## 6 Conclusions and Future Work  
The optimization of the area coverage in WSNs is the subject of the research work presented in this article. Specifically, two computational intelligence algorithms were designed in order to achieve the maximization of area coverage and area k-coverage. Both the genetic algorithm and the particle swarm optimization algorithm developed were analytically compared against each other in the various scenarios examined, using statistical testing. In all case studies examined, the efficacy of the two algorithms developed was found to be very close to the ideal in terms of the achieved coverage percentage. More precisely, it was found that in the simpler case studies examined, the execution of the GA required less execution time compared to the PSO algorithm. Yet, as the scale of the experiments increased, the genetic algorithm needed more time and a larger size of population in order to achieve results as well as those accomplished by using the PSO algorithm. Similarly, in the simpler case scenarios examined, the GA achieved a minimally better result in terms of mean value of coverage calculations compared to the PSO algorithm. In the cases where the k-coverage was pursued, the difference in the mean value of coverage calculated when using both the algorithms was reduced and finally in the most complex case scenario, the PSO algorithm was found to be better. Additionally, the GA and the PSO algorithms developed were analytically compared against two well-known coverage maximization methods, i.e. a grid based PSO and a Voronoi-based PSO algorithm, using statistical testing. Both the algorithms proposed in this article were found to provide higher coverage percentages than the other algorithms in comparison, with execution times lower than the former of them and higher than the latter one. Despite the promising results derived from this work, there are a lot of efforts that need to be made in order to achieve even better performance enhancement of WSN performance. The reason for this statement is that there are many problems that generally obstruct the operation of WSNs and in particular affect either directly or indirectly the coverage optimization. For instance, restrictions of energy in WSNs have a detrimental influence on coverage maximization. This is because the energy insufficiency of sensor nodes limits their operational life [39], and whenever the residual energy of a sensor node is exhausted, this node becomes inactive thus causing coverage holes. Consequently, the use of schemes aiming at the achievement of power control [40], data aggregation [41; 42], energy efficiency [43; 44; 45], energy balancing [46; 47; 48], data compression and restoration [49; 50; 51], congestion avoidance, and congestion control [52; 53; 54; 55; 56] should be also considered. Likewise, the preservation of network connectivity is an issue of crucial importance for WSNs in order to be operational and thus has to be pursued [57; 58; 59; 60]. Yet, the deployment of sensor nodes at the network field of a WSN affects not only connectivity but also coverage. This is because in order to ensure connectivity, the sensor nodes need to be placed close enough to each other so that they remain within their communication range. On the other hand, in order to maximize the coverage, the sensor nodes need to be scattered around the area so as to prevent forming coverage holes. For the aforementioned reasons, in order to achieve optimal results, coverage maximization should be pursued in combination with the attainment of energy conservation, connectivity preservation, and other performance metrics through the application of proper multiobjective schemes [61; 62; 63; 64; 65; 66; 67]. This is the next challenge for the authors of this article in terms of future research work.