## 4 Proposed MCA-IoT Design
### MCA-IoT Supervisory Software Development Phases and Implementation
**Activation and Configuration:** The MCA-IoT network participants were activated, clusters were formed, and a 3-h sampling schedule was established using the onboard crontab-e of the Raspberry Pi 3 B+. 3. **Data Transmission and Processing:** During the experimental phase, the mobile nodes (MNs) transmitted sampled data to the base station (BS) through their respective cluster heads (CHs), relay CHs (RCHs), and edge CHs for processing, decision making, TTN cloud database updates, and irrigation system control relay actuation. 4. **Performance Evaluation:** The network's performance stability was assessed over a complete farming season lasting 3 months, during which the aforementioned steps were repeated to account for extreme climatic conditions and variable participant activity durations. The final MCA-IoT network, comprising four MNs per CH, two distant CHs, two RCHs, two ECHs, and one BS, in the two deployment modes, is illustrated in Figure 14.

### Results and Discussion

Since all the aforementioned post-deployment performance parameters culminate in a node's availability or lifespan, this section discusses the key modalities for ensuring energy efficiency. With the aid of the UM25C, Type-C USB 2.0 Full-Color LCD Multimeter Tester, participants' principal performance parameters, such as energy, current, voltage, power, and ambient temperature in both \({}^{\circ}\)C and \({}^{\circ}\)F, were remotely monitored and recorded via this Bluetooth-enabled USB dongle placed between the power supply and RPi-based MN, CH, RCH, and edge CH (ECH). The ECH receives aggregated data from the RCH via the BLE and forwards it to the BS via a LoRa communication device. This UM25C dongle comes with PC software that offers a real-time graphical view of these parameters and can extract them into.csv files for further analysis. The initial deployment and prototype iterations were conducted in the laboratory for a month, as illustrated in Figure 13(a). Afterward, the proposed MCA-IoT technology was deployed on the project implementation farm, as shown in Figure 13(b). Although the technology was designed to mitigate drought challenges and support year-round farming, this study evaluated performance only during the dry season. The impact of environmental conditions on the network's performance and potential risk factors remain to be fully understood. Throughout the three-month deployment period, the system continuously transmitted data to the TTN cloud without any network participant failures. With the aid of our mobile app, as shown in Figure 16, and sample real-time soil temperature and moisture data, as shown in Figure 17, the farmer could view the farm's

Figure 15: A map showing the experimental setup of the proposed MCA-IoT network. conditions and send a GSM-based message to operate the irrigation system, as illustrated in Figure 18a. This ensured that the correct amount of water was applied to the crops at the optimal time. Consequently, the Crop Department of Ghana's Ministry of Food and Agriculture, Tarkwa Nsuaem Unit, confirmed that our farm produce was of comparatively better quality. Samples of the vegetables harvested from our farm are shown in Figure 18b, and the crop quality is evident. Figure 16: A mobile app showing real-time moisture data from a farm. Unlike related Agri-IoT studies [3; 5; 14; 15; 19; 25; 60; 61; 62; 63; 64] that only focus on developing workable real-world Agri-IoT solutions using classical IoT design principles and technologies without incorporating real-world performance data sampling and data processing, this study proposed and custom-built a robust, affordable, context-relevant, and globally significant MCA-IoT remedy with prototypes and unique indoor and outdoor performance results.