## 2 Materials and Methods
### Technical Discussions on Related Works
#### 2.1.3 Energy-Efficient Wireless Routing Strategies and Protocols
In the same vein, [PERSON] et al. [57] and [PERSON] et al. [58] developed energy-balanced routing protocols for WSNs. [PERSON] et al. [59] identified the solar energy production and consumption models for sensor networks. Using this platform, this scheme proposes a modified solar-constrained AODV protocol for enriching the energy plans of each sensor node. Compared to other sensor platforms, the establishment of solar sources admits periodical recharging panels and energy-utilization principles. In this concern, the modified AODV has been trained to coordinate the events of each sensor node in terms of data production, data collection, load balancing and recharging plans. In any event, the technical benefits of solar initiative AODV have not been justified against natural disturbances. Equally, [PERSON] et al. [60] and [PERSON] et al. [61] developed multi-path energy optimization possibilities for constructing flat routing protocols in small scale WSNs. [PERSON] et al. [62] proposed a deep-learning model for improving the routing quality to reduce the energy depletion rate. Particularly, reinforcement learning (RL) networks are trained with Q-matrix computations for observing the practices of routing protocols in order to reduce the energy consumption rate. In this case, the observation reveals that the energy of each sensor node  
Figure 2: Cross-layered model. has been spent excessively during irregular data aggregation and transmission periods. Accordingly, the Q-learning model computes event-based Q-values or weights for classifying the sensor node's issues. In association with previous routing protocols, this method initiates data aggregation and routing processes based on the computed Q-values. On the other hand, the limitations of deep-learning procedures in tiny sensor nodes are not significantly explored in this effort. [PERSON] et al. [63] implemented energy optimization techniques for achieving efficient routing policies in order to operate agricultural sensor networks. Agricultural sensor networks are widely used for automating irrigation systems, plant monitoring systems, surveillance systems and soil monitoring systems. In this field, various types of sensor nodes are used according to the sensing role. This can be considered as a heterogeneous sensor network where the nodes are oddly deployed and configured. Simultaneously, these nodes are completely different in terms of internal components and other communication tools attached with sensor nodes. This field-based sensor network has serious vulnerabilities against uniform energy plans. Under this circumstance, this work proposes LEACH principles, deterministic energy clustering principles, sensor information collection modules and stable node selection models in a hierarchical fashion. The network assumptions are made with clustered sensor communities, base stations and gateway points. The contribution of this work is commendable, yet the computation complexity of proposed techniques is ignored for contemplation. In the same way, a related set of techniques are contributed for energy optimization problems and clustered communities in WSNs [64; 65; 66]. The considerations of energy limitations and optimization parameters in WSNs are extensively noted with regard to WMAC and routing policies. From the vast analysis, the clarifications are derived on the basis of the contributions and limitations of current technologies [67; 68; 69]. In this sense, several existing techniques only consider WMAC irregularities as the causes for the fastest downtime of sensor nodes and energy wastage. On the other hand, a few notable research works consider the energy problems against routing functions and data aggregation functions [70; 71; 72; 73]. The perspectives of each research work vary with respect to the authors' own research motivations.