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Abstract The Wireless Sensor Network needs to become a dynamic and adaptive network to conserve energy stored in the wireless sensor network node battery. This dynamic and adaptive network sometimes are called SON (Self Organizing Network). Several SON concepts have been developed such as routing, clustering, intrusion detection, and other. Although several SON concepts already exist, however, there is no concept for SON in dynamic radio configuration. Therefore, the authors’ contribution to this field would be proposing a dynamic and adaptive Wireless Sensor Network node radio configuration. The significance of their work lies in the modelling of the SON network that builds based on our measurement in the real‐world jungle environment. The authors propose input parameters such as SNR, the distance between the transmitter and receiver, and frequency as the static parameter. For adaptive parameters, we propose bandwidth, spreading factor, and its most important parameter such as power for data transmission. Using the Levenberg Marquardt Artificial Neural Network (LM‐ANN) self‐organise Network model, power reduction and optimisation from 20 dBm to 14.9 dBm for SNR 3, to 11.5 dBm for SNR 6, and to 12.9 dBm for SNR 9 all within a 100‐m range can be achieved. With this result, the authors conclude that we can use LM‐ANN for the wireless sensor network SON model in the jungle environment.

This study presents a new improved voltage gain dc-dc converter architecture to maximize solar photovoltaic (PV) power output. The maximum power point tracking (MPPT) method utilizes particle swarm optimization (PSO)–based artificial neural networks (ANN) to reduce the oscillations of output electrical performance at the maximum power point (MPP). For any solar cell temperature and irradiance, the ANN delivers the electrical current and voltage outputs at the MPP and thus could reach the MPP in minimum instances. A DC-DC converter needs specific characteristics to work with photovoltaic systems. These include higher voltage development to meet increased DC link voltage requirements, the continuous input current to extend the PV system life, common grounding to prevent electromagnetic interference, and reduced electrical stress with fewer components. This paper combines a quasi-switched switch inductor network and extendable diode capacitor modules to achieve the quality mentioned earlier. The converter offers constant input current, high efficiency (95 %) with considerable gain (12 times higher than input), lower voltage stress (almost one-fifth of output voltage), and a common grounding feature. The effectiveness of the proposed MPPT technique is analyzed in terms of higher efficiency, MPP tracking time, gain, and the normalized voltage stress on diodes. The experimental step is developed to validate the simulation (Simulink and Psim) results of the present research work.

DC-DC converters with significant gain, ripple-free input current, and shared ground are required to elevate the output voltages of batteries, fuel cells, and Photovoltaic sources. The proposed topology utilizes a solitary switch to control the circuit and it has additional inculpation of a voltage doubler cell at the load side, a switch capacitor cell in the middle, and a quadratic cell at the output side. These cascaded configurations lead to significant voltage gains at moderate duty cycle rates. Additionally, the voltage stress over the power components is negligible, coming in under one-third of the resultant voltage. Moreover, the number of cells at the input and output side can be extended to obtain high voltage according to the requirements of the load. The gain in voltage, efficiency, and normalized voltage stress of the semiconductor elements in the circuit are examined concerning other solutions found in the literature. Eventually, photovoltaic and battery sources were included to analyze the proposed topology to confirm the circuit’s multifaceted functionality. The circuit was developed for 270 W, 440 V output from 36 V input, and a 40 kHz switching pulse was used to drive the switch. The theoretical and simulation analysis states that incorporating photovoltaic and other sources did not deteriorate the transformation efficiency. Simulink and PSIM analysis found that the circuit successfully transferred power from source to load. ABSTRAK: Penukar DC-DC yang mempunyai gandaan ketara, input arus bebas riak dan pembumi berkongsi penting bagi meningkatkan voltan keluar bateri, sel bahan api dan sumber fotovolta. Topologi yang dicadangkan ini menggunakan suis tersendiri bagi mengawal litar dan ia mengandungi sel pendua voltan tambahan bagi menghentikan arus di bahagian beban, sel suis kapasitor di tengah dan sel kuadratik di bahagian voltan keluar. Konfigurasi berturutan ini membawa kepada gandaan voltan ketara pada kadar kitar tugas sederhana. Tambahan, tekanan voltan ke atas komponen kuasa boleh diabaikan, iaitu satu pertiga daripada voltan terhasil. Selain itu, bilangan sel di bahagian kemasukan dan keluaran arus boleh dilanjutkan bagi mendapatkan voltan tinggi mengikut keperluan beban. Gandaan voltan, kecekapan dan tekanan voltan ternormal pada bahan dalam litar semikonduktor diperiksa dengan menyamai penyelesaian lain yang ditemui dalam kajian terdahulu. Akhirnya, sumber fotovolta dan bateri dimasukkan bagi menganalisis topologi yang dicadangkan bagi mengesahkan fungsi pelbagai rupa litar. Litar yang dibangunkan ini digunakan pada kuasa 270 W, pada aras voltan 440 V dengan kemasukan voltan 36 V dan suis operasi berfrekuensi 40 kHz. Analisis teori dan simulasi menyatakan bahawa gabungan fotovolta dan sumber lain tidak mengurangkan kecekapan transformasi. Analisis Simulink dan PSIM mendapati litar ini berjaya memindahkan 95% kuasa dari sumber kepada beban.

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 and 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.

In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models.