*2.7.1. Definition*

244 Wireless Sensor Networks – Technology and Protocols

problem. Some trust models make use of this observation and introduce probabilistic modeling that uses a Bayesian updating scheme known as the Beta Reputation System [65] for assessing and updating the nodes reputations. The use of the Beta distribution is due to the binary form of the events considered. For example, RFSN[2] uses a probability model in the form of a reputation system to summarize the observed information (FHI) and share the values of the parameters of the probability distributions as second-hand information(SHI). This shared information is soft data, requiring a proper way to incorporate it with the observed data into the trust model. The step of combining both sources of information is handled differently by different trust models. RFSN uses Dempster-Shafer belief theory model [48], solving it using the concept of belief discounting, and doing a reverse mapping from belief theory to continuous probability. In [49], a new Bayesian fusion algorithm to combine more than one trust component - data trust and communication trust to infer the overall trust between nodes is proposed. The trust value calculated between nodes based on their cooperation in routing messages to other nodes in the network is termed as Communication trust (CT). The trust value calculated based on the actual sensed data of the sensors in WSNs is known as Data trust (DT). As an extension to this work, authors proposed Recursive Bayesian Approach to Trust Management (RBTMWSN)[50] by introducing a new trust model and a Gaussian reputation system(GRSSN) for wireless sensor networks based on a sensed continuous data. In this work, Bayesian probabilistic approach based on the work done in modelling Expert Opinion[51] for mixing second-hand information from neighboring nodes with directly observed information is proposed. Opinions provided by knowledgeable sources are known as experts opinions. Such opinions are modulated by existing knowledge about the experts themselves, to provide a calibrated answer. It allows for the formal incorporation of informed knowledge into a statistical analysis. The probabilistic approach adopted is to consider the opinion given by the expert as soft data that is merged with the hard data according to the laws of probability[52]. In [53], authors proposed a Node Behavioral Strategies Banding Belief Theory of the Trust Evaluation (NBBTE) Algorithm. In this approach, at first, each node establishes the direct and indirect trust values of neighbor nodes by comprehensively considering various trust factors such as packet receive, send, strictness, delivery, consistency and availability, *etc*, and combining these factors together with network security grade, correlation of context time and rewards degree. Next, fuzzy set theory is used to decide the trustworthiness levels in accordance with the fuzzy subset grade of membership functions. Based on the levels of trustworthiness, the basic confidence function of D-S evidence theory[54] is accordingly formed. Finally, using the revised Dempster rules of combination, the integrated trust value

of a node is obtained by integrating its trustworthiness of multiple neighbor nodes.

Current research challenge has been in designing an accurate and efficient trust and/or reputation model for distributed and heterogeneous environments[47]. When developing such models, different issues have to be taken into consideration. The problem to be solved here consists of deciding in a distributed environment which entity is the most reliable to interact with, in terms of trust and reputation. That is, having a system where different entities offer some services or goods and other ones are requesting those services, the former A trust aware routing protocol is a routing protocol in which a node incorporates in the routing decision its opinion about the behavior of a candidate router. This opinion is quantified and called the trust metric. Trust metric should reflect how much a router is expected to behave, for example, forward a packet when it receives it from a previous node.

Obtaining the trust metric is a problem by itself since it requires several operational tasks on observing nodes behavior, exchanging nodes' experience and opinions as well as modeling the acquired observations and exchanged knowledge to reflect nodes trust values. A system that provides these tasks to ultimately output a "rating" or a trust value on nodes is called a reputation system.

To appreciate the concept of trust behavior based routing, we provide in the next section some aspects that highlight the importance of trust aware routing.
