## 1 Introduction  
Optimization plays a key role in wireless sensor networks. The optimization in WSNs can be broadly categorized into single and multi-objective optimization problem. In single objective optimization, the main aim of the optimizer is to minimize or maximize one objective under various constraints. Where as, in multi-objective optimization multiple objectives are simultaneously optimized. Most of the real-world problems involve multiple objectives, where all objectives need to be optimized simultaneously. This condition makes the multi-objective optimization (MOO) a challenging task and undoubtedly a very hot topic of research for theorists and engineers [1; 2; 3; 4; 5; 6]. Usually, the problem formulation is done as an initial phase, where the desired scenarios are formulated as multi-objective optimization problems, and are solved by using different algorithms. The multiple objectives may or may not be conflicting, but in most of the cases, the objectives conflict with each other [7; 8; 9; 10]. Therefore, it is very less probable to find a global optimal solution, contrary to the problems of the single objective optimization [11]. In MOO there exists multiple optimal solutions, and the decision maker has to choose the best among them, depending on the priorities of the objectives to be achieved. Depending upon the preference of the multiple objectives, the optimization problem can be tackled using various techniques [12]. The most commonly used approach is to combine multiple objectives to one figure of merit by assigning different weights to different objectives and then perform single objective optimization algorithm. Weights can be assigned to multiple conflicting objectives through direct assignment, eigenvector method, entropy method and minimal information method, _etc_. Few other commonly used multi-objective handling techniques are Min-Max, Pareto, Ranking, Goals, Preference, Gene, Sub-population, Lexicographic, Phenotype sharing function and Fuzzy [13]. WSNs have been widely adopted for monitoring purpose, e.g., to monitor the environment, habitat, greenhouse, climate, water networks [14], and personal health [15]. Similarly, WSNs have been proven a great tool for automation, e.g., home automation [16] and industrial automation [17; 18]_etc._, are few promising applications of WSNs. WSNs are composed for tiny nodes, where the nodes sense data from the environment and pass the data to the central processing unit. The nodes are usually equipped with low power, low energy and very little memory [19; 20]. Due to the limited on-board resources, the designing, deployment and the operations of WSNs become challenging, while simultaneously providing the quality of service requirements [21; 22]. Researchers have proposed and adopted various techniques in order to utilize the resource constrained WSNs efficiently [23; 24; 25; 26; 27]. For example, [26] has proposed a multi-objective hybrid optimization algorithm to solve the coverage and connectivity problem and to enhance the performance of the WSNs in terms of network life time, by joining a multi-objective on-demand algorithm employing Genetic Algorithm (GA) and a local on line algorithm. In [27], the authors have used a formulation of data aggregation problem as a mixed integer linear optimization problem, by minimizing the total power, considering the co-channel interference constraints. Abundant literature is available where MOO has been used to solve different optimization problems relating to WSNs. This article presents an updated review of the MOO techniques being used to solve different problems relating to design, operation, deployment, placement, planning and management of WSNs. The paper provides an insight into varying degree of preferences for different conflicting objectives. Therefore, it can provide means to configure WSNs for different tradeoffs between various performance parameters depending upon the application environment of the WSN.