## 5 Open Research Issues and Future Directions  
Key findings of the study have been summarized in Table 5. In summarizing, characteristics such as node positioning, node localization, data aggregation, clustering, are considered. The issues are shown as addressed or not addressed. Figure 9 presents the total number of research works considered in this survey (non-exhaustive list) in recent years covering the optimization techniques in WSN. \begin{table}
\begin{tabular}{c c c c} \hline
**Optimization Strategies** & **PSO** & **ACO** & **GA** \\ \hline Optimal & Centralized nature of PSO & Distributed nature of & Good for random as well \\ WSN & minimizes the area of & ACO is better in solving & as for deterministic node \\ Deployment & coverage holes of stationary & mobile node deployment & deployments. \\  & node positioning. & issues. & \\ \hline Data & Data aggregation is a & In case of large scale and & Suitable in finding \\ Aggregation & repetitive process which is & dynamic WSNs it can & minimum number of \\ in WSN & quite suitable for PSO. & perform better. & aggregation points while \\ Problem & PSO shows better & & routing data to the BS. \\ Domains & Energy & performance in selecting & & GA is used in formation \\ Efficient & the high energy node as & maximizing both & of a number of \\ Clustering & CHs in each round and can & network lifetime and & pre-defined clusters, \\ and Routing & find optimal route & data delivery to the base & which helped in reducing \\  & effectively. & station. & the overall minimum \\ \hline Sensor & & Improves the accuracy of & Global searching \\ Node & Minimizes the localization & the unknown node & capability of GA obtains \\ Localization & error effectively & location. & better estimated location \\  & & location. & of the sensor nodes. \\ \hline \end{tabular}
\end{table} 
Table 4: Strengths of major bio-mimetic optimization algorithms in solving WSN problems. \begin{table}
\begin{tabular}{c c c c} \hline
**Optimization Methods** & & & \\
**Areas Covered** & **PSO** & **ACO** & **GA** \\
**(Issues addressed)** & & & \\ \hline Stationary Node Deployment (QoS) & Addressed & Addressed & Addressed \\ Hybrid Deployment (QoS) & Addressed & Not addressed & Addressed \\ Dynamic Deployment (QoS) & Addressed & Not Addressed & Addressed \\ Base Station Positioning & Addressed & Not addressed & Not addressed \\ Node Localization (EE) & Addressed & Addressed & Addressed \\ Energy Aware Clustering (EE) & Addressed & Addressed & Addressed \\ Data Aggregation and fusion (Data Security) & Addressed & Addressed & Addressed \\ Cross Layer Optimization & Addressed & Addressed & Not addressed \\ Optimal Routing & Addressed & Addressed & Addressed \\ \hline \end{tabular}
\end{table} 
Table 5: A summary of major bio-mimetic optimization methods in WSNs. Although the bio-mimetic optimization techniques presented herein address many issues of WSNs (Table 5) such as design and deployment, optimal routing and clustering, localization, security, data aggregation, and QoS management, there are still some open research challenges. In particular, research is needed in the area of integration of energy efficiency, QoS, and security. In addition, most previous works view optimization in WSN from a single perspective only. Hence, research in this area addressing the coexistence of all three key issues is limited. * _Integration of QoS, Energy Efficiency, and Security_: Although the presented approaches address many issues associated with optimization in WSNs, some research questions remain relatively unexplored, such as support for and integration of QoS, energy efficiency and security. In number of WSNs applications such as Body Area Networks, Vehicular _ad hoc_ Networks, _etc._ integration of QoS and security along with energy efficiency will be necessary. So integration of these issues in WSNs using metaheuristic algorithms could be a potential future direction. * _Cross-layer Design_: Generally, issues considered to be optimized are supported by different layers of the network protocol stack of a WSN.