## 4 Proposed MCA-IoT Design
### MCA-IoT Supervisory Software Development Phases and Implementation
Since the effects of all the critical design metrics (i.e., FM, adaptability, and power consumption optimization) were revealed in the SN lifespan (SN out-of-service) and data outliers [10], of which the latter were auto-addressed by the embedded routing protocol, the power depletion rate or SN lifespan remained our principal real-time performance metric. The power depletion patterns of the MNs, CHs, RCH, and ECH under different outdoor environmental conditions are presented in Figure 12.0 The LoRa BS is excluded because it was located in the lab where power is reliable. As depicted in Figure 19, the power depletion rate scaled with the network participant's task. For instance, the MN consumed the least power because it only samples and sends measurements to its CH. A CH receives the data, aggregates it, and forwards it to the next-hop RCH at a corntab-scheduled timeslot. Although the RCH also aggregates its MN data and forwards aggregated data from a distant CH, there was not much appreciable power depletion difference between a CH and an RCH due to the improved efficiency of the scheduling

Figure 12.1 Real-time temperature and humidity data from on-farm deployment, as shown in the TTN cloud. Figure 12.2 The irrigation setup of the proposed MCA-IoT technology and sample organic farm produce showing high crop quality. algorithm. However, the ECH showed an appreciable power depletion difference due to the inclusion of the LoRa communication device, which consumed more power than the BLE. None of the network participants' power was depleted beyond 70% because the 60,000 mAh solar-powered power bank's recharging efficiency was extremely high. Thus, using similar hardware components, this study in its novelty presents a reference Agri-IoT routing protocol for the MCA-IoT architecture with task-scalable prototypes and a unique performance assessment technique. ## 6 Conclusions

This study provides a comprehensive evaluation of an adaptive multihop cluster-based agricultural IoT (MCA-IoT) network specifically designed for precision farming and greenhouse applications. The system is characterized by its robustness, cost-effectiveness, scalability, infrastructure independence, location versatility, and fault tolerance. Tested in an agricultural environment, the custom-built network utilizes BLE and LoRa communication technologies alongside a clustering topology to improve performance efficiency and data quality. The technology exhibits notable operational simplicity and flexibility, which facilitates network expansion and enhances user accessibility. Initial testing conducted during the dry season confirms the system's robustness and adaptability. The technology achieved data transmission with zero packet loss. Transmission covered up to 2.1 km using the LoRa technology for LoS and 1.5 km for NLoS, saving transportation costs and potential costs resulting from crop loss. Power depletion for all network participants did not exceed 70% with a 60,000 mAh solar-powered power bank. However, comprehensive validation through extended testing over multiple years and across additional nodes is essential to fully evaluate the system's commercial viability and identify potential risk factors. Climatic extremes, in particular, may impact the rate of power depletion in nodes, which is a principal risk factor. Limiting testing to the dry season may have restricted the identification of all risk factors associated with adverse weather conditions. Our findings indicate that integrating existing IoT-based communication technologies is the

Figure 12.3 Worst-case power depletion of the proposed MCA-IoT network’s participants. most effective strategy for typical farming contexts in sub-Saharan Africa. Future research should focus on incorporating soil nutrient sensors and remote monitoring technologies to further extend the system's capabilities and optimize farm resource management. The first author contributed 70%, while the remaining authors each contributed 30%. Conceptualization, E.E. ; methodology, E.E.