless sensor networks,” Wireless Communications and Mobile Computing, vol. 2022, p. 8006751, 2022, doi: 10.1155/2022/8006751.

B. Han, H. Li, Y. Zhang, and H. Sun, “A novel adaptive cluster based routing protocol for energy harvesting WSNs (HCEH-UC),” Sensors, vol. 22, no. 4, p. 1564, Feb. 2022, doi: 10.3390/s22041564.

G. Liu and L. Wang, “Routing for intermittently-powered sensing systems,” arXiv preprint arXiv:2305.12550, 2023.

G. Kaur, P. Chanak, and M. Bhattacharya, “Energy-Efficient Intelligent Routing Scheme for IoT-Enabled WSNs,” IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11440-11449, 2021, doi: 10.1109/JIOT.2021.3051768.

M. Hosseinzadeh, S. Ghavami, and A. Ghaffari, “A novel Q-learning-based routing scheme using an efficient exploration strategy in Flying Ad Hoc Networks,” Digital Communications and Networks, vol. 9, no. 4, pp. 741-752, Nov. 2023, doi: 10.1016/j.dcan.2023.01.003.

F. Liu, Z. Zhang, and Y.-A. Liu, “Low-Delay Opportunistic Routing with Reducing Overhead in Asynchronous Duty-Cycled Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 2022, p. 2308615, 2022, doi: 10.1155/2022/2308615.

“A Whale Optimization-Based Energy-Efficient Clustered Routing for Wireless Sensor Networks,” in Smart Innovation, Systems and Technologies, 2022, ch. (WECR), doi: 10.1007/978-981-19-0707-4_31.

R. Lavanyaa, “Energy Efficient with Trust and Qos-Aware Optimal Multipath Routing Protocol Based on Elephant Herding Optimization for IoT Based Wireless Sensor Networks,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 9, 2021, doi: 10.17762/TURCOMAT.V12I9.3347.

R. Dogra, S. Rani, and G. Gianini, “REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks,” Energies, vol. 16, no. 17, p. 6248, 2023, doi: 10.3390/en16176248.

“Multi-class Multipath Routing Protocol for Low Power and Lossy Networks, with Energy Balanced Optimal Rate Assignment (M2RPL),” Wireless Personal Communications, vol. 125, no. 4, pp. 3683-3708, 2022, doi: 10.1007/s11277-022-09911-8.

H. Chaudhuri, “A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications,” International Journal of Cognitive Computing in Engineering, vol. 4, pp. 172-181, 2023, doi: 10.1016/j.ijcce.2023.04.005.

W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, doi: 10.1109/HICSS.2000.926982.

K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks,” Ad Hoc Networks, vol. 3, no. 3, pp. 325-349, 2005, doi: 10.1016/j.adhoc.2003.09.010.

W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660-670, 2002.

C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” Proceedings WMCSA’99. Second IEEE Workshop on Mobile Computing Systems and Applications, 1999, pp. 90-100.

X. Fu and J. Kim, “Deep-Q-Network-Based Packet Scheduling in an IoT Environment,” Sensors, vol. 23, no. 3, p. 1339, 2023, doi: 10.3390/s23031339.

V. K. Mutombo, S. Lee, J. Lee, and J. Hong, “EER-RL: Energy-Efficient Routing Based on Reinforcement Learning,” Mobile Information Systems, vol. 2021, p. 5589145, 2021, doi: 10.1155/2021/5589145.

“A Whale Optimization-Based Energy-Efficient Clustered Routing for Wireless Sensor Networks,” in Smart Innovation, Systems and Technologies, 2022.

R. Lavanyaa, “Energy Efficient with Trust and Qos-Aware Optimal Multipath Routing Protocol Based on Elephant Herding Optimization for IoT Based Wireless Sensor Networks,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 9, 2021.

S. Singh, A. S. Nandan, A. Sikka, A. Malik, and R. Vidyarthi, “A secure energy-efficient routing protocol for disease data transmission using IoMT,” Computers and Electrical Engineering, vol. 101, p. 108113, 2022, doi: 10.1016/j.compeleceng.2022.108113.

A. Zeb, R. Wakeel, F. Rahman, A. Khan, M. Uddin, and M. Niazi, “Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network,” Computational Intelligence and Neuroscience, vol. 2022, p. 2558590, 2022, doi: 10.1155/2022/2558590.

A. Arshad, M. Asim, N. Tariq, T. Baker, A. Tawfik, and O. Al-Jumeily, “THC-RPL: A lightweight Trust-enabled routing in RPL-based IoT networks against Sybil attack,” PLOS ONE, vol. 17, no. 7, p. e0271277, 2022, doi: 10.1371/journal.pone.0271277.

S. Suresh et al., “A Novel Routing Protocol for Low-Energy Wireless Sensor Networks (OEERP),” Journal of Sensors, vol. 2022, p. 8244176, 2022, doi: 10.1155/2022/8244176.

G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 7, no. 3, pp. 537-568, 2009.

N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy-efficient routing protocols in wireless sensor networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 2, pp. 551-591, 2013.

M. Zaminkar and R. Fotohi, “SoS-RPL: Securing Internet of Things Against Sinkhole Attack Using RPL Protocol-Based Node Rating and Ranking Mechanism,” Wireless Personal Communications, vol. 114, no. 2, pp. 1287-1310, 2020, doi: 10.1007/s11277-020-07421-z.

A. Gupta, Z. Wadhwa, S. Rani, A. Khan, and N. Boulila, “EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications,” Sensors, vol. 23, no. 21, p. 8839, 2023, doi: 10.3390/s23218839.

T. Weber, A. Fersi, R. Fromm, and F. Derbel, “Wake-Up Receiver-Based Routing for Clustered Multihop Wireless Sensor Networks,” Sensors, vol. 22, no. 9, p. 3254, 2022, doi: 10.3390/s22093254.

R.-I. Chang, C.-H. Tsai, and C.-H. Wang, “Edge Computing of Online Bounded-Error Query for Energy-Efficient IoT Sensors,” Sensors, vol. 22, no. 13, p. 4799, 2022, doi: 10.3390/s22134799.

Z. Alansari, N. B. Anuar, A. Kamsin, and M. R. Belgaum, “RPLAD3: anomaly detection of blackhole, grayhole, and selective forwarding attacks in wireless sensor network-based Internet of Things,” PeerJ Computer Science, vol. 9, p. e1309, 2023.

A. Zeb, R. Wakeel, F. Rahman, A. Khan, M. Uddin, and M. Niazi, “Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network,” Computational Intelligence and Neuroscience, vol. 2022, p. 2558590, 2022.