1. Introduction

Recently, agricultural UAV or drones become one of the most useful agricultural instruments utilized in smart farming, especially in the ground sensing applications. The UAV is employed as a mobility hotspot in the area lacking of the communication network to collect the data of soil and corps in order to monitor and control the water management system for precision agriculture. The agricultural UAV with a combination of IoT sensors are preferred. At present, the IoT sensors detect temperature and moisture of the soil and send them to farmers by using the UAV. Therefore, the identification of locations that suffer from droughts, water scarcity,  and dryness of soil profile is easily observed. It helps the farmers to take precautionary methods such as contouring, damming, draining the surface water, and curtailing for further irrigation. Small UAV, either copters or flat-winged models with a combination of IoT sensors are essential.

In [1], the authors firstly introduced UAV which plays a great role in the aerial imagery of soils, particularly soil characteristics. Drones are attractive because they can fly at a low attitude over the fields, as many times as requires in the short intervals to study the same location. The result in [1] showed that the UAV or drones are one of the common methods for obtaining high-resolution maps showing spatial and temporal changes in soil moisture. However, the UAV imagery with low resolution is not used consistently to evaluate a field experiment. In [2], the authors have shown that the precision of soil moisture measurement with the frequency domain reflection (FDR) method was highly accurate. However, the soil moisture FDR sensor type is still expensive.  Therefore, the low-cost capacitive sensor and very low-cost resistive sensors have been investigated in [3]. The authors have presented that low-cost capacitive sensors with soil-specific calibration matched the performance of the secondary standard and could possibly be used for water management with limited effects on irrigation efficiency. Nowadays, most of soil moisture application is based on IoT networking [4] or wireless sensor networking (WSN) [5]. The objective is to automatically obtain the agricultural parameters without requiring  human-to-human or human-to-computer.

2. Related Work

2.1. UAV-SC

Most of the wireless IoT nodes utilized in the agricultural environment limit power energy which leads to the propagation problem in the surrounding area. Interestingly, B. Liu et al. [6] have proposed to solve the resource allocation problem in their UAV and IoT. The UAV works as  power distribution by using a dynamic gamin-based model for wireless power transfer applications. The authors in [6] have illustrated that the power energy transfer of UAV depends on the amount of the density of IoT wireless nodes and they can be controlled by the power by using the dynamic game method. On the other hand, the proposed measurement aided dynamic planning algorithm in [7] can solve the problem-based of multiple IoT connectivity network. This algorithm can help to control the drone small cell (DSC) base station as a self-configuration on the fly regarding the cell being located and channel being used, based on the real-time measurement of network throughput. In addition, more increasingly, an optional mobile backhaul link might use the drone or UAV aided emergency network [8]. However, the problem-based such as the inter-cell interference should be considered. In [9], the multiple DSCs used for the several based stations were optimized by using the equivalent uniform density plane entity (EUDPE) method. The results indicated that the increase of the transmission power of drone did not necessarily improve coverage performance for the IoT connectivity. Similar to the drone base stations (DBSs) in [10], the authors proposed a realistic mathematical model for the joint optimization problem of DBS placement and IoT users. Path loss measurement for an air-to-ground channel was investigated in [10]. For the data monitoring system based on WSN with the large-scale area such as a coast or river [11], the authors considered to maximize data collection with given energy budgets for the drone and sensors. DroneTank [11], the UAV can determine the flight path and speed to conduct data collection. As mentioned in the works of literature, the UAV-SC for ground sensing is very important in the smart farming applications.

2.2. Smart Farming

Recently, the proposed AgriLogger has been implemented in Italy [12]. The aim is to build precision agriculture in the areas lacking data communication networks that can collect data and store the long periods and transmitted agrometeorological data, such as temperature and relative humidity. The drone is equipped with a payload, such as a wireless transceiver, radio transmitter, and GNSS receiver. Additionally, a communication link provides the narrowband IoT (NB-IoT) network to collect the data via cloud management. AgriLogger was able to collect and store for long periods estimated for more than ten years, and consistently, agrometeorological parameters data in a successful way would be  great profit for agriculture. As well-known that the WSN technology is useful for precision agriculture as well as the IoT solution. There are several works focused on using WSN [13,14]. In [15], the authors have presented the technique and practice of precision agriculture. They implied that precision agriculture is to provide decision support systems based on multiple parameters impacted to the crops plantation such as soil moisture, water management, wind speed, and so on, by using WSN-based or IoT-based cloud computing. In [16], a secure IoT-based WSN framework for smart agriculture was described. The simulation results show that the proposed was more efficient compared to the IoT communication reliabilities. The network throughput was increased to 13.5 % and 38.5 % for the packets drop ratio, 13.5 % for the network latency, 16 % for the energy consumption, and 26 % for the routing overheard of smart agriculture.

SmartFarmNet [17] platform is an example application for smart agriculture in Australia by using IoT platform. It provides a scalable sensor data acquisition, analysis, and visualization platform for smart farming applications, based on IoT. Interestingly, this platform measures soil moisture at particular multiple depths and steams data from the physical sensors to monitor a phenomenon. By using the SmartFarmNet gateway to process soil moisture data which is close to the source, the cloud computing performs real-time analysis on incoming sensor data streams. For a platform of precision agriculture, SmartFarmNet is an existing model of smart agriculture based on IoT-based platform. Considering the IoT for smart farming, the IoT platform can help the farmer to predict the disease of the strawberry farm in [18]. However, the problem is the false detection probability of IoT sensors to the gateway. Then, the authors in [19] have presented a solution called SensorTalk to automatically detect the potential sensor failures and semi-automatically calibrate the aging sensors. The solution showed that the false detection probability of IoT sensors to the gateway was reduced to less than 0.7 %.

In related work of smart irrigation, IoT framework for smart irrigation such as data monitoring, preprocessing, fusion, synchronization, storage, and irrigation management enriched by the prediction of soil moisture was presented in [20]. Smart&Green was used to preprocess soil moisture data with outlier removal criteria and techniques of Zscore, MZscore, GESD, and Chauvenet to provide a more precise irrigation water needed in irrigation management. While the propose of IoT for the water management interestingly was presented in [21], WSN, LoRa, and ESP32 modules were used. The system retrieved real-time data and used them to determine the correct amount of water to be used in a garden. With this solution [21], it was possible to save up to 34 % of water with sensor data from temperature, humidity, and soil moisture, or up to 26 % with only temperature inputs. At present, the application of IoT in smart farming is rapidly emerging. A Long-range wide area network (LoRaWAN) based on IoT platform has introduced and called LoRaFarM [22]. The LoRaFarM platform has evaluated on a real farm in Italy. This system can collect data such as air, soil, temperature, and humidity. The advantage of the LoRa network is the long-range communication network and life-long battery of sensors, but it is still needed more gateways in the field. In [22], the design for the soil moisture sensor kit was proposed. The LoRaFarM hardware was designed by using the 1W solar-cell, solar charger, soil sensor, LoRa module at 868 MHz, and LiPo battery.

3. Proposed Model and Implementation

      The GS-UAV-SC model for smart farming applications is shown in Figure 1. It can be noted that the IoT soil moisture GS kits were installed in the field, then, were connected to the UAV-SC to transfer data streaming to the internet. After that, the data are stored and computed on the cloud platform. Additionally,  the farmer/user can control the UAV around the field in order to collect all data from every sensor. User can monitor the data via the application program interface (API) on the mobile application. This proposed model aims to compensate for the low energy from the multiple GSs connectivity.

The structure model of the soil moisture GS prototype is shown in Figure 2. It can be noted that this platform would be used in case of sunlight. Herein, the capacitive soil moisture was used in this study. By using the capacitive soil moisture GS, the capacitance is varied based on the water content present in the soil. The capacitance converted into voltage level basic from 1.2 V to 3.0 V maximum. The advantage of capacitive soil moisture GS which is made of corrosion-resistant material is that it gives  long service life. The structure model of the soil moisture GS prototype consists of five parts:

Figure 1: The GS-UAV-SC model in smart farming application.

Figure 2: The structure model of soil moisture GS prototype.

1. Polycrystalline solar cell panel size is 165 mm wide and 135 mm length. The maximum voltage is 6 V, the maximum current is 583 mA, and the maximum output power is 3.5 W.
2. Voltage regulator is DC to DC up/down LM2596 module, the maximum output current is 3 A, and the size is 50 mm wide and 37 mm
3. Microcontroller ESP8266 NodeMCU is used for controlling data from the soil moisture and transmitting to WiFi internet. The voltage is driven at 3.3 V and current consumption ranges from 70 mA to 240 mA. The transmitting power of microcontroller is 20 dBm, and it supports  11 b/g/n.
4. Capacitive soil moisture sensor SKU: SEN0193, this module includes an on-board voltage regulator which gives an operating voltage range of 1.2 V to 3.0 V. The operating current is 5 mA. This module is an analog sensor which has 3-pins: A0 signal pin, positive pin +, and negative pin -. The depth of soil moisture sensor in the soil is 0.2 m according to the standard as shown Figure 2.
5. The material structure is made from aluminum pipe, and shield is inserted in the circuit box.

The implementation was conducted at the Tropical Animal Research Institute, Ramkhamhaeng University, Thailand. Figure 3(a) shows an example location of Napier grass farm and Ruzi grass farm in Figure 3(b) where the dimension of Napier grass farm is 25 m wide and 45 m length as shown in Figure 3(c) and Ruzi farm is 30 m wide and 50 m length in Figure 3(d). It is noted that these Napier and Ruzi grass are the main food of animals in the smart farm and 50 m of length in Figure 3(d). To note that, these Napier and Ruzi grass are mainly food of the animals within the smart farm.

3.1. Data Processing

The block diagram of data processing is shown in Figure 4. The quadcopter UAV is used for mobility and mounted with the WiFi transceiver. The WiFi transceiver radiates power to the GSs and is connected with the microcontroller ESP8266 NodeMCU module. After receiving the data from the capacitive soil moisture sensor, the ESP8266 NodeMCU transmits data to the internet, cloud computing, and cloud storage based on the Google firebase solution. Eventually, the soil moisture data is alerted to the mobile application.

Figure 3: Smart farming of the Tropical Animal Research Institute, Ramkhamhaeng University: (a) Side view of Napier grass farm; (b) Side view of Ruzi grass farm; (c) Top view of Napier grass dimension; (d) Top view of Ruzi grass dimension.

Figure 4: Block diagram of data processing.

The flowchart of the soil moisture monitoring process is shown in Figure 5. Under the condition of soil moisture, the level of the moisture is set  to be alerted on the mobile application as follows:

• With the show “check” status, all sensors send the data as 0 %. It means that the capacitive soil moisture sensor is disconnected..
• With the show “Dry” status, all sensors send the data as the range between 1 % to 45 %. It means that the soil is dry.
• With the show “Humid” status, all sensors send the data as the range from 46 % to 79 %.  It means that the soil is humid.
• With the show “Wet” status, all sensors send the data as the range between 80 % and 100 % . It means that the soil is wet.

Figure 5: Flowchart of decision.

4. Experimental Setup

      This section demonstrates the experimental setup in both Napier farm and Ruzi farm.

Figure 6: Experimental setup in Napier farm with 12 GSs.

Figure 7: Real-time test in Napier farm.

Figure 8: Experimental setup in Ruzi farm with 15 GSs.

The experimental setup in Napier farm is demonstrated in Figure 6. The GSs are installed along the line of drip irrigation water systems in the field. The separation distance of each sensor is 10 m, and the position names are Napier11 to Napier34. Real-time test in Napier farm is shown in Figure 7. The flight time of the UAV is approximately 20 minutes, and the altitude is varied from 1 m to 10 m. For the sensor depth, it is a pin into the soil with about 0.2 m depth. The tested soil moisture GS kits are performed by the power supply from the solar cell panel in the day time. Therefore, the farmer/user can monitor the data in real time..

      Additionally, the experimental setup in Ruzi farm is shown in Figure 8 and real-time test in Figure 9. It demonstrates the position of sensors in the field in which the number of GS is 15 positions. The GS sets as Ruzi11 to Ruzi35. The sensors installed along the line of the water sprinkler in the field. At the experiment, the UAV altitude was also varied from 1 m to 10 m. Note that the average temperature of all days of these locations was 40 degrees. As a result, both the Napier and Ruzi farms need more water spraying every day. Therefore, the GS-UAV-SC model is useful for the farmer/user to automatically control the on-off of the water management system in these agricultural farms.

Figure 9: Real-time test in Ruzi farm.

5. Results and Discussion

The experimental data tests were performed in the daytime on April 2, 2020, and during 2 PM to 5:15 PM. Table 1 and Table 2 show the real-time soil moisture values before and after the opening of the drip irrigation water system in Napier farm, monitoring it as a percentage. Meanwhile, the real-time soil moisture monitoring in Ruzi farm is presented in Table 3 and Table 4, respectively. Herein, the WiFi network is connected by the UAV-SC in every fifteen minutes.

Table 1: In case: Before opening the water system in Napier farm.

Table 2: In case: After opening the water system in Napier farm.

Table 3: In case: Before opening the water system in Ruzi farm.

Table 4: In case: After opening the water system in Ruzi farm.

Before the opening of the drip irrigation water system in Napier grass farm, soil moisture sensors were tested from 2 PM to 3:15 PM. The results of soil moisture sensors show that the data is in the range from 22 % to 42 % of the real-time monitoring. Therefore, the status was Dry status. After the opening of the drip irrigation water system for about forty-five minutes, it is observed that the soil moisture values were rapidly increased in the range of 45 % to 69 % as shown in Table 2. Thus, the “Humid” status was shown on the mobile application.

In Ruzi farm, the soil moisture values in Ruzi farm were in the range from 12 % to 34 % in Table 3 before opening the water sprinkler system, Therefore, the status showed as Dry. After the opening water sprinkler system about forty-five minutes in Ruzi farm, the results of soil moisture values were in the range from 38 % to 81 %. As a result, the status shown on mobile application was Dry, Humid, Wet, and Check status. And then, the capacitive soil moisture sensors were removed  from the soil. Both Ruzi 34 and Ruzi 35 were  0 % with the “Check” status. The experiment data in Ruzi farm were collected on April 3, 2020.

The examples of mobile application on screen as real-time monitoring were shown in Figure 10 (a)-(b). The data collected  at 4 PM is shown  in Table 2 and Table 4. Figure 10 (a) and 10 (b) show the status of soil moisture values in Napier farm and Ruzi farm. The application is easily used and can be applied to control the water management (on-off) system both in Napier and Ruzi farms. It took just to 15 minutes to collect the data for the proposed model.

Figure 10: Example of real-time monitoring on mobile application at 4 PM:

 (a) On the screen in Napier farm. (b) On the screen in Ruzi farm.

6. Conclusion

In this paper, the proposed GS-UAV-SC model based on IoT and wireless communication connected network in smart farming has been implemented for soil moisture monitoring. The low-cost multiple GSs were designed as the ground sensing to monitor the soil moisture in the daylight both for Napier grass farm and Ruzi grass farm. The UAV-SC is equipped with the WiFi transceiver similar to the hotspot mobility and connected with the multiple GSs. This proposed smart farming system is called the GS-to-UAV-SC communication system. The implementation and results confirm that the UAV-SC can be applied as mobility transceiver to compensate for the low energy of GSs, including the real-time data that are designed to monitor soil moisture values on the mobile application. In future work, the optimization of path loss model for smart farming (SF) system will be implemented by the machine learning algorithms.            .

Conflict of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgment

This research was financially supported by the Academic Melting Pot, KMITL research fund. The authors would like to thank the Tropical Animal Research Institute, Ramkhamhaeng University, in Thailand, which was cooperative research in this work.

1. C. M. Gevaert, J. Suomalainen, J. Tang, L. Kooistra, “Generation of Spectral-Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imaginary for Precision Agricultural Applications” IEEE J. of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3140–3146, 2015. https://doi.org/10.1109/JSTARS.2015.2406339
2. L. Chen, L. Zhangzhong, W. Zheng, J. Yu, Z. Wang, L. Wang, C. Huang “Data-Driven Calibration of Soil Moisture Sensor Considering Impacts of Temperature: A Case Study on FDR Sensors” Sensors, 19(20), 1–11, 2019. https://doi.org/10.3390/s19204381
3. S. Adla, N. K. Rai, S. H. Karumanchi, S. Tripathi, M. Disse, S. Pande “Laboratory calibration and performance evaluation of low-cost capacitive and very low-cost resistive soil moisture sensors” Sensors, 20(2), 1–27, 2020. https://doi.org/10.3390/s20020363
4. W. L. Chen, Y. B. Lin, Y. W. Lin, R. Chen, J. K. Liao, F. L. Ng, Y. Y. C. Liu, C. C. Wang, C. H. Chiu, T. H. Yen, “AgriTalk: IoT for Precision Soil Farming of Turmeric Cultivation” IEEE Internet of Things J., 6(3), 5209–5223, 2019. https://doi.org/10.1109/JIOT.2019.2899128
5. F. Viani, M. Bertolli, A. Polo, “Low-Cost Wireless System for Agrochemical Dosage Reduction in Precision Farming” IEEE Sensors J., 17(1), 5–6, 2017. https://doi.org/10.1109/JSEN.2016.2622244
6. B. Liu, H. Xu, X. Zhou, “Resource Allocation in Unmanned Aerial Vehicles (UAV)-Assisted Wireless-Powered Internet of Things” Sensors, 19(8), 1–13, 2019. https://doi.org/10.3390/s19081908
7. N. Lu, Y. Zhon, C. Shi, N. Cheng, B. Li, “Planning While Flying: A Measurement-Aided Dynamic Planning of Drone Small Cells” IEEE Internet of Things J., 6(2), 2693–2705, 2019. https://doi.org/ 10.1109/JIOT.2018.2873772
8. G. Castellanos, M. Deruyck, L. Martens, W. Joseph, “Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks” Sensors, 19(15), 1–16, 2019. https://doi.org/10.3390/s19153342
9. C. Dong, J. Xie, H. Dai, Q. Wu, Z. Qin, Z. Feng “Optimal Deployment Density for Maximum Coverage of Drone Small Cells” China Communications, 15(5), 25–40, 2018. https://doi.org/10.1109/CC.2018.8387984
10. A. Ahmed, M. Awais, T. Akram, S. Kulac, M. Alhussein, K. Aurangzeb, “Joint Placement and Device Association of UAV Base Stations in IoT Networks” Sensors, 8(6), 1–16, 2019. https://doi.org/10.3390/s19092157
11. R. Xoing, F. Shan, “DroneTank: Planning UAVs Flight and Sensors Data Transmission under Energy Constraints” Sensors, 18(9), 1–19, 2018. https://doi.org/10.3390/s18092913
12. M. Idbella, M. Iadaresta, G. Gagliarde, A. Mennella, S. Mazzoleni, G. Bonanomi, “AgriLogger: A New Wireless Sensor for Monitoring Agrometeorological Data in Areas Lacking Communication Networks” Sensors, 20(6), 1–13, 2020. https://doi.org/10.3390/s20061589
13. M. Bacco, A. Berton, A. Gotta, L. Caviglione, “IEEE 802.15.4 Air-Ground UAV Communications in Smart Farming Scenarios” IEEE Communication L. 22(9), 1910–1913, 2018. https://doi.org/10.1109/LCOMM.2018.2855211
14. F. Viani, M. Bertolli, A. Polo, “Low-Cost Wireless System for Agrochemical Dosage Reduction in Precision Farming” IEEE Sensors J., 17(1), 5–6, 2017. https://doi.org/10.1109/JSEN.2016.2622244
15. U. Shafi, R. Mumtaz, J. Garcia-Nieto, S. A. Hassan, S. A. Raza Zaidi, N. Iqbal, “Precision Agriculture Techniques and Practices: From Consideration to Applications” Sensors, 19(17), 1–25, 2019. https://doi.org/10.3390/s19173796
16. K. Haseeb, I. U. Din, A. Almogren, N. Islam, “An Energy Efficient and Secure IoT-based WSN Framework: An Application to Smart Agriculture” Sensors, 20(7), 1–14, 2020. https://doi.org/10.3390/s20072081
17. P. P. Jayaraman, A. Yavari, D. Geogakopoulos, A. Morshed, A. Zaslavsky, “Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt” Sensors, 16(11), 1–17, 2016. https://doi.org/10.3390/s16111884
18. S. Kim, M. Lee, C. Shin, “IoT-based Strawberry Disease Prediction System for Smart Farming” Sensors, 18(11), 1–17, 2018. https://doi.org/ 10.3390/s18114051
19. Y. B. Lin, Y. W. Lin, J. Y. Lin, H. N. Hung, “SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming” Sensors, 19(21), 1–19, 2019. https://doi.org/10.3390/s19214788
20. N. G. S. Campos, A. R. Rocha, R. Gondim, T. L. Coelho da Sliva, D. G. Gomes, “Smart & Green: An Internet-of-Things Framework for Smart Irrigation” Sensors, 20(1), 1–25, 2020. https://doi.org/10.3390/s20010190
21. A. Gloria, C. Dionisio, G. Simoes, J. Cardoso, P. Sebastiao, “Water Management for Sustainable Irrigation Systems using Internet-of-Things” Sensors, 20(5), 1–14, 2020. https://doi.org/10.3390/s20051402
22. G. Codelippi, A. Cilfone, L. Davoli, G. Ferrari, “LoRaFarM: a LoRaWAN-based Smart Farming Modular IoT Agriculture” Sensors, 20(7), 1–24, 2020. https://doi.org/10.3390/s20072028

Citations by Dimensions

Citations by PlumX

Google Scholar

Crossref Citations

1. Amalia Rodriguez Espinoza de los Monteros, Maximo Giovani Tandazo Espinoza, Byron Ivan Punina Cordova, Ronald Eduardo Tandazo Vanegas, "IoT and Business Intelligence Based Model Design for Liquefied Petroleum Gas (LPG) Distribution Monitoring", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 79–92, 2024. doi: 10.25046/aj090409
2. Stefania Nanni, Massimo Carboni, Gianluca Mazzini, "From Sensors to Data: Model and Architecture of an IoT Public Network", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 14–20, 2024. doi: 10.25046/aj090403
3. Sheikh Tareq Ahmed, Annamalai Annamalai, Mohamed Chouikha, "Strengthening LoRaWAN Servers: A Comprehensive Update with AES Encryption and Grafana Mapping Solutions", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 33–41, 2024. doi: 10.25046/aj090104
4. Alioune Cisse, Ousmane Diallo, EL Hadji Malick Ndoye, Mamadou Sy, Ousseynou Sene, Joel José Puga Coelho Rodrigues, "A Smart Farming Management System based on IoT Technologies for Sustainable Agriculture", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 1–8, 2024. doi: 10.25046/aj090101
5. Nizar Sakli, Chokri Baccouch, Hedia Bellali, Ahmed Zouinkhi, Mustapha Najjari, "IoT System and Deep Learning Model to Predict Cardiovascular Disease Based on ECG Signal", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 08–18, 2023. doi: 10.25046/aj080602
6. Abdulwahid Mohammed, Mohamed S. Elbakry, Hassan Mostafa, Abdelhady Abdelazim Ammar, "Doubling the Number of Connected Devices in Narrow-band Internet of Things while Maintaining System Performance: An STC-based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 01–10, 2023. doi: 10.25046/aj080401
7. Chanuka Bandara, Yehan Kodithuwakku, Ashan Sandanayake, R. A. R. Wijesinghe, Velmanickam Logeeshan, "Design and Implementation of an Automated Medicinal-Pill Dispenser with Wireless and Cellular Connectivity", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 161–169, 2023. doi: 10.25046/aj080318
8. Jérémy Quignon, Anthony Tornambe, Thibaut Deleruyelle, Philippe Pannier, "Antenna System Design To Increase Power Transfer Efficiency with NFC Wireless Charging Technology", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 123–128, 2022. doi: 10.25046/aj070314
9. Ming Fong Sie, Jingze Wu, Seth Austin Harding, Chien-Lung Lin, San-Tai Wang, Shih-wei Liao, "Secured Multi-Layer Blockchain Framework for IoT Aggregate Verification", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 106–115, 2022. doi: 10.25046/aj070312
10. Osaretin Eboya, Julia Binti Juremi, "iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 185–202, 2021. doi: 10.25046/aj060521
11. Antonio Casquero Jiménez, Jorge Pérez Martínez, "Remote Patient Monitoring Systems with 5G Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 44–51, 2021. doi: 10.25046/aj060406
12. Niranjan Ravi, Mohamed El-Sharkawy, "Enhanced Data Transportation in Remote Locations Using UAV Aided Edge Computing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 1091–1100, 2021. doi: 10.25046/aj0602124
13. Murtadha Arif Bin Sahbudin, Chakib Chaouch, Salvatore Serrano, Marco Scarpa, "Application-Programming Interface (API) for Song Recognition Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 846–859, 2021. doi: 10.25046/aj060298
14. Athanasios Tziouvaras, Georgios Dimitriou, Michael Dossis, Georgios Stamoulis, "Frequency Scaling for High Performance of Low-End Pipelined Processors", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 763–775, 2021. doi: 10.25046/aj060288
15. Mochammad Haldi Widianto, Ari Purno Wahyu, Dadan Gusna, "Prototype Design Internet of Things Based Waste Management Using Image Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 709–715, 2021. doi: 10.25046/aj060282
16. Bismark Tei Asare, Kester Quist-Aphetsi, Laurent Nana, "Node-Node Data Exchange in IoT Devices Using Twofish and DHE", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 622–628, 2021. doi: 10.25046/aj060271
17. Khadija Alaoui, Mohamed Bahaj, "Categorization of RDF Data Management Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 221–233, 2021. doi: 10.25046/aj060225
18. Shahenaz S. Abou Emira, Khaled Y. Youssef, Mohamed Abouelatta, "Simulated IoT Based Sustainable Power System for Smart Agriculture Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1030–1039, 2021. doi: 10.25046/aj0601114
19. Angelina, Weliati, Edwin Christian Jonatan Wardoyo, Sugiarto Hartono, "Mobile Application Design for Student Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 776–782, 2021. doi: 10.25046/aj060186
20. Amin S. Ibrahim, Khaled Y Youssef, Mohamed Abouelatta, "Traffic Aggregation Techniques for Optimizing IoT Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 509–518, 2021. doi: 10.25046/aj060156
21. Arman Mirmanov, Aidar Alimbayev, Sanat Baiguanysh, Nabi Nabiev, Askar Sharipov, Azamat Kokcholokov, Diego Caratelli, "Development of an IoT Platform for Stress-Free Monitoring of Cattle Productivity in Precision Animal Husbandry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 501–508, 2021. doi: 10.25046/aj060155
22. Fang-Lin Chao, Wei Zhong Feng, Kaiquan Shi, "Smart Collar and Chest Strap Design for Rescue Dog through Multidisciplinary Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 386–392, 2021. doi: 10.25046/aj060144
23. Vítor Viegas, J. M. Dias Pereira, Pedro Girão, Octavian Postolache, "Study of latencies in ThingSpeak", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 342–348, 2021. doi: 10.25046/aj060139
24. Zainatul Yushaniza Mohamed Yusoff, Mohamad Khairi Ishak, Kamal Ali Alezabi, "The Role of RFID in Green IoT: A Survey on Technologies, Challenges and a Way Forward", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 17–35, 2021. doi: 10.25046/aj060103
25. Shahenda S. Abou Emira, Khaled Y. Youssef, Mohamed Abouelatta, "Design of Power Efficient Routing Protocol for Smart Livestock Farm Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1719–1726, 2020. doi: 10.25046/aj0506205
26. Afsah Sharmin, Farhat Anwar, S M A Motakabber, "Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1710–1718, 2020. doi: 10.25046/aj0506204
27. Hasan Tariq, Abderrazak Abdaoui, Farid Touati, Mohammad Abdullah Al Hitmi, Damiano Crescini, Adel Ben Mnaouer, "Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1666–1673, 2020. doi: 10.25046/aj0506198
28. Nesma N. Gomaa, Khaled Y. Youssef, Mohamed Abouelatta, "On Design of IoT-based Power Quality Oriented Grids for Industrial Sector", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1634–1642, 2020. doi: 10.25046/aj0506194
29. Chinedu Wilfred Okonkwo, Magda Huisman, Estelle Taylor, "A Framework for Adoption and Diffusion of Mobile Applications in Africa", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1577–1592, 2020. doi: 10.25046/aj0506189
30. Tarek Frikha, Hedi Choura, Najmeddine Abdennour, Oussama Ghorbel, Mohamed Abid, "ESP2: Embedded Smart Parking Prototype", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1569–1576, 2020. doi: 10.25046/aj0506188
31. Alexi Delgado, Enrique Lee Huamaní, Alfredo Chiara-Sotomayor, Florencio Roman-Casahuamán, "Design of a Mobile Application for the School Enrollment Process in Order to Prevent Covid-19", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1042–1046, 2020. doi: 10.25046/aj0506126
32. Mohammed Hadwan, Rehan Uallah Khan, Khalil Ibrahim Mohammad Abuzanouneh, "Towards a Smart Campus for Qassim University: An Investigation of Indoor Navigation System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 831–837, 2020. doi: 10.25046/aj050699
33. Naeem Ahmed Haq Nawaz, Hamid Raza Malik, Ahmed Jaber Alshaor, Kamran Abid, "A Simulation Based Proactive Approach for Smart Capacity Estimation in the Context of Dynamic Positions and Events", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 423–438, 2020. doi: 10.25046/aj050651
34. Yevhen Fediv, Olha Sivakova, Mykhailo Korchak, "Multi Operated Virtual Power Plant in Smart Grid", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 256–260, 2020. doi: 10.25046/aj050630
35. Muhammad Usman Ali Khan, Raad Raad, Javad Foroughi, "Transient Response & Electromagnetic Behaviour of Flexible Bow-Tie Shaped Chip-less RFID Tag for General IoT Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 757–764, 2020. doi: 10.25046/aj050592
36. Nghia Duong-Trung, Nga Quynh Thi Tang, Xuan Son Ha, "Interpretation of Machine Learning Models for Medical Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 469–477, 2020. doi: 10.25046/aj050558
37. Gulam, Afan Galih Salman, Bayu Kanigoro, "Learning Tool for Kids on Android Platform", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 212–216, 2020. doi: 10.25046/aj050526
38. Deepti Sehrawat, Nasib Singh Gill, "IoT Based Human Activity Recognition System Using Smart Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 516–522, 2020. doi: 10.25046/aj050461
39. Olayan Alharbi, "Industry 4.0 Operators: Core Knowledge and Skills", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 177–183, 2020. doi: 10.25046/aj050421
40. Kanishk Rai, Keshav Kumar Thakur, Preethi K Mane, Narayan Panigrahi, "Design of an EEG Acquisition System for Embedded Edge Computing", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 119–129, 2020. doi: 10.25046/aj050416
41. Alexi Delgado, Enrique Lee Huamaní, Elizabeth Jenny Cortez-De La Peña, "A Mobile Application Design to Prevent Criminal Acts in Lima, Peru", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 40–46, 2020. doi: 10.25046/aj050406
42. Maximo Giovani Tanzado Espinoza, Joseline Roxana Neira Melendrez, Luis Antonio Neira Clemente, "A Survey and an IoT Cybersecurity Recommendation for Public and Private Hospitals in Ecuador", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 518–528, 2020. doi: 10.25046/aj050364
43. Zuhri Syarifudin, Suharjito, "Mobile Based for Basic English Learning Assessment with Augmented Reality", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 774–780, 2020. doi: 10.25046/aj050297
44. Rajesh Kannan Megalingam, Santosh Tantravahi, Hemanth Sai Surya Kumar Tammana, Nagasai Thokala, Hari Sudarshan Rahul Puram, Naveen Samudrala, "ROS Based Multimode Control of Wheeled Robot", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 688–696, 2020. doi: 10.25046/aj050285
45. Nuno Martins, Jéssica Campos, Ricardo Simoes, "Activerest: Design of A Graphical Interface for the Remote use of Continuous and Holistic Care Providers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 635–645, 2020. doi: 10.25046/aj050279
46. ?ahin Aydin, Mehmet Nafiz Aydin, "A Sustainable Multi-layered Open Data Processing Model for Agriculture: IoT Based Case Study Using Semantic Web for Hazelnut Fields", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 309–319, 2020. doi: 10.25046/aj050241
47. Mihaela Balanescu, George Suciu, Marius-Alexandru Dobrea, Cristina Balaceanu, Radu-Ioan Ciobanu, Ciprian Dobre, Andrei-Cristian Birdici, Andreea Badicu, Iulia Oprea, Adrian Pasat, "An Algorithm to Improve Data Accuracy of PMs Concentration Measured with IoT Devices", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 180–187, 2020. doi: 10.25046/aj050223
48. Ery Muchyar Hasiri, Asniati, Mohamad Arif Suryawan, Rasmuin, "The Implementation of Smart Farming Application Based on the Microcontroller and Automatic Sprinkler Irrigation System of Agricultural Land", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 174–179, 2020. doi: 10.25046/aj050222
49. Nour Mostafa, "Resource Selection Service Based on Neural Network in Fog Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 408–417, 2020. doi: 10.25046/aj050152
50. Samruan Wiangsamut, Phatthanaphong Chomphuwiset, Suchart Khummanee, "Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 163–173, 2019. doi: 10.25046/aj040522
51. Alghamdi Abdullah, Mohammed Thanoon, Anwar Alsulami, "Toward a Smart Campus Using IoT: Framework for Safety and Security System on a University Campus", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 97–103, 2019. doi: 10.25046/aj040512
52. Anang Hudaya Muhamad Amin, Nazrul Muhaimin Ahmad, Subarmaniam Kannan, "Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 256–264, 2019. doi: 10.25046/aj040432
53. Tlija Amira, Istrate Dan, Badii Atta, Gattoufi Said, Bennani Az-eddine, Wegrzyn-Wolska Katarzyna, "Stress Level Classification Using Heart Rate Variability", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 38–46, 2019. doi: 10.25046/aj040306
54. Amir Rizaan Rahiman, Md. Ashikul Islam, Md. Noor Derahman, "Resourceful Residual Energy Consumption in TDMA Scheduling for IoT-based Wireless Sensor Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 31–37, 2019. doi: 10.25046/aj040305
55. Iryna Zhuravska, Dmytro Lernatovych, Oleksandr Burenko, "Detection the Places of the Heat Energy Leak on the Underground Thermal Pipelines Using the Computer System", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 01–09, 2019. doi: 10.25046/aj040301
56. Imtiaz Parvez, Arif I. Sarwat, "A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 70–77, 2019. doi: 10.25046/aj040209
57. Shruthi Narayanaswamy, Anitha Vijaya Kumar, "Application Layer Security Authentication Protocols for the Internet of Things: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 317–328, 2019. doi: 10.25046/aj040131
58. João Manuel Leitão Pires Caldeira, Vasco Nuno da Gama de Jesus Soares, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar, Joel José Puga Coelho Rodrigues, Ricardo Manuel Valentim Fontes, José Luís Lopes Silva, "iSensA – A System for Collecting and Integrating Sensor Data", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 213–221, 2018. doi: 10.25046/aj030628
59. Lin Dong, Akira Rinoshika, "Analysis and Methods on The Framework and Security Issues for Connected Vehicle Cloud", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 105–110, 2018. doi: 10.25046/aj030611
60. Procheta Nag, Saikat Mondal, Arun More, "A Holistic User Centric Acute Myocardial Infarction Prediction System With Model Evaluation Using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 56–66, 2018. doi: 10.25046/aj030605
61. Helen Hasenfuss, Muftah Fraifer, Sameer Kharel, Asma Elmangoush, Alan Ryan, Walid Elgenaidi, "It Takes Two to Tango: Merging Science and Creativity to Support Continued Innovation in the IoT Domain", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 82–91, 2018. doi: 10.25046/aj030511
62. Kun Zhang, Liu Liu, Cheng Tao, Ke Zhang, Ze Yuan, Jianhua Zhang, "Wireless Channel Measurement and Modeling in Industrial Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 254–259, 2018. doi: 10.25046/aj030425
63. Vittorio Miori, Dario Russo, Luca Ferrucci, "Supporting Active Aging Through A Home Automation Infrastructure for Social Internet of Things", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 173–186, 2018. doi: 10.25046/aj030415
64. Tsz Hei Yeung, Chi Kit Ng, Vincent Ng, "A Smart Mobile Application for Assisting Parents in Anti-Drug Support", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 434–442, 2018. doi: 10.25046/aj030153
65. Davar Pishva, "IoT: Their Conveniences, Security Challenges and Possible Solutions", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1211–1217, 2017. doi: 10.25046/aj0203153
66. Stefania Nanni, Elisa Benetti, Gianluca Mazzini, "Indoor monitoring in Public Buildings: workplace wellbeing and energy consumptions. An example of IoT for smart cities application", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 884–890, 2017. doi: 10.25046/aj0203110
67. Muftah Fraifer, Mikael Fernström, "Designing a Smart Car Parking System (PoC) Prototype Utilizing CCTV Nodes: A vision of an IoT parking system via UCD process", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 755–764, 2017. doi: 10.25046/aj020396
68. Jorge Oliveira e Sá, João Cacho Sá, José Luís Pereira, Francisco Pimenta, Manuel Monteiro, "Internet of Things: An Evolution of Development and Research area topics", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 240–247, 2017. doi: 10.25046/aj020129