The recent proliferation of global networking has an enormous impact on the cooperation of smart elements, of arbitrary kind and purpose that can be located anywhere and interact with each other according to the predefined protocol. Furthermore, these elements have to be intelligently orchestrated in order to support distributed sensing and/or monitoring/control of real world phenomena. That is why the Internet of Things (IoT) concept raises like a new, promising paradigm for Future Internet development. Considering that Wireless Sensor Networks (WSNs) are envisioned as integral part of arbitrary IoTs, and the potentially huge number of cooperating IoTs that are usually used in the real world phenomena monitoring and management, the reliability of individual sensor nodes and the overall network performance monitoring and improvement are definitely challenging issues. One of the most interesting real world phenomena that can be monitored by WSN is indoor or outdoor fire. The incorporation of soft computing technologies, like fuzzy logic, in sensor nodes has to be investigated in order to gain the manageable network performance monitoring/control and the maximal extension of components life cycle. Many aspects, such as routes, channel access, locating, energy efficiency, coverage, network capacity, data aggregation and Quality of Services (QoS) have been explored extensively. In this article two fuzzy logic approaches, with temporal characteristics, are proposed for monitoring and determining confidence of fire in order to optimize and reduce the number of rules that have to be checked to make the correct decisions. We assume that this reduction may lower sensor activities without relevant impact on quality of operation and extend battery life directly contributing the efficiency, robustness and cost effectiveness of sensing network. In order to get a real time verification of proposed approaches a prototype sensor web node, based on Representational State Transfer (RESTful) services, is created as an infrastructure that supports fast critical event signaling and remote access to sensor data via the Internet.

The main goal of soft computing technologies (fuzzy logic, neural networks, fuzzy rule-based systems, data mining techniques...) is to find and describe the structural patterns in the data in order to try to explain connections between data and on their basis create predictive or descriptive models. Integration of these technologies in sensor nodes seems to be a good idea because it can significantly lead to network performances improvements, above all to reduce the energy consumption and enhance the lifetime of the network. The purpose of this paper is to analyze different algorithms in the case of fire confidence determination in order to see which of the methods and parameter values work best for the given problem. Hence, an analysis between different classification algorithms in a case of nominal and numerical data sets is performed with the goal to realize which of applied techniques obtain higher accuracy and less error