Adaptive network switching algorithm for sensor data transfer: a simulation framework integrating GSM, Wi-Fi, and LoRa networks
Open accessAbstract
The growing deployment of IoT devices necessitates reliable sensor data transfer under diverse network conditions. This study introduces an adaptive network switching framework to enhance data dependability across GSM, Wi-Fi, and LoRa networks. By leveraging delay tolerant networking (DTN) principles and real-time performance metrics, the algorithm dynamically selects the optimal channel for transmitting sensor data—text, audio, image, and video—across urban, suburban, and rural settings. Simulations demonstrate an average 33% improvement in throughput, 24% reduction in latency, and 45% decrease in packet loss when using the adaptive framework compared to standalone networks. The adaptability score averaged 0.7 in rural scenarios, with peak performance scores reaching 1,000 for video data at night. A beehive monitoring case study validates these results in real-world conditions. This work contributes a robust, adaptable solution for sensor data optimization in IoT applications.
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The study of this topic is mostly published in medical education journals, with BMC Medical Education having the highest rates of AI bias, followed by JMIR Medical Education.
“Figure 6 shows that the study of this topic is mostly published in medical educational publications. BMC Medical Education has the highest rates of AI bias [7], followed by JMIR Medical Education [4], suggesting that researchers consider AI bias to be highly relevant to the pedagogical issue of medical education.”
From The impact of generative artificial intelligence tools on assessment equity for non-native English-speaking medical students: a systematic review (2026) · cites this paper as [4]
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- Authors
- Namugenyi, E. E., Sansa Otim, J., Zennaro, M., Wolthusen, S., & Nsabagwa, M.
- Affiliations
- Makerere University (UG)
- The Abdus Salam International Centre for Theoretical Physics (ICTP) (IT)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Namugenyi, Ephrance Eunice
- Venue
- Frontiers in the Internet of Things
- Publisher
- Frontiers Media SA
- Published
- 2025-10-03
- Volume
- 4
- ISSN
- 2813-3110 (ISSN-L)
- OpenAlex
- W4414773392
- Cited by
- 5
- References
- 44
- Citations by year
- 2026: 4 · 2025: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in the Internet of Things (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Abdus Salam International Centre for Theoretical Physics
- Direktoratet for Utviklingssamarbeid (QZA‐21/0159)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Opportunistic and Delay-Tolerant Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Keywords
- GSM, Wi-Fi, LoRa, delay tolerant networks, Internet of Things, network selection algorithm, throughput, latency, packet loss, beehive monitoring, rural networks, multimedia transmission
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