Modeling the Trade-off Between Throughput and Reliability in Bluetooth Low Energy Connections Under Interference

Core Concepts

The trade-off between throughput and reliability in Bluetooth Low Energy (BLE) connections can be accurately modeled and quantified through mathematical models that are validated by extensive practical experiments.

Abstract

The paper presents a thorough investigation of the trade-off between throughput and reliability in Bluetooth Low Energy (BLE) communications under interference.
First, a mathematical model is derived to predict the throughput of a BLE connection under interference. This throughput model is then linked to a previously developed reliability model. The theoretical results from both models are extensively validated through practical experiments under various scenarios, including different interference conditions and BLE parameter settings.
The validated models are then used to explore the trade-off between throughput and reliability. Pareto curves are drawn to visualize the compromise between these two key performance metrics. The models provide a design-level guideline for BLE usage and deployment, allowing users to quantify the impact of different BLE parameters on throughput and reliability.
The key highlights are:

A novel mathematical model to predict BLE throughput under interference is derived and validated.
The throughput model is linked to a previously developed reliability model to explore the trade-off.
Extensive practical experiments are conducted to validate the accuracy of both the throughput and reliability models.
Pareto curves are used to illustrate the trade-off between throughput and reliability under different scenarios.
The models can serve as a design-level guideline for BLE usage and deployment.

Stats

The packet transmission time of the victim connection (PTV) ranges from 912 μs to 1712 μs, corresponding to a payload size from 100 bytes to 200 bytes.
The packet transmission time of the disturber connection (PTD) is 512 μs with a 50-byte payload.
The connection interval (CID) is set to 7.5 ms for both the victim and disturber connections.
The inter-frame space (IFS) is 150 μs.

Quotes

"The similarity between the theoretical results and the experimental ones highlights the accuracy of the proposed throughput and reliability models. Hence, the two models can be used to explore the performance of various BLE designs or deployments from diverse perspectives."
"The significance of the suggested model is to explain the trade-off within BLE communications simply through numbers and formulas. Rather than just providing a rough or general trend, the trade-off and relationships between reliability and throughput are accurately calculated under various scenarios."

Key Insights Distilled From

Modeling the Trade-off between Throughput and Reliability in a Bluetooth Low Energy Connection

by Bozheng Pang... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01231.pdf

Deeper Inquiries

How can the proposed models be extended to consider the impact of other wireless technologies, such as Wi-Fi, coexisting in the 2.4 GHz band

The proposed models can be extended to consider the impact of other wireless technologies, such as Wi-Fi, coexisting in the 2.4 GHz band by incorporating additional parameters and variables into the existing models.
One approach could be to introduce a parameter that represents the presence and activity of Wi-Fi devices in the same frequency band. This parameter could influence the interference levels experienced by the BLE devices and impact their throughput and reliability. By including this parameter in the models, the interactions between BLE and Wi-Fi transmissions can be analyzed more comprehensively.
Furthermore, the models can be modified to account for the specific characteristics of Wi-Fi interference, such as the channel utilization, signal strength, and packet collision probabilities. By integrating these factors into the models, a more accurate representation of the coexistence of BLE and Wi-Fi in the 2.4 GHz band can be achieved.

What are the potential applications of the trade-off analysis between throughput and reliability in BLE networks, beyond the IoT domain

The trade-off analysis between throughput and reliability in BLE networks has potential applications beyond the IoT domain. Some of these applications include:

Industrial Automation: In industrial automation systems, where reliability and real-time communication are crucial, understanding the trade-off between throughput and reliability can help optimize the performance of wireless communication networks. By balancing these two factors, industrial processes can be more efficiently monitored and controlled.

Healthcare: In healthcare applications, such as remote patient monitoring and medical device connectivity, maintaining a high level of reliability while ensuring sufficient throughput is essential. The trade-off analysis can assist in designing wireless networks that meet the stringent requirements of healthcare environments.

Smart Cities: In smart city deployments, where a large number of devices are interconnected to enable various services, optimizing the trade-off between throughput and reliability can enhance the overall efficiency of the smart city infrastructure. This can lead to improved services in areas such as transportation, energy management, and public safety.

Telecommunications: In the telecommunications sector, especially in the development of 5G networks and beyond, understanding the trade-off between throughput and reliability is critical for ensuring seamless connectivity and high-quality services. By applying the analysis to wireless communication networks, telecom operators can enhance network performance and user experience.

How can the models be further improved to capture the dynamic nature of interference in real-world deployments, where the interference sources may change over time

To capture the dynamic nature of interference in real-world deployments, where interference sources may change over time, the models can be further improved in the following ways:

Dynamic Parameter Adjustment: Introduce mechanisms to dynamically adjust model parameters based on real-time interference conditions. This could involve incorporating feedback loops that continuously monitor interference levels and adapt the model parameters accordingly.

Machine Learning Integration: Utilize machine learning algorithms to predict and adapt to changing interference patterns. By training the models on historical data and real-time measurements, they can learn to anticipate and respond to variations in interference sources.

Adaptive Algorithms: Develop adaptive algorithms that can optimize the trade-off between throughput and reliability in response to changing interference environments. These algorithms can dynamically adjust communication parameters to maintain optimal performance under varying conditions.

Real-world Validation: Conduct extensive field trials and experiments in diverse environments to validate the models' performance in dynamic interference scenarios. By testing the models in real-world deployments, their accuracy and effectiveness in capturing dynamic interference can be assessed and improved.