Adaptive ESP-NOW Transmission Mechanism Based on Network Congestion

Brawijaya University is actively advancing Internet of Things (IoT) communication through its currently ongoing project, “Development of an Adaptive ESP-NOW Transmission Mechanism Based on Network Congestion.” Led by Primantara Hari Trisnawan and Aswin Suharsono, the project aims to improve the reliability and efficiency of ESP32-based communication in dense wireless environments.

Reliable Communication for Dense IoT Networks

The research focuses on developing an application-layer mechanism that allows ESP32 devices to adjust their transmission behavior according to current network conditions.

The proposed system monitors indicators such as Packet Delivery Ratio (PDR), packet loss, transmission delay, queue length, throughput, and transmission callback results. These metrics help determine whether the network is operating normally or experiencing congestion.

The mechanism will be tested using a multi-node star topology, where several ESP32 sensor nodes transmit data to a central gateway. Network conditions will be varied by changing the number of nodes, transmission intervals, payload sizes, transmission power, and distance between devices.

Key Research Components

Network Congestion Detection

The system detects congestion using metrics that can be observed locally by ESP32 devices. A sudden decrease in PDR, an increase in delay, or repeated transmission failures may indicate that the wireless channel is becoming congested.

Threshold-based detection can be used to trigger adaptation when packet loss or delay exceeds a defined limit. Trend-based methods, such as the Exponentially Weighted Moving Average (EWMA), may also be explored to detect gradual changes in network conditions.

Adaptive Transmission Control

When congestion is detected, each node can modify its transmission strategy. Several mechanisms will be evaluated, including:

• Rate control to reduce how frequently packets are transmitted.
• Dynamic backoff to increase the waiting time after transmission failures.
• Dynamic transmission intervals based on current network load.
• Transmission power adjustment to balance communication range and interference.
• Application-layer acknowledgements and retransmission mechanisms.

For example, a node may double its transmission interval when PDR falls below a specified threshold. The research will also explore adaptive backoff strategies such as Fibonacci Backoff and Binary Exponential Backoff to reduce simultaneous transmissions among competing nodes.

Balancing Energy and Latency

Adaptive transmission introduces an important trade-off between energy consumption and communication latency. Reducing transmission frequency can save energy and prevent collisions, but it may also increase the time required for sensor data to reach the gateway.

Conversely, frequent transmissions and repeated retries may reduce latency but consume more energy and contribute to congestion. The proposed mechanism therefore seeks an effective balance between Packet Delivery Ratio, delay, throughput, and power consumption.

Experimental Evaluation

The adaptive mechanism will be implemented using ESP-IDF and the ESP-NOW library. Experiments will be conducted in controlled indoor environments and realistic outdoor conditions. The adaptive mechanism will then be compared with a baseline configuration that uses fixed transmission intervals.

Performance will be evaluated using Packet Delivery Ratio, average delay, jitter, throughput, retransmission frequency, and energy consumption. Repeated experiments will determine whether the adaptive mechanism can maintain reliable communication as the number of devices and network load increase.

Supporting Resilient IoT Infrastructure

This research is expected to produce a validated adaptive ESP-NOW transmission algorithm, an ESP32-based IoT network prototype, and an academic publication.

The proposed mechanism may support IoT deployments in environments with limited internet infrastructure and high device density, including smart farming, industrial monitoring, and environmental sensing. By improving communication reliability while considering energy efficiency, the project contributes to the development of more resilient and scalable IoT infrastructure.