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A new challenge in the sensor network area is the coordination of heterogeneous sensors (with different sensing, mobility and computing capabilities) in an integrated network. This kind of sensor networks have clearly high relevance in surveillance systems, in which both low-end static ground sensor nodes and more sophisticated sensors carried by mobile platforms, such as Unmanned Aerial Vehicles (UAVs), cooperate. This paper provides an analysis of two different strategies to guide the collaboration among the sensor nodes mentioned above, applied to area surveillance systems. The first analyzed problem is related to the choice of the UAV instance that will respond to a given alarm issued by a ground sensor node. The second issue is the estimation of the response time until any UAV can be engaged in handling an alarm and effectively handles it. Two strategies are introduced and compared: one based on a pheromone inspired approach and another based on utility functions inspired on risk profiles that models decisions of investors in the stock market.

Chaos has an intrinsically richness related to its structure and, because of that, there are benefits for a natural system of adopting chaotic regimes with their wide range of potential behaviors. Under this condition, the system may quickly react to some new situation, changing conditions and their response. Therefore, chaos and many regulatory mechanisms control the dynamics of living systems, conferring a great flexibility to the system. Inspired by nature, the idea that chaotic behavior may be controlled by small perturbations of some physical parameter is making this kind of behavior to be desirable in different applications. Mechanical systems constitute a class of system where it is possible to exploit these ideas. Chaos control usually involves two steps. In the first, unstable periodic orbits (UPOs) that are embedded in the chaotic set are identified. After that, a control technique is employed in order to stabilize a desirable orbit. This contribution employs the close-return method to identify UPOs and a semi-continuous control method, which is built up on the OGY method, to stabilize some desirable UPO. As an application to a mechanical system, a nonlinear pendulum is considered and, based on parameters obtained from an experimental setup, analyses are carried out. Signals are generated by numerical integration of the mathematical model and two different situations are treated. Firstly, it is assumed that all state variables are available. After that, the analysis is done from scalar time series and therefore, it is important to evaluate the effect of state space reconstruction. Delay coordinates method and extended state observers are employed with this aim. Results show situations where these techniques may be used to control chaos in mechanical systems.

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