A Trust Evaluation Algorithm for Wireless Sensor Networks Based on Node Behaviors and D-S Evidence Theory

Abstract

For wireless sensor networks (WSNs), many factors, such as mutual interference of wireless links, battlefield applications and nodes exposed to the environment without good physical protection, result in the sensor nodes being more vulnerable to be attacked and compromised. In order to address this network security problem, a novel trust evaluation algorithm defined as NBBTE (Node Behavioral Strategies Banding Belief Theory of the Trust Evaluation Algorithm) is proposed, which integrates the approach of nodes behavioral strategies and modified evidence theory. According to the behaviors of sensor nodes, a variety of trust factors and coefficients related to the network application are established to obtain direct and indirect trust values through calculating weighted average of trust factors. Meanwhile, the fuzzy set method is applied to form the basic input vector of evidence. On this basis, the evidence difference is calculated between the indirect and direct trust values, which link the revised D-S evidence combination rule to finally synthesize integrated trust value of nodes. The simulation results show that NBBTE can effectively identify malicious nodes and reflects the characteristic of trust value that ‘hard to acquire and easy to lose’. Furthermore, it is obvious that the proposed scheme has an outstanding advantage in terms of illustrating the real contribution of different nodes to trust evaluation.

Human Safety and Secure Trust Management Schemes for Underwater Wireless Sensor Networks

“…To overcome the drawbacks of probability and Bayesian theory chen and groups studied the fuzzy logic and the inference rule is established to implement the fuzziness and it cannot provide a specific quantitative method for trust value 7 .Due to the above rule it cannot be suit in UWSNS.…”

Fuzzy Based Secure Data Aggregation Technique in Wireless Sensor Networks

“…The proposed protocol is combined with a Fuzzy Logic inference system to aid in the selection of the best route based on a combination of three factors: the path length, the available power and the node reputation resulted from the Intrusion Detection System (IDS). Feng et al (2011) have proposed a node behavioral strategies banding belief theory of trust evaluation algorithm that integrates the approach of nodes behavioral strategies and modified evidence theory. They employed a fuzzy set method temporarily to form the basic input vector of evidence.…”

“…• Fuzzy matching: the degree to the input fundamental steps and condition of the fuzzy logic are determined • Inference: on the basis of the degree of match, the conclusion of the rule is determined • Combination: the result obtained by every fuzzy rules are merged together into a single overall result (Feng et al, 2011) Rule definition: A fuzzy set A in X is characterized by a membership function which are easily implemented by fuzzy conditional statements. In the case of fuzzy statement if the antecedent is true to some degree of membership then the consequent is also true to that same degree.…”

Reputation System Based Trust-Enabled Routing for Wireless Sensor Networks

“…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.…”