## 8 Conclusions and Future Work  
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may conflict with each other. For example, maximization of coverage conflicts with the packet error rate, delay, network/battery life time and the overall cost of the system. Whereas in some cases, there exist multiple objectives having no direct relationship with each other, rather they are design dependent; e.g., maximization of coverage has no direct relationship with the throughput, energy efficiency and the QoS. On the other hand, some objectives support each other; e.g., maximization of network/battery life supports the maximization of energy efficiency and minimization of the overall cost of the system. Keeping in view the nature of application, the sensing scenario, input and output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types.  
Figure 8: Trend of research community w.r.t. nature of Multi-objective Optimization (MOO) formulations.  
Due to resource constraints of wireless sensor networks, optimization method that requires relatively less memory and computational power, and produces acceptable results is highly desirable in view of implementing it on each sensor node. This article presented a contemporary review of multi-objective optimization techniques being used to solve different problems relating to design, operation, deployment, placement, planning and management of wireless sensor networks. We analyzed the existing literature to show the trend of the research community with respect to multi-objective optimization algorithms, nature of optimization problems, year-wise optimization objectives and with respect to research emanating from different geographical areas. We also presented a generic resource allocation problem in wireless sensor networks which consists of input variables, required output, objectives and constraints. A list of constraints are also presented to give an overview of different constraints which are considered while formulating the optimization problem in wireless sensor networks. Finally, the article classified different solution algorithms being used to solve the optimization problems relating to wireless sensor networks.  
Keeping in view the multi facet coverage of this article relating to multi-objective optimization, it can be expected that this article will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. For example, efficient wireless charging is an open challenge in wireless sensor networks in order to efficiently charge the motes and to prolong the network life time. Similarly, adopting the renewable energy sources to provide adequate power to the motes can be another challenging task. Prolonged life time coupled with the enhanced processing power can also be formulated as a multi-objective optimization problem. Similarly, simultaneous solution of security and energy issues also become a multi-objective task. Therefore, a multi-objective optimization framework can be developed to jointly optimize the security, power, lifetime and the onboard processing capability.