Study of Power of Received Signal Strength Indicator (RSSI) for the Distance Between Nodes, Based on Freakduino for Wireless Sensor Networks (WSN)
DOI: 10.65220/x8c4m2
Authors: Ramón H. Sandoval, Francisco G. Flores-García, Mario Francisco Jesús Cepeda Rubio
Affiliations: Tecnológico Nacional de México / Instituto Tecnológico de La Laguna, Torreón, Coahuila, Mexico
Series: ICBioMed Proceedings 2019 · Journal: International Journal of Bioelectronics (ISSN 2448-7732)
Open Access: CC BY 4.0 (post–peer review & technical editing).
Peer Review: Double-blind; at least two independent reviewers.
Abstract
Wireless Sensor Networks (WSN) are composed of autonomous electronic nodes that can collect data from various sensors and transmit information to other nodes. This research studies the RSSI power versus the coverage distance of a Freakduino board in an outdoor environment with obstacles. Three types of sensors were used at different distances between nodes to evaluate performance using transmitter and receiver nodes. Programs were developed in Arduino open-source software to observe node behavior. Results indicate that increasing RSSI signal power by adjusting programming parameters nearly doubles signal strength; however, the communication range did not exceed 180 meters.
Keywords: Wireless Sensor Networks, Received Signal Strength Indicator (RSSI), Freakduino, Nodes, Distance.
Introduction
Wireless sensor networks (WSN) have advanced to support numerous scientific and engineering applications thanks to their small size, low cost, and easy installation compared with wired systems. They are deployed for environmental monitoring, agriculture, and health. Nodes (or motes) integrate sensors, RF communication, data processing, and power modules, forming flexible topologies for various domains. Power efficiency and autonomy are key challenges in WSN design, motivating optimization of consumption through parameter tuning and duty cycling.
Methodology
The study used Freakduino-900 MHz boards (v2.1a and v3.0a) for transmitter and receiver nodes. Node configuration involved PAN ID assignment (0x1234), addressing, and 900 MHz channel selection. Tests were conducted at the Venustiano Carranza Forest, Torreón, Coahuila, with obstacles present. Distances tested: 0, 30, 50, 100, 120, 150, 180, and 200 m. Sensors employed: LM35dz (temperature), DHT11 (temperature & humidity), and FC-28 (soil moisture).
Results
Three stages of testing were performed using the LM35dz, DHT11, and FC-28 sensors. Initial results with LM35dz indicated RSSI values decreasing from 84 (0 m) to 1 (200 m). The DHT11 sensor yielded similar results with a minimum of 6 (200 m). Reconfiguration of Freakduino library parameters (OQPSK modulation) using FC-28 increased RSSI by nearly twofold while maintaining stable readings, though the range still did not surpass 180 m.
Discussion
Parameter tuning improved signal strength but slightly reduced communication range. RSSI-based localization methods remain viable for distance estimation under ideal conditions (low humidity and interference). Environmental factors and Wi-Fi interference affected data transmission within the test site.
Conclusions
It is possible to enhance RSSI power by modifying Freakduino configuration parameters, almost doubling the signal intensity. Nevertheless, distance coverage remained limited to 180 m. These adjustments can benefit obstacle-rich environments if node placement compensates for reduced range. The study reinforces the importance of parameter optimization for reliable WSN design.
Acknowledgements
The authors thank Tecnológico Nacional de México / Instituto Tecnológico de La Laguna (TECNM–ITLaguna) and Consejo Nacional de Ciencia y Tecnología (CONACYT) for facilities and support in developing this research.
References
- Benkic K, Malajner M, Planinsic P, Cucej Z. Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. IWSSIP 2008.
- Bigelow S. Understanding Telephone Electronics. Indianapolis: SAMS; 1991.
- FreakLabs. Freakduino 900 MHz Wireless Arduino Compatible Board v3.0a. Open Source Wireless Sensor Networks. 2018.
- JM Industrial. Data acquisition boards. Available: https://www.jmi.com.mx/tarjetas-de-adquisicion-de-datos.html.
- Srinivasan K, Levis P. RSSI Is Underappreciated. Proc. Third Workshop on EmNets. 2006.
- Texas Instruments. CC1190 DataSheet. Available: http://www.ti.com/product/CC1190.
© 2019 International Journal of Bioelectronics (IJBIOE). Article licensed under CC BY 4.0.