## 2 Related Work and Motivation  
The introduction of some intelligent capabilities into sensor networks enables them to be applied to the control or modeling of complex systems. An intelligent sensor that modifies its internal behavior to optimize its ability to collect data from the physical world and to communicate it in a responsive manner to a host system was defined in [5]. [PERSON] _et al._[6] presented a model of intelligent sensor systems and proposed three broad areas in which intelligence is applied to sensors: intelligence of perception, reasoning and social intelligence. [PERSON] [7] reported an analysis of an intelligent sensor and proposed a language specifically developed for the design of such systems. As described in [8], a sensor network with intelligent behavior is a system that can adapt to the situation and present information that is relevant at the moment. Furthermore, [PERSON] [9] presented new structural concepts of intelligent sensors and networks with intelligent agents. The past few years have witnessed a growing interest in intelligent sensors based on SC, such as in neural networks [10], fuzzy logic [11-15], evolutionary algorithms [16] and hybrid systems composed of fuzzy logic and neural networks [17]. [PERSON] _et al._[18] presented a scheme for data-centric multipath routing in WSNs utilizing a fuzzy logic controller architecture at each node. The authors of [19] presented a distributed, general-purpose reasoning engine designed for WSNs. As described in [20], inference is one of the most common methods, techniques and algorithms applied in data fusion. Inference-based methods make decisions based on the system's knowledge of the perceived situation. In [17], the term _\"soft computing\"_ was introduced to the field of WSNs. Recently more applications of SC in WSNs have been published: in [21] two intelligent localization schemes especially designed for WSN, in which SC technologies play a crucial role, are presented; in [22] a model to improve the distance estimation error between sensor is shown and in [23] the authors present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection by mean of artificial intelligence techniques. In [24] and [25] two real-world applications which use fuzzy inference systems in order to implement a collaborative approach have been described. These papers present two techniques based on fuzzy inference systems which are used to control the navigation of an autonomous mobile robot equipped with sensors. Lately, several studies have been devoted to collaborative WSNs. Due to the characteristics of sensor networks, [PERSON] _et al._[26] argued that the nodes used in their work have to collaborate in order to accomplish their tasks: sensing, signal processing, computing, routing, localization, security, _etc._ Moreover, in [27], [PERSON] claimed that \"... recently the idea of WSNs has started to appear, where entities that sense their environment not only operate individually, but collaborate together using _ad hoc_ network technologies to achieve a well-defined purpose of supervision of some area, some particular process, _etc._\" In both [27] and [28], a cooperating object (CO) was defined as a single entity or a collection of entities consisting of _sensors_, _controllers (information processors)_, _actuators_ or _cooperating objects_ that communicate with each other and are able to achieve, more or less autonomously, a common goal. As described in [28], wireless sensors and actuator networks are typical examples of such cooperating objects. These networks consist of objects that are individually capable of simple sensing, actuation, communication and computation, but the full capabilities of such networks are reached only by the cooperation of all these objects. All the above works are related and relevant to: sensors which incorporate intelligent capabilities, sensors based on SC technologies, collaborative sensors and sensors as cooperating objects.