Reliable and Delay Tolerant Transmission Protocols for LPWAN IoT Sensors

Huang, Cheng-Che; Chien, Yu-Cheng; Zhang, Jia-Cheng; Huang, Nen-Fu

doi:10.1109/icoin50884.2021.9333934

Cited by 2 publications

(1 citation statement)

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“…Analysis shows that transmission delays over heterogeneous networks can be reduced using this scheduler, and network efficiency can be improved. Huang et al (2021) proposed a time-constrained data collection method for IoT sensor networks. It estimated the expected/receiving interval for packets to cope with the delay factor.…”

Section: Problem Statement and Contributionsmentioning

confidence: 99%

Delay-Conscious Service Quality Constraint in IoT Sensor Networks for Smart Farming

Gupta,

Bindal

2023

Journal of Computer Science

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The Internet of Things (IoT) offers different services for the agriculture industry, such as monitoring and analysing real-time data related to current weather conditions, water level, irrigation requirements, growth of plant disease and health status/temperature/humidity, etc.,). The performance of IoT networks may vary due to environmental conditions and operational areas (rural area/urban area/underwater). These constraints may degrade the transmission quality due to delay factors because the signal propagation may vary in these areas. IoT sensors are low-powered devices designed for longdistance communication. The transmission rate may be degraded due to the delay factor, which may cause packet loss/ congestion/collision, thus resulting in unnecessary re-transmission over the cost of network resources. To resolve the transmission delay issue, there is a need to develop a solution to ensure reliable transmission under the constraint of delay, and this study will introduce a delay-aware scheme to manage the uncertainty over IoT networks in rural and urban areas. Its performance will be analysed using different quality of service constraints (i.e., throughput/delay/residual Energy/Energy Consumption, etc.) using two different IoT-based communication standards, i.e., LoRaWAN and SigFox, with IoT sensor density variation from 100-400 IoT sensors only. For simulation, an NS-3 network simulator will be utilised.

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Section: Problem Statement and Contributionsmentioning

confidence: 99%