## 1 Introduction  
Wireless sensor networks have been recently applied to a wide variety of both civil and military applications. They consist of a large number of sensor nodes generally deployed in different environments and are designed essentially to detect events or natural phenomena, collect, process and transmit sensed data to interested users [1].  
The recent improvements in sensing, communication and computing technologies contributed to increase the penetration of WSN in everyday life, in order to perform tasks like intrusion detection, target tracking, environmental monitoring [2], remote sensing and global surveillance, among others [3]. Thus some of the applications foreseen for WSNs will require multi-platform systems with a high number of different devices of the order of many hundreds nodes: the different types of sensors may completely differ in nature of the device (e.g., fixed or mobile), size, computational and energy capabilities, functions to be performed, parameters to be measured, topology and mobility patterns.  
Especially considering the high-efficiency required in terms of power management, we focus our attention on the communication issue that is the most expensive in the entire sensor network power budget. Moving from sensor nodes to the network server, the system generally increase in compute power, data storage and power availability. However, along with the inherent characteristics of actual environments in which sensor networks are deployed, sensor network modeling and design represent a very complex and sophisticated process and performing system optimization is fundamental to make realistic the employment of such new technologies for real life applications.  
The energy constraints of sensor networks, especially when their size increase, represent an additional aspect to be taken into account. For this reason the authors decided to investigate the effectiveness of evolutionary algorithms to optimize network lifetime in a limited power supply framework, mainly focusing on communication strategies [4]. With the continuing advances in network design, RFIDs, smart phones, and other wireless mobile devices, optimization in the communication layer is needed not only to extend the system lifespan, but also to increase reliability, availability and integration efficiency. These concurrent aspects represent a typical multi-objective problem.  
In the last decades several evolutionary algorithms have been developed for the optimization of different kinds of engineering problems. All these algorithms are population-based heuristic search techniques, which can be used to solve general combinatorial optimization problems, modeled on the concepts of natural selection and evolution, or based on cultural and social behaviors typical of the swarm intelligence. More tests have been performed to further demonstrate the algorithm robustness and a new interface has been developed in order to adapt this hybrid algorithm to WSN domain [5].  
Section 2 presents main WSN concepts and critical issues while Section 3 describes how UAVs can be integrated in WSN. In Section 4 we discuss the WSN optimization problem and illustrate the procedure we developed to apply an Evolutionary Optimization method to WSN model. Section 5 describes the considered network model and some assumptions we made before performing the numerical simulations. Finally, numerical implementation and results for wireless sensor network optimization are reported in Section 6.