International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 722KB)
An Adaptive Hybrid Epidemic-PRoPHET Routing Framework for Opportunistic Internet of Things Networks
PDF (722KB), PP.137-151
Views: 0 Downloads: 0
Author(s)
Abraham Tetteh
1,*
Maxwell Dorgbefu Jnr.
2
Joshua C. Dagadu
1
Victor Dela Tattrah
1
Index Terms
Opportunistic Internet of Things, Epidemic routing, PRoPHET, Hybrid routing, Reliability and Delivery ratio
Abstract
Opportunistic Internet of Things (O-IoT) networks operate in highly dynamic, infrastructureless environments where connectivity is intermittent and unpredictable, making efficient and reliable data delivery a persistent challenge. Traditional routing protocols such as Epidemic Routing and PRoPHET have been widely studied, yet both present significant drawbacks: Epidemic Routing ensures high delivery probability by replicating messages extensively, but this causes excessive buffer usage, bandwidth consumption, and energy drain, while PRoPHET employs probabilistic forwarding based on encounter histories, which is more resource-efficient but struggles in highly mobile or sparse networks where prediction accuracy decreases. To overcome these issues, this paper proposes a Hybrid Adaptive Routing framework that integrates the predictive capability of PRoPHET with a controlled epidemic fallback mechanism. The framework applies a predictability threshold of 0.6 to decide when to rely on probabilistic forwarding and when to activate epidemic replication, while carefully constraining the latter with EPIDEMIC_LIMIT = 5, HOP_LIMIT = 8, and TTL = 300 minutes to prevent resource exhaustion. . The framework was subsequently simulated and analyzed in an Opportunistic Network Environment (ONE) at different network densities and compared with the traditional routing protocols Epidemic and PRoPHET. The system's performance was evaluated using parameters such as delivery probability, overhead ratio, average latency, hop count, and buffer utilization. Experimental results confirm the approach’s effectiveness at high node density (246) nodes, where the hybrid protocol achieves a 19.03% improvement in delivery probability over Epidemic routing and 30.45% improvement over PRoPHET, alongside a 43.5% and 31.1% overhead reduction compared to Epidemic and PRoPHET respectively, and 35.5% and 37.1% latency reduction compared to Epidemic and PRoPHET respectively, making it a robust and resource-efficient solution for real-world O-IoT applications.
Cite This Paper
Abraham Tetteh, Maxwell Dorgbefu Jnr., Joshua C. Dagadu, Victor Dela Tattrah, "An Adaptive Hybrid Epidemic-PRoPHET Routing Framework for Opportunistic Internet of Things Networks", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.4, pp. 137-151, 2026. DOI:10.5815/ijwmt.2026.04.09
Reference
[1]Malik, A., Bhushan, B., Kumar, A., & Chaganti, R. (2022). Opportunistic Internet of Things (OIoT): Elucidating the Active Opportunities of Opportunistic Networks on the Way to IoT. Intelligent Systems Reference Library, 209–224. https://doi.org/10.1007/978-3-030-99329-0_14
[2]Lohachab, A., & Jangra, A. (2019). Opportunistic Internet of Things (IoT): Demystifying the Effective Possibilities of Opportunisitc Networks Towards IoT. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). https://doi.org/10.1109/spin.2019.8711621
[3]Srinidhi, N., Sagar, C. S., Chethan, S. D., Shreyas, J., & Kumar, S. D. (2020). An improved PRoPHET - Random forest based optimized multi-copy routing for opportunistic IoT networks. Internet of Things, 11, 100203. https://doi.org/10.1016/j.iot.2020.100203
[4]Patil, P. A., Deshpande, R. S., & Mane, P. B. (2020). Trust and opportunity based routing framework in wireless sensor network using hybrid optimization algorithm. Wireless Personal Communications, 115, 415-437.
[5]Bialon, R., & Graffi, K. (2019). Congestion Control for Epidemic Routing in Opportunistic Networks. 2019 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), 102–109. https://doi.org/10.1109/ithings/greencom/cpscom/smartdata.2019.00039
[6]Khan, Md. K. M., Kawsaruzzaman, K., Rahman, Md. M., Imtiaz, A., & Imtiaz, A. (2020). Impact of Node Density and Buffer Size on DTN Routing Protocols with Energy Efficiency. European Journal of Engineering and Technology Research, 5(9), 1054–1061. https://doi.org/10.24018/ejeng.2020.5.9.2104
[7]Ariwibisono, F. X., Basuki, A., & Ramdani, F. (2018). Hybrid PRoPHET-epidemic routing protocol for optimizing possibility of sending messages in remote fishermen residential area. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-3), 31-36. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4189
[8]Srinidhi, N. N., Nagarjun, E., & Kumar, S. M. D. (2019). HMCRA: Hybrid Multi-Copy Routing Algorithm for Opportunistic IoT Network. 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 370–375. https://doi.org/10.1109/icssit46314.2019.898779.
[9]Rani, & Malik, A. (2023). A social relationship-based energy efficient routing scheme for Opportunistic Internet of Things. ICT Express, 9(4), 697-705. https://doi.org/10.1016/j.icte.2022.10.002
[10]Mishra, M., Gupta, G. S., & Gui, X. (2021). Network lifetime improvement through energy-efficient hybrid routing protocol for IoT applications. Sensors, 21(22), 7439.
[11]Galán-Jiménez, J., Berrocal, J., Garcia-Alonso, J., &Azabal, M. J. (2019). A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios. Sensors, 19(8), 1875–1875. https://doi.org/10.3390/s19081875
[12]Xu, G., Li, L., & Wang, Y. (2020). PRoPHET_TD routing algorithm based on historical throughput and encounter duration. Computers, Materials & Continua, 64(3), 1611-1625. https://www.techscience.com/cmc/v64n3/39462
[13]Li, Y., Zhang, S., Chen, X., & Wang, H. (2020). An improved PRoPHET routing protocol in delay tolerant network using fuzzy logic. Computer Networks, 178, 107334. https://doi.org/10.1016/j.comnet.2020.107334
[14]Mao, Y., Zhou, C., Ling, Y., & Lloret, J. (2019). An Optimized Probabilistic Delay Tolerant Network (DTN) Routing Protocol Based on Scheduling Mechanism for Internet of Things (IoT). Sensors, 19(2), 243. https://doi.org/10.3390/s19020243
[15]Khalil, A., & Zeddini, B. (2024). Cross-layer optimization for enhanced IoT connectivity: A novel routing protocol for opportunistic networks. Future Internet, 16(6), 183. https://doi.org/10.3390/fi16060183
[16]Rani, P., Balyan, A., Jain, V., Sangwan, D., Singh, P. P., &Shokeen, J. (2020). A probabilistic routing-based secure approach for opportunistic IoT network using blockchain. In 2020 IEEE 17th India Council International Conference (INDICON). pp. 1-7. IEEE. https://doi.org/10.1109/indicon49873.2020.9342068
[17]Huang, Z., Li, X., Wang, Y., Zhang, L., & Chen, M. (2024). An energy recovery routing algorithm for opportunistic networks based on connection stability and node encounter type. Ad Hoc Networks, 154, 103455. https://doi.org/10.1016/j.adhoc.2024.103455
[18]Yu, G., Chen, Z., Wu, J., & Wu, J. (2020). Predicted encounter probability based on dynamic programming proposed probability algorithm in opportunistic social network. Computer Networks, 181, 107465. https://doi.org/10.1016/j.comnet.2020.107465
[19]Hossen, Md. S. (2021). Simulation Tools for Opportunistic Networks. Opportunistic Networks, 81–101. https://doi.org/10.1201/9781003132585-5
[20]Huang, Q., & Cao, X. (2023). Probabilistic Routing Algorithm Based on Inter-node Reliability in DTN. Proceedings of the 2023 7Th International Conference on Electronic Information Technology and Computer Engineering, 908–912. https://doi.org/10.1145/3650400.3650554
[21]Koukis, G., Safouri, K., & Tsaoussidis, V. (2024). All about Delay-Tolerant Networking (DTN) Contributions to Future Internet. Future Internet, 16(4), 129. https://doi.org/10.3390/fi16040129