## 4 Classification of Routing-Related Surveys
### Least Focused Areas
#### 4.3.1 QoS Based Routing Surveys  
The QoS-based routing surveys consider those routing protocols which struggle to maintain a balance between energy consumption, data quality [81], and quantity. That is why, QoS-based surveys focus on energy efficiency, reliability, end-to-end delay minimization, fault tolerance, throughput/bandwidth enhancement, delay-variability diminution, and collision/congestion avoidance [81]. The available QoS-based routing surveys may be broadly classified into subsequent categories i.e.,  
* In typical QoS-based categorization, routing surveys may reckon those QoS protocols that consider a broad-spectrum of QoS routing in WSNs. These QoS protocols may consider the parameters such as energy efficiency, reliability, packet-delivery rate, end-to-end delay, network lifetime, throughput, protocol overhead and robustness. * In multipath-QoS based categorization, those multipath routing protocols are discussed which provide QoS assurance such as reliability to WSNs. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Survey Type**} & \multicolumn{2}{c}{**Categorization and Design Requirements Based Analysis of Computationally-Intelligent Routing Surveys**} \\ \hline \multirow{3}{*}{**Swarm based**} & _Hg_ & [41] \\ \cline{2-3}  & _Md_ & [64; 100; 103] \\ \cline{2-3}  & _Lw_ & [112] \\ \hline \multirow{2}{*}{**Hybrid based**} & _Md_ & [48; 92] \\ \cline{2-3}  & _Hg_ & [82; 93] \\ \hline \multirow{2}{*}{**Partial based**} & _Hg_ & [134] \\ \cline{2-3}  & _Lw_ & [120] \\ \hline \hline \end{tabular}  
* Hg* = High Count, Md* = Medium Count and Lw* = Low Count. Note: Various categories of count correspond to Count Number Index (CN) in Tables 2 and 3.  
\end{table} 
Table 9: Design analysis of computationally-intelligent routing surveys in WSNs. * In fault-tolerance based categorization, those routing protocols are discussed which have the ability to work continuously even in the presence of faults for increasing reliability and availability. * In Congestion (Cong. )-handling based categorization, those routing protocols are delineated which have the capability to detect, control and avoid congestion by handling issues such as channel contention/overload, packet collision, buffer overflow, data-rate control and load balancing near sink node. * In partial QoS-based categorization, those surveys are delineated which consider typical, fault-tolerant and congestion-oriented QoS-based routing as a subpart of their discussion regarding QoS-based routing protocols in WSNs. While considering the surveys with complete emphasis on routing, the authors in [50; 77; 106; 113] have mainly classified routing protocols employing typical QoS-based categorization. In [106], the authors have discussed those WMSNs protocols that try to maintain a balance between QoS and energy efficiency. In addition to discussing QoS-aware routing approaches, the authors in [113] have outlined the distributions of QoS-oriented literature and QoS parameters used in the literature. In [111], the authors have discussed and compared the multipath routing approaches guaranteeing QoS in real-time WMSNs. The authors in [58] have categorized routing protocols using fault-tolerance based categorization while [81] have mainly classified routing protocols applying QoS-based congestion-oriented categorization. In [77], a QoS-based comparison of discussed routing protocols is presented using NS-2 [145]. The authors in [77; 81] have outlined challenges of QoS-based routing in WSNs. In [113], QoS issues in WSNs are elaborated while QoS modeling requirements of WMSNs are delineated in [111]. Future research directions are outlined in [58; 81; 106; 111; 113]. Besides that Table 10 clearly demonstrates the presence of 2 high count, 4 medium count and 1 low count surveys. The surveys in this category with partial emphasis on routing include [117; 123; 140; 141]. The article [125] also outlines a case study for forest fire surveillance and thereby, is helpful to enhance practical learning.