## 2 Theoretical Background
### Related Work  
In [29] the area coverage maximization is pursued through the appropriate positioning of mobile sensor nodes among stationary sensor nodes. The overall network field area is partitioned into a number of individual grids. At the same time, the weight of each individual grid is estimated. The target grid of any mobile node is considered to be the one that owns the minimum value of weight among all grids. In [30] WSN coverage optimization is pursued by using the PSO method along with a grid based strategy. Particularly, the square grid configuration is used to evaluate the particles in the fitness functions used. Additionally, maximizing the coverage and the minimization of energy consumption are also chased. The authors in [31] proposed a novel algorithm in order to optimize the coverage in a WSN using the PSO algorithm along with Voronoi diagrams. The points of interest are defined by the Voronoi diagram as the points at the edges of the Voronoi polygons as well as points in the boundaries where there is a high possibility that a coverage hole exists. According to the distance of the points of interest from the sensors, the area of coverage holes are computed and minimized by using a PSO algorithm.  
In [32], the authors discovered that the phenotype space of the maximum coverage deployment problem in WSNs is a quotient space of the genotype space and proposed an efficient genetic algorithm using a novel normalization method. Additionally, they utilized the Monte Carlo method in order to design an efficient evaluation function, and achieved short computation times via a technique that starts from a few random samples and progressively raises their number for succeeding generations. In [33], a novel bidding protocol, which is applied in an area where a mixture of static and mobile nodes has been deployed, is proposed. According to the specific protocol, the mobile nodes change their locations in order to improve the coverage area by fulfilling the holes that have been determined by the static ones through the calculation of Voronoi diagrams.  
A virtual force algorithm (VFA) is proposed in [34], as a sensor deployment methodology in order to improve the coverage area. The sensor nodes are initially deployed in a random way. Attractive and repulsive virtual forces are applied in the nodes in order to obtain a new position and once this position is determined they proceed to a one-time movement to their new optimal locations.  
The authors in [35] proposed a virtual force-directed particle swarm optimization (VFPSO) algorithm. VFPSO is a self-organizing algorithm, aiming to enhance the coverage in WSNs that consist of both mobile and stationary node. The proposed algorithm combines the virtual force (VF) algorithm with particle swarm optimization, where VF uses both attractive and repulsive forces to determine virtual motion paths and the rate of movement for sensors. In this way, the velocities of particles are updated according to not only the historical local and global optimal solutions but also the aforementioned virtual forces of sensor nodes.  
In [36] the so called coverage enhancing (COVEN) algorithm was introduced. COVEN calculates the percentage of hole area after random deployment of sensor nodes. Specifically, by using a two-step deployment process, static nodes cooperate in order to define the number of mobile nodes that need to be added in the sensor network, as well as their final location, so as to achieve maximization of coverage. In this way, a tradeoff between the cost of deployment and percentage of area covered is attained.