*6.4. Open Issues on Existing FM Solutions for Classic WSN-Based IoT Networks and Recommended Design Guidelines for Achieving Efficient FM in WSN-Based Agri-IoT*

A fault in the WSN sublayer of Agri-IoT networks can be manifested as a data outlier and SN-out-of-service or node failure, both of which must be detected and resolved locally or globally using the spatially correlated event information and efficient threshold-based decision frameworks. Although there has been extensive research concerning FM schemes for the WSN sublayer, several technical challenges that require urgent contextual research considerations exist. They include the following:


Existing FM solutions are meant for resource-sufficient and expensive classic WSNbased IoT, not resource-constrained, context-specific use cases like Agri-IoT networks. Regarding these technical challenges, this tutorial presents the following design guidelines for building efficient and realistic FM schemes for WSN-based Agri-IoT:


#### **7. State of the Art on Real-World, Canon WSN-Based Agri-IoT Testbed Solutions**

It is well documented that WSN-based Agri-IoT is the most reliable remedy for mitigating the negative impacts climate change has had on agricultural production, for which many architectural designs and testbed prototypes have been proposed [12,36]. In addition, since the autonomous, resource-constrained SNs in Agri-IoT are expected to operate without post-deployment maintenance checks, the issues of FM, power optimization, and selforganization during SN design and network deployment cannot be ignored in existing testbed solutions [12,117]. Essentially, the results from most research projects on Agri-IoT relied on simulation experiments [1,10,14], which have retained the gap between the philosophy of this technology and the comprehension of its real-world behavior for a more accurate performance assessment. This section presents a systematic performance assessment of the few real-world WSN-based Agri-IoT testbed solutions currently based on the classic WSN-based IoT principles. To understand how the benchmarking realization testbeds of Agri-IoT in [1,10,11,14,18,19] fared in real-world operational conditions, the results from their respective performances are systematically evaluated and summarized in Table 8. It was discovered that the current benchmarking testbed solutions in [1,10,11,14,18,19] are capital-intensive because they are reliant on fixed/location-restricted backbone infrastructures (see the middle of Figure 3), too complicated to deploy and manage by even expert users, based on unrealistic indoor conditions which do not commensurate real-world environmental conditions, and based on the high- power-demanding centralized or flooding architectures which further complicate network manageability when up-scaled. A concise and systematic survey of these benchmarking real-world Agri-IoT networks and their flaws in the state of the art is summarized in Table 8.

Additionally, it can be established from the comparative assessment of the benchmarking Agri-IoT testbeds in Table 8 [10,11,18,19] that the embedded communication technology, event routing architecture, and the SNs' power management are the core factors that made them capital-intensive and complicated to both experts and low-income farmers. Additionally, self-healing, reconfigurability, and adaptability mechanisms to faults were not deployed [1,14,17]; hence, faulty and turbulent conditions could not be tolerated. Furthermore, since the battery-powered SNs rely on expensive Wi-Fi and cellular communication technologies that are not freely accessible at all locations, the SNs exhausted their battery supply a few days after deployment. Similarly, those that relied on ZigBee/IEEE 802.15. 4 communication technologies with power-intensive 6LoWPAN or IPv6 protocols restricted the resulting network to drive on the problematic centralized or flooding architectures without any efficient FM techniques. As a result, these solutions used costly fixed IP infrastructural supports and the centralized routing architecture, making them practically impossible to manage as the networks scaled. This is why the SNs unstably exhausted their battery power and abruptly abridged network lifespans [1,10,11,14,18,19].


**Table 8.** Comparative analysis of WSN-based Agri-IoT testbed solutions.

Therefore, the freely available low-power wireless technologies (e.g., LoRa, BLE, 5G, Z-wave, NB-IoT, and SigFox) that are founded on a suitable routing topology are the best candidates for making this ubiquitous application [1,16] cheap [1,20] and simple for all users. Thus, the cluster-based topology is more pivotal to addressing the above challenges of Agri-IoT [10,17] than the traditional cellular and WiFi technologies that are inaccessible in many farms, depending on their locations [10,20]. However, besides distance-power constraints, architectural support, and network manageability challenges, these freely accessible wireless communication technologies have specific limitations, which include:


Therefore, a research opportunity exists for a flexible, ubiquitous, realistic, energyefficient, self-healing, simple, low-cost, cluster-based, and wireless outdoor-based testbed that consists of infrastructure-less, task-scalable, and wirelessly configurable experimental SNs and a BS. It should also be deployed, re-deployed, monitored, controlled, and managed by non-experts to operate stably throughout the entire crop-growing season.

#### **8. Case Study: Cluster-Based Agri-IoT (CA-IoT) for Precision Irrigation**

As earlier established in Figure 2, the design and implementation of Agri-IoT networks are driven by unique critical factors, which are mainly determined by the associated routing architecture, communication technology, actuation management mechanisms, and environmental impacts. In the operation phase, these factors constitute the specific objectives in Figure 10, which the supervisory routing protocol must address in order to optimize performance efficiency and stability. As systematically established above, the LEACH-inherited cluster-based architecture has the most promising potential to address

these technical challenges. It helps to attain high power optimization via communication distance and packet minimization, efficient network administration/adaptability, high event data quality through auto-FM, and local data quality management, as indicated in Figure 10. So, this section presents a systematic analysis of how the merits of this architecture evolve in CA-IoT for precision irrigation use cases. Using the framework in Figure 12, the clusterbased architecture was pre-examined to uncover how the fundamental Agri-IoT design requirements and goals presented in the reference frameworks in Figures 2, 9 and 10 can unfold into realistic multi-parametric optimization metrics.

The conceptual architectural framework of the proposed network, as illustrated in Figure 18, can be implemented using Arduino-based or Raspberry Pi(RPi)-based microcontrollers, BLE and LoRa for intra-cluster, inter-cluster, and BS–cloud communications, respectively, and DHT22/STEMMA soil moisture sensors for measuring the respective ambient and soil microclimatic parameters. Also, a unit cluster from Figure 18 detailing the key network components of MNs, CH, BS, and the field-deployed precision irrigation system is shown in Figure 19. It is assumed that the core units constituting the MNs, CH, and BS, as illustrated in Figure 19, are optimally selected and designed after Figure 2. Using Figures 18 and 19 as the reference architectural frameworks for achieving our contextualized objectives, this section presents an in-depth systematic assessment and characterization of the scores of canon cluster-based routing protocols of conventional WSN-based IoT applications so that the desired MOO metrics can be appropriately deduced and adapted for the design of the associated routing for our case study.

**Figure 18.** Conceptual architectural framework of the proposed CA-IoT for precision irrigation management.

**Figure 19.** CA-IoT use case cluster illustrating the key network components: MNs, CH, and BS.
