# Packet-Level System Performance of a Lightweight Consensus Cryptographic Protocol in Wireless Body Area Networks

Eunice Fuentes-Juarez, Gina Gallegos-Garcia, Mario Rivero-Angeles, Gerardo Rubino

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Eunice Fuentes-Juarez, Gina Gallegos-Garcia, Mario Rivero-Angeles, Gerardo Rubino. Packet-Level System Performance of a Lightweight Consensus Cryptographic Protocol in Wireless Body Area Networks. ICBC 2025 - IEEE International Conference on Blockchain and Cryptocurrency, Jun 2025, Pisa, Italy. pp.1-3, 10.1109/ICBC64466.2025.11114506. [https://doi.org/10.1109/ICBC64466.2025.11114506](https://doi.org/10.1109/ICBC64466.2025.11114506)

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Packet-Level System Performance
of a Lightweight Consensus
Cryptographic Protocol
in Wireless Body Area Networks

Eunice J. Fuentes-Juarez¹, Gina Gallegos-Garcia², Mario E.

Rivero-Angeles² & Gerardo Rubino³ ¹,²*Instituto Politécnico Nacional,*

³INRIA Rennes – Bretagne Atlantique

¹,²Mexico City, Mexico, ³Rennes, France

¹efuentesj1500@alumno.ipn.mx, ²{ggallegos, erivero}@cic.ipn.mx,

³gerardo.rubino@inria.fr

**Abstract**

Wireless Body Area Networks (WBANs) play a critical role in healthcare by enabling real-time monitoring through wearable devices. Blockchain technology improves data integrity and security in WBANs, but introduces significant transmission and energy challenges due to the constrained nature of these networks. This study evaluates a lightweight consensus protocol that uses Practical Byzantine Fault Tolerance (PBFT) principles, tailored to WBAN requirements, which is stated in [2]. Our study considers the main dynamics of the system, such as activation and deactivation rates, as well as packet generation rates and transmission durations. Through packet-level system simulations, the reliability and efficiency of the protocol are analyzed. This work provides actionable insights into optimizing blockchain-based WBAN systems, highlighting the trade-offs between node activation rates and network stability.

**Index Terms**

Wireless Body Area Networks, Blockchain, Consensus Protocols, Throughput Analysis, Performance Modeling, Stochastic Simulation

# I. INTRODUCTION

Wireless Body Area Networks (WBANs) consist of wearable devices designed to monitor physiological parameters and transmit data to a central hub, such as a smartphone or a medical device. The intra-WBAN layer plays a critical role in these systems, as it governs direct communication between sensors and the hub before

The authors kindly acknowledge the financial support from the Instituto Politécnico Nacional (IPN) under grants
SIP-20250059 and SIP-20250076.
any external data transmission occurs. These networks operate in highly constrained environments, with devices limited in energy, bandwidth, and computational power. Understanding these constraints is essential for accurately simulating their performance under real-world conditions.

The integration of a blockchain in WBANs introduces both opportunities and challenges. The achievement of consensus plays a crucial role in blockchain-based WBANs, as it ensures agreement among nodes on the validity of transactions. Despite this, the consensus process often introduces significant delays and increases the likelihood of collisions due to the extensive message exchanges required. Evaluating how consensus mechanisms, such as the Practical Byzantine Fault Tolerance (PBFT) consensus, affect throughput and collision probability, are essential for developing optimized protocols [6].

## II. SIMULATION

The lightweight consensus cryptographic protocol analyzed in this work, which is specifically designed for biosignal monitoring in Wireless Body Area Networks (WBANs), is defined in [2]. This previous research on consensus protocols for biosignal monitoring serves as a basis for the design choices made in this analysis of the protocol. To study its impact, the protocol detailed in [2] is modeled as a system, where the devices known as nodes generate, transmit and receive packets and can be active or inactive, with a Central Unit (CU) acting as the coordinator managing communications.

The simulation process is depicted in Figure 1 and it provides a representation of the simulation logic; it aims to evaluate the behavior of the network under different parameter settings. A dynamic event-driven approach is adopted, where events such as packet arrivals, node activations, and packet transmissions occur in a stochastic manner. A FIFO-based event handling mechanism ensures realistic simulation of network dynamics. Parameters include $α$ for node activation rate, $β$ for node deactivation rate, $λ$ for packet generation rate, and $σ$ for packet transmission time. The gamma function encapsulates the consensus condition stated in [1] and the packet generation mechanism. Here we model whether the system can successfully transmit packets, based on the condition "# of nodes $\ge 1+3 \times$ # of nodes OFF"; this condition ensures that there are enough active nodes to maintain stable communication and achieve consensus. The function evaluates whether the previous consensus condition is met, and if it is not, the system fails to transmit packets successfully.

## III. NUMERICAL RESULTS

The findings of this study provide insight into the performance of Wireless Body Area Network (WBAN) systems under a range of network conditions. Through the analysis of throughput, collision probability, and the influence of network parameters, these results are juxtaposed with existing literature to elucidate their relevance and potential implications for blockchain-based consensus protocols in WBANs.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Event-Driven Simulation Flowchart for Network Throughput and Collision Analysis of our Lightweight Consensus Cryptographic Protocol in Wireless Body Area Networks.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Throughput Analysis with Respect to Transmission Time ($\sigma$) and Packet Generation Rate ($\lambda$).</figcaption></figure>

<table><caption>TABLE I
COMPARISON WITH STATE-OF-THE-ART PROTOCOLS</caption><thead><tr><th>Protocol</th><th>Throughput</th><th>Collision Prob.</th><th>ON/OFF Dynamics</th><th>Energy Efficiency</th></tr></thead><tbody><tr><td>Fuentes-Juarez et al., 2024 [2]</td><td>Moderate</td><td>Low</td><td>Yes</td><td>High</td></tr><tr><td>Xu et al., 2023 [7]</td><td>High</td><td>Moderate</td><td>No</td><td>Moderate</td></tr><tr><td>Quistom et al., 2022 [9]</td><td>High</td><td>Low</td><td>No</td><td>High</td></tr><tr><td>Hu et al., 2023 [12]</td><td>Moderate</td><td>Low</td><td>Indirect</td><td>High</td></tr><tr><td>Iyobebbe et al., 2022 [13]</td><td>Moderate</td><td>Not Analyzed</td><td>Indirect</td><td>Moderate</td></tr><tr><td>Sharmila et al., 2022 [4]</td><td>Moderate</td><td>Not Analyzed</td><td>No</td><td>Low</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Fig. 3. Throughput Evaluation as a Function of Node Activation (α) and Deactivation (β) Rates.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Collision Probability Characterization Based on Node Activation (α) and Deactivation (β) Rates.</figcaption></figure>

The comparative analysis illustrated in Table I highlights how various consensus protocols perform in WBAN environments with respect to key metrics: throughput, collision probability, ON / OFF dynamics and energy efficiency. These metrics are crucial for evaluating the practicality of consensus mechanisms in resource-constrained networks such as WBANs.

IV. CONCLUSIONS

Our findings highlight key trends that align closely with those in the literature. Performance is highly sensitive to activity levels of nodes, packet generation rates, and transmission times, confirming the insights of [7] and [11]. The probability of collision is mainly influenced by the activation and deactivation rates, with trends mirroring observations by [9] and [11]. Furthermore, our analysis emphasizes the importance of optimizing network parameters to balance throughput and collision probability, supporting the conclusions of [10] and [8].

These results validate the performance trends observed in existing studies and provide actionable insights to design efficient WBAN systems using blockchain-based consensus protocols. By refining network parameters and maintaining an optimal balance between activation and deactivation rates, the performance of WBANs can be significantly enhanced, ensuring reliability and efficiency in real-world deployments.
REFERENCES

[1] S. Zhong, X. Li, X. Zhao, S. Zou, X. Zhang, H. Xie, and L. Yao, "Byzantine Fault-Tolerant Consensus Algorithms: A Survey," IEEE Trans. Computers, vol. 72, no. 1, pp. 58-75, January 2023.

[2] E. J. Fuentes-Juarez, M. E. Rivero-Angeles, and G. Gallegos-Garcia, "Lightweight Consensus Cryptographic Protocol for Biosignal Monitoring in Wireless Body Area Networks," in Smart Cities: 7th Ibero-American Congress, ICSC-Cities 2024, Costa Rica, November 12-14, 2024, Revised Selected Papers, Communications in Computer and Information Science. Cham, Springer, in press.

[3] Zirou Meng, Jing Chen, and Lei Yang, "Network Performance Optimization Based on Blockchain Technology," 2023 4th International Conference on Computers and Artificial Intelligence Technology (CAIT), pp. 253-258, 2023.

[4] Helen Sharmila Anbarasan and Jaisankar Natarajan, "Blockchain-Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment," Sensors (Basel, Switzerland), vol. 22, 2022.

[5] Héctor Kaschel and Á. Díaz, "High Security Ubiquitous H-IoT on a WBAN-Based EHR using Blockchain," 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), pp. 1-6, 2021.

[6] Weikang Liu, Bin Cao, and M. Peng, "Blockchain-Based Offloading Strategy: Incentive, Effectiveness, and Security," IEEE Journal on Selected Areas in Communications, vol. 40, pp. 3533-3546, 2022.

[7] Xu, Jie, Wang, Cong, and Jia, Xiaohua, "A Survey of Blockchain Consensus Protocols," ACM Computing Surveys, vol. 55, no. 1, pp. 1-37, 2023.

[8] Guo, Hao, and Li, Yu, "A Hierarchical and Location-Aware Consensus Protocol for IoT-Blockchain Applications," IEEE Transactions on Blockchain, vol. 3, no. 2, pp. 91-102, 2022.

[9] Qustom, Haytham, Misic, Jelena, and Misic, Vojislav, "Efficient Multi-Tier, Multiple Entry PBFT Consensus Algorithm for IoT," IEEE Transactions on Communications, vol. 70, no. 4, pp. 2931-2945, 2022.

[10] Kaur, Manpreet, Khan, Mohammad Zubair, Gupta, Shikha, Noorwali, Abdulfattah, Chakraborty, Chinmay, and Pani, S., "MBCP: Performance Analysis of Large Scale Mainstream Blockchain Consensus Protocols," IEEE Access, vol. 9, pp. 80931-80944, 2021.

[11] Ma, Fan-Qi, Li, Quanlin, Liu, Yi-Han, and Chang, Yan-Xia, "Stochastic Performance Modeling for Practical Byzantine Fault Tolerance Consensus in the Blockchain," Peer-to-Peer Networking and Applications, vol. 15, no. 5, pp. 2516-2528, 2021.

[12] Hu, Xiaokang, Guo, Hao, and Zhang, Yan, "Maximizing Throughput for Coexisting Wireless Body Area Networks (WBANs) Based on Optimal Clustering," IEEE Transactions on Communications, vol. 71, no. 2, pp. 1005-1018, 2023.

[13] Iyobhebhe, D. A., and Yaro, S. T., "Enhancing Throughput Cluster-Based WBAN Using TDMA and CCA Scheme," International Journal of Wireless and Mobile Networks, vol. 14, no. 4, pp. 23-35, 2022.