## 4 Classification of Routing-Related Surveys
### Medium Focused Areas
#### 4.2.3. Computationally-Intelligent Based Routing Surveys  
The Computationally Intelligent (CI) based surveys analyze those routing techniques which have the ability to adapt themselves with dynamic and challenging environments. These surveys may be classified into four categories as outlined below:  
* In swarm intelligence based categorization, the robustness, reliability and flexibility of optimization techniques such as shortest-path based Ant Colony Optimization (ACO), foraging based Bee Colony Optimization (BCO), Particle Swarm Optimization (PSO), Termite Colony Optimization (TCO) or Spider Monkey Optimization (SMO) are discussed. * In hybrid intelligence based categorization, the adaptability, steadfastness and strength of both already discussed swarm intelligence and adaptive intelligence based techniques (such as knowledge-base/experience based Reinforcement Learning (RL), human reasoning oriented Fuzzy logic (FL), genetic-selection/natural exclusion based Genetic Algorithm (GA), human neurons based Artificial Neural Networks (ANNs), morphogenesis-based Reaction Diffusion (RD) and bacterial-signaling-based quorum sensing (QS)) are evaluated. * In mixed based categorization, both biologically-inspired or non-biological routing techniques may be discussed and compared. * In partial based categorization, those surveys are delineated which consider CI based routing as a subpart of their discussion regarding intelligence based WSNs. As far as the surveys with complete emphasis on routing are concerned, the authors in [41; 64; 100; 103; 112] have mainly discussed routing protocols belonging to swarm intelligence based categorization. In [48; 92], the authors have mainly discussed various biologically inspired hybrid intelligence based routing techniques. The authors in [82; 93] have mainly discussed routing protocols belonging to mixed based categorization. In [103], the authors have discussed the latest swarm-intelligence based routing approaches. The paper very briefly outlines some aspects of TCO whereas the functionality of SMO is also summarized in a concise manner. In [48], the authors have discussed mathematical principles of biological calculations and general routing framework regarding WSNs. The authors in [41; 100] delineates various design challenges for routing in WSNs. Additionally, reference [41] outlines a general framework that may provide guidelines to design new swarm intelligence based routing protocols and also delineates a useful discussion that describes shortcomings in the already published research work. The design issues and challenges are discussed \begin{table}
\begin{tabular}{c l c} \hline \hline \multicolumn{1}{c}{**Survey Type**} & \multicolumn{2}{c}{**Categorization and Design Requirements Based Analysis of Multipath-based Routing Surveys**} \\ \hline \multirow{3}{*}{**Typical based**} & \(Hg\) & [76; 111] \\ \cline{2-3}  & \(Md\) & [80] \\ \cline{2-3}  & \(Lw\) & [37; 90] \\ \hline \multicolumn{1}{c}{**Multichannel Routing**} & \(Hg\) & [114] \\ \hline \multirow{3}{*}{**Security based**} & \(Hg\) & [59] \\ \cline{2-3}  & \(Md\) & [65; 96] \\ \hline \multirow{3}{*}{**Fault-Tolerant**} & \(Md\) & [58] \\ \cline{2-3}  & \(Lw\) & [90] \\ \hline \multicolumn{1}{c}{Hg* = High Count, Md* = Medium Count and Lw* = Low Count. Note: Various categories of count} \\ \cline{1-1} \cline{2-3}  & correspond to Count Number Index (CNI) in Table 2. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Design analysis of multipath-based routing surveys in WSNs. in [48; 64; 82; 103]. Future research directions are summarize in [41; 48; 82; 103; 112]. Furthermore, Table 9 manifests the presence of 3 high count surveys, 5 medium count surveys and only 1 low count survey. The surveys having partial emphasis on routing consist of [120; 134]. In [120], the authors have mainly discussed various ant colony optimization based routing approaches in WSNs. The survey in [134] is more comprehensive and evaluates computational intelligence based energy aware routing and clustering in WSNs.