Soil Energy Harvesting and Its Review on WSN Node Operation

Ramadhan, Eko; Hakim, Galang P. N.; Hajar, Muhammad Hafizd Ibnu; Firdausi, Ahmad

doi:10.1109/isesd53023.2021.9501765

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can calculate how many days WSN network will be running based on node 18 power profile. If we take node 18 the most far coverage, and current measurement for soil temperature monitoring application [55] with battery capacity at 3000 mAH, then we can calculated the WSN network lifetime. The WSN network lifetime based on node 18 power profile can be seen in figure 12 below: As we see from figure 12 above, the ANFIS near ground and the LM-ANN adaptive transmission power control model has managed to do power conservation and in the end has managed to increase WSN node lifetime.…”

Section: Evaluation Using Wsn Node Lifetime Parametermentioning

confidence: 99%

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

To facilitate wireless sensor networks deployment in dense jungle environments, the challenges of unreliable wireless communication links used for routing data between nodes and the gateway, and the limited battery energy available from the nodes must be overcome. In this paper, we introduce the Self-Organized Wireless Sensor Network (SOWSN) to overcome these challenges. To develop the traits needed for such SOWSN nodes, three types of computational intelligence mechanisms have been featured in the design. The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. The second feature is the introduction of the fuzzy logic ANFIS-optimized Near Ground Propagation Model to predict the wireless transmission link quality and power transfer between transmitters. The third feature is the introduction of the (Levenberg Marquardt artificial neural network (LM-ANN) for Adaptive Dynamic Power Control to further optimize the transmitter power levels, radio modulation, Spreading Factor configurations, and settings of the employed SOWSN LoRaWAN nodes based on predicted wireless transmission link quality parameters. The introduced features were extensively evaluated and analyzed using simulation and empirical measurements. Using clustering, near-ground propagation, and adaptive transmission power control features, a robust wireless data transmission system was built while simultaneously providing power conservation in SOWSN operation. The payload loss can be improved using SAW clustering from 1275 bytes to 5100 bytes. The result of power conservation can be seen from the reduction of transmission power in SOWSN nodes with the increase of transmission time (TOA) as its side effect. With the original power transmission at 20 dBm, original TOA time at 96.832 milliseconds for all nodes, and SNR 3 as input, transmission power was reduced to 12.76 dBm and the TOA increased to 346.78 milliseconds for all nodes.

show abstract

Section: Evaluation Using Wsn Node Lifetime Parametermentioning

confidence: 99%