*5.4. MAC Techniques and Requirements for Agri-IoT*

Next to node deployment, the routing protocol defines the network architecture and selects a suitable MAC technique and a communication pattern for the routing architecture. Unlike classic IoT, requirements for Agri-IoT applications include a low control

message complexity and low latency MAC technique that moderates sampling schedules, access to a shared medium, transceiver operation modes, (e.g., packet transmission and reception, retransmission, collision, over-hearing, overhead handling, and idle listening) active–sleep duty cycles of the deployed SNs, and transceiver channels. Thus, an MAC protocol for WSN-based Agri-IoT applications must be architecture-specific and adaptive to network dynamics such as data transmission errors, interferences/packet collisions, and regular interfacing of the active–sleep duty-cycled schedules of the SNs' transceiver states (e.g., transmitting state, receiving state, idle state, and sleep state [52]) during packet transmission and reception in order to improve network throughput, energy efficiency, latency, and other QoS metrics.

Unlike MAC protocols for classic IoT, an efficient MAC technique for Agri-IoT must ensure exponential energy savings via channel assignment management (CAM) and active– sleep duty-cycle coordination in both time and channel perspectives. Based on these common dual tasks of Agri-IoT-based MAC (thus, duty-cycle optimization—DCO and channel access management—CAM), existing IoT-based MAC techniques can be classified as illustrated in Figure 13 and the state of the art in Table 6.

**Figure 13.** Proposed functionality-based MAC classification framework.

The CAM role eliminates packet collisions, overhearing, and over-emitting to ensure the desired functional balance, while the DCO task minimizes idle listening. A comparative assessment of related MAC methods used in recent WSN-based Agri-IoT applications in Table 6 affirms the need for further research on the functionality balance between DCO and CAM as well as a context-based MMAC approach for the LEACH family of protocols used in Agri-IoT applications.




**Table 6.** *Cont.*

#### *5.5. Overall Perspective*

This section systematically surveyed core Agri-IoT-based routing protocols and evaluated the parent protocols (i.e., RPL, AODV, and LEACH/cluster-based families of protocols) for classic WSN-based IoT networks, of which LEACH-based methods are the best candidates for the resource-limited WSN-based Agri-IoT. However, the RPL and AODV have received more research considerations in terms of realizations in both simulations and practice [9,12,21]. Although the cluster-based architecture has unique endowments for realizing the proposed expectations in Figures 2 and 10, it lacks an in-depth design synthesis in the current state of the art that can uncover its contextualized performance optimization modalities for real-world Agri-IoT applications. In addition, the deployment requirements with trending technologies such as BLE, LoRaWAN, SigFox, 5G, LoRa via Satellite, and NB-IoT under both simulation and real-world operational conditions is imperative. Consequently, the following sections present in-depth overviews on FM, the benchmarking of WSN-based Agri-IoT testbed solutions, clustering methods in the existing state of the art, and how the possible deductions from these syntheses can evolve in a typical case-study such as a WSN-specific Agri-IoT routing protocol for precision irrigation.

#### **6. State of the Art on FM Techniques for Classic WSN Sublayer of IoT**

Since faults and failures are inevitable in the WSN sublayer of Agri-IoT networks (refer to Figure 10), it is imperative to reevaluate the faults, causes, types, strengths/weaknesses of existing FM (i.e., fault detection—FD, fault tolerance—FT, and fault-avoidance—FA) schemes, revisit their founding assumptions [71], and make appropriate recommendations for Agri-IoT network designers. In this section, we establish the root source/cause(s) of faults in the WSN sublayer by assessing the behaviors of the different fault types, examining the extent to which the existing FM schemes address these root faults, and exploring how these schemes will evolve in realistic WSN-based Agri-IoT networks based on their core assumptions, control message overheads/complexities, and energy-saving capacities. From this thorough assessment, this section proposes practical fault-avoidance-based FM techniques for the next generation of WSN-based Agri-IoT.

#### *6.1. Systematic Overview of Faults, Sources, and Taxonomy of Faults in Agri-IoT*

According to the fault–error–failure cycle depicted in Figure 14, a fault can be defined as any impairment that causes a system to produce erroneous results or leads to the failure of the entire system or specific components [72]. The prevalence of faults in WSN-based Agri-IoT is primarily due to the SN component malfunction, lack of post-deployment maintenance, or resource exhaustion [73], which can lead to either impaired event data quality (thus, sensory data error/outlier) or SN-out-of-service (thus, the shortened lifespan of SNs) [25].

Due to the high susceptibility of WSNs to faults, the supervisory routing protocol is expected to incorporate efficient FM mechanisms that can guarantee optimum event data quality and network availability. By implication, FM algorithms for WSNs must not be stand-alone as currently seen in the state of the art [73]; instead, they must be an integral aspect of the routing protocol that agrees with the core participants of the PHY layer, such as the SN, wireless communication medium, and the BS. As illustrated on the left of Figure 15, the WSN sublayer is the most prevalent source of faults in the Agri-IoT ecosystem, in which the SNs are the central origin of faults that can propagate to the upper layers [25,43,73]. This is because the BS is resource-sufficient mainly, and the link's reliability also hinges upon the SNs' availability, as indicated in Figure 15. At the local SN's level, each unit depicted at the bottom of Figure 3 is a potential source of fault/failure, but the degree of prevalence is frequently accelerated whenever power consumption is mismanaged through the disregard of any of the network design requirements and deployment conditions presented in later sections.

The different taxonomies of faults in the state of the art of the WSN sublayer [44,71,73–77], as illustrated on the left side of Figure 16, can be compared as follows:


**Figure 14.** Fault–error–failure cycle [72].

**Figure 15.** Faults in the WSN sublayer of Agri-IoT: sources and fault propagation model.

**a.** Different Taxonomies of Faults in State-of-the-Art of Classic IoT

**b.** Proposed Classification of Faults for Agri-IoT

**Figure 16.** Classification of faults in the state of the art and proposed fault taxonomies for WSNbased Agri-IoT.

From the above definitions and the fault taxonomies on the left side of Figure 16, it can be deduced that hard, permanent, and static faults are practically manifested as SN-out-ofservice, while soft, dynamic, and data-inconsistency faults can be observed as data outliers. Both SN-out-of-service and data outliers are consequences of unit malfunction or resource exhaustion and can be permanent or intermittent in behavior. Both conditions can impair the quality of event data and the global actionable decisions of the network. Therefore, the quality of FM schemes can be evaluated based on their capacities to effectively detect, tolerate, or avoid SN-out-of-service and data outlier faults. In summary, most FM schemes in the state of the art focus on their effects, instead of the root faults, which are the flaws in existing FM schemes [25]. Additionally, since the SN is the sole network device responsible

for event sensing, data computation, packet forwarding, and communication in the WSN sublayer of Agri-IoT, it is the principal source of faults in Agri-IoT networks. A new fault classification framework shown in Figure 16 can be deduced from the above analysis.

Secondly, it is discernible that SNs' power mismanagement is the most prevalent origin of faults [43,80,81], which then propagate to the backend or application level (refer to the right side of Figure 15). For instance, communication, sensing, and computational accuracies of a node can be impaired when the battery energy falls below certain thresholds [43]. Also, network faults can be traced to power exhaustion and node failures, which create holes in the topology that divide the network into multiple disjointed segments [43]. On that account, faults can be avoided in WSN-based Agri-IoT if the energy-saving strategies presented in Figures 9 and 10 are effectively implemented.

Additionally, any FM scheme or fault-monitoring mechanism, be it proactive, reactive, passive, or active, must incorporate the following underlying qualities: thresholds that represent the probable fault conditions without false alarms, fault discovery, minimized message/time complexities, and self-healing and self-reconfiguration to neutralize the effects of the faults [43].

#### FM Framework and Architectures in WSN Sublayer of Agri-IoT

As illustrated in Figure 17, every FM scheme consists of three main steps, which include fault detection (FD), fault diagnosis, and fault recovery/tolerance (FT) [82,83], which always require input information. These steps are implemented in a decision-making framework that involves four major processes: data/information collection, FD model formulation, FD decision and fault classification, and tolerance of its effects using any of the FT mechanisms shown in Figure 17. Thus, the FD model detects the fault, the fault discovery technique distinguishes that fault from false alarms, while the FT mechanism helps to auto-heal and recover from the faults or failures [84]. Mainly, SN-out-of-service faults are detected and tolerated using self-reconfiguration techniques, whereas data outlier faults must strictly follow Figure 17.

**Figure 17.** FM framework in WSN sublayer of Agri-IoT.

In addition, FM schemes can be implemented using either a centralized or distributed architecture [44,85,86]. In a centralized scheme, the FD/FT protocol is hosted and managed on the BS, whereas the distributed scheme hosts and manages this algorithm on the local SNs [87,88] (see Figure 17). The centralized approach is simpler for small-scaled networks but suffers many technical challenges, such as common point failure due to heavy message traffic at the BS and high SN energy waste. In contrast, the distributed approach saves power and controls message traffic on the BS because it allows local decision and selfFD/FT with or without neighboring. According to Figure 17, the distributed architecture can be implemented in three major ways [43,89–91], which include self-detection, neighbor coordination, and the clustering approach. Since the basic design requirement of a WSNbased Agri-IoT is to maintain the healthy functionality and longevity of the SNs and the BS, any post-deployment impairments that cannot be self-fixed must be tolerated to not interfere with the core function of the network. Therefore, any automated FT mechanism that can be achieved through the self-reconfiguration and self-management for enhanced network availability, reliability, and dependability is encouraged in the WSN sublayer [92]. According to Figure 17, an efficient WSN-based Agri-IoT, therefore, requires a calculated mix of FT mechanisms based on the intended application.

#### *6.2. Systematic Survey of Fault Management Schemes in WSN-Based IoT*

FM in Agri-IoT networks has not received adequate conceptualized research considerations. As a result, existing Agri-IoT solutions inherit the FM propositions from the traditional WSN-based IoT networks, which have proven to be unsuitable [14]. This subsection presents a concise overview of these FM schemes, including their strengths, weaknesses, and underlying theories/concepts. It then proposes a more suitable remedy for WSN-based Agri-IoT technology. In canon centralized FM schemes (see references in [93–97]), the underlying FM algorithm is hosted and managed on the BS, while the local SNs host and manage the FM algorithm in distributed architectures [87,88]. Although the centralized approach is simpler for small-scale networks, it suffers many technical challenges, such as common point failure due to heavy message traffic at the BS, management difficulties, and high energy wastages on distant routing. This clearly explains why most outdoor Agri-IoT testbed experiments in [1,10,11,14,18,19] experienced severe FM complications to the extent that the networks became infeasible to operate or manage at higher scalability levels. However, the distributed approach (see references in [74,76,77,91,98–103]) saves power and controls message traffic and workload on the BS because it allows local decisions as well as local-FD/FT with or without neighboring nodes. The distributed FD/FT scheme can also be self-executed, neighbor-coordinated, or clustering-aided [89–91]. Although the clustering-based FM architecture has promising potential to improve energy conservation, network adaptability, and ease of implementation, it has not been extensively researched and exploited.

Again, distributed FD schemes are mainly established on the assumption that the failure of SNs is spatially uncorrelated, while event information is spatially correlated. Therefore, the FD's decision framework is frequently modeled using sensory data or statistical properties of the spatial or temporally correlated SNs [79,104–106] from the immediate neighborhood of a node [74,103] or data from farther SNs [107]. To date, the applicability of these solutions to the Agri-IoT context has attracted several technical challenges. Consequently, the strengths and weaknesses of the main results of the benchmarking FM schemes, their underlying assumptions, and how they addressed the critical fault-affinity factors such as energy conservation, FT/FA, control message complexity, and processor burden of the SNs, are presented in the comparative evaluation summary of Table 7.


**Table 7.** Comparative summary of FM schemes for WSN-based IoT networks.



 YES, 

#### *6.3. Theories/Concepts of Benchmarking FM Schemes and Their Shortcomings*

The conceptual models/theories of the canon FD decision frameworks and the associated shortcomings can be expressed as follows:


In addition to the stipulated shortcomings, these benchmarking FM methods usually ignore the sensory data correlation (i.e., attribute correlation, spatial correlation, and temporal correlation) properties of SNs, require high communication overhead with high FD delays [83], and normally operate in an offline manner, which is inconsistent with the modus operandi of typical Agri-IoT. Hence, they are unsuitable for the recent low-power Agri-IoT applications.
