## 2 Related Work and Motivation  
Communication capabilities are one of the main characteristics in sensor networks, and enable the networks to share information, knowledge, and sensing effort in sensor nodes. The introduction of intelligent capabilities into sensor networks requires the use of communication resources and their optimization. In this sense, [PERSON] [7] defined an intelligent sensor as one that modifies its internal behavior to optimize its ability to collect data from the physical world and to communicate the data in a responsive manner to a host system. [PERSON] _et al_. [8] presented a model of intelligent sensor systems that emphasized the ability to exchange knowledge with other actors. [PERSON] [9] defined an intelligent sensor network as autonomous sensor nodes that exchange information, reason, and collaborate with each other. The specific application implemented should preserve energy resources and work as one unit when delivering fused and compiled sensor information to the end user. A new structural concept of intelligent sensors and networks with intelligent agents which provide communications elements was suggested by [PERSON] [10]. The past few years have witnessed a growing interest in the use of techniques based on SC to optimize the communication process between intelligent sensors. In this sense, the use of Artificial Neural Networks to discover redundant input data was proposed in [11]. [PERSON] _et al_. [12] proposed a FLC algorithm to ensure that the sensor network attains a large coverage region and maintains dynamic _ad hoc_ network connectivity between nodes. [PERSON] [13] proposed a fuzzy optimization algorithm (FRBS) to efficiently adjust the sensor placement after an initial random deployment. A fuzzy logic control based QoS management scheme for WSANs was developed in [14]. It utilized a fuzzy logic controller inside each source sensor node to adapt the sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. [PERSON] [15] showed a combination of embedded fuzzy logic and neural network models for information processing in complex environments. The most interesting aspect of this approach is the use of a WSN as a distributed computing environment for intelligent data processing methods. [PERSON] [16] presented a novel scheme for data-centric multipath routing in wireless sensor networks utilizing a fuzzy logic controller architecture at each node in the network to determine its capability to transfer named data packets based on its own battery power levels and the type of data being forwarded. [PERSON] [17] proposed a distributed general-purpose reasoning (D-FLER) algorithm that uses fuzzy logic for fusing individual and neighborhood observations. [PERSON] [18] described how information fusion is closely related to data communication in WSNs. As described in [19], collaboration enriches functionality and enhances scalability and manageability of networked sensor systems, particularly in those comprised of a large number of heterogeneous sensor networks deployed over a large area. Effective collaboration may require that various sensor networks share synchronized data replicas. That paper proposed a novel data replication mechanism suitable for the limited bandwidth of sensor networks. The scheme uses neural networks for scheduling of replication. In both [20] and [21], a cooperating object 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 autonomously achieve a common goal. In all of the previous works, the use of SC techniques was proposed to fuse or aggregate raw data in nodes to reduce its redundancy and transmit only the processed data. In [6] and [17], the use of a collaborative algorithm, based on embedding FRBSs into WSNs, was proposed to implement these fusion tasks.