## 4 WSN Optimization  
As described in the introduction, sensor networks are emerging as an important field of study in different technological areas and offer a rich domain of active research. These complex systems use technologies essentially from sensing, communication and computing fields, and combine at the same time many design issues of wireless communication and mobile computing. The future vision of large multi-platform networks makes it even more attractive in multimedia and other sensitive applications. Performing the processing at the source can dramatically reduce the computational cost of networking and management; therefore WSN organization should be autonomously performed with a minimum of human interference. Minimizing energy consumption certainly is the key requirement when designing sensor network protocols and algorithms. Since sensor nodes are equipped with small and very limited batteries, it is crucial for the network to be energy efficient in order to maximize network lifetime. However further requirements for a well designed system are represented by the fault tolerance property, network scalability, reliability and of course low production costs as much as possible. Generally sensor network applications require quality of service guarantees suggesting the need for global network cost optimization. Many different approaches have been used in the WSN optimization research field. For example in [21] the authors present an approach for optimizing global cost in sensor networks through greedy local decisions at each node, and they explore the benefits of this approach in reducing the idle listening at individual nodes in order to reduce the global network energy cost. In [22] an approach based on genetic algorithms (GA) is proposed to optimize design of application-specific WSNs with connectivity and energy conservation limitations. The authors develop a fitness function to incorporate different aspects of network performance, including the status of sensor nodes (active or inactive) and the choice of appropriate cluster heads, but still referring to a single-hop routing scheme. A model of meta-heuristic optimization algorithm such as ACO (Ant Colony Optimization), basically inspired by the behaviour of ants in finding paths from the colony looking for food, has been used in [23] in data gathering and communication for WSNs, assuming unlimited energy for the base station and comparing its results with LEACH and PEGASIS algorithms [24, 25], again following a cluster based approach. A similar approach can be found in [26], but this time the ant colony optimization is applied with respect to an _ad hoc network_ where nodes are not fixed. In [27] a global multi-hop routing strategy for heterogeneous sensor networks is proposed mixing tiered and clustered architectures through some heuristics. In applying evolutionary techniques to WSN design, critical issues must be considered to trade off between different objectives:  
* to reduce the amount of power wasted by wireless devices;
* to increase the network lifetime;
* to achieve fault tolerance in case of individual node failure;
* to allow network scalability and deployment;
* to reduce bandwidth requirements, enhancing collaboration among nodes (e.g., data fusion) since the limited wireless channel bandwidth must be shared among all the sensors in the network. Before building the objective function and performing the optimization, it is important to understand the main sources of energy loss in operation of wireless sensor networks in order to make more effective the optimization process itself. Basically the two main sources of power consumption are represented by communication or computation tasks [28]: communication involves essentially the use of transceivers while computation is related with processing aspects due to the chosen protocol and data compression techniques. The goal of protocol development for environments with limited power resources is to optimize the transceiver usage for a given communication task.