Quoty Degree Factors Of Recommendations

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Therefore, the certainty degree factors of the received recommendations are: μ(0.3)=0.85, μ(0.29)=0.844 and μ(0.8)=0.85. The similarity between the values and the absence of other useful information do not allow the model to make a decision regarding the dishonest and honest recommendations. Second, the model can generate false positives and false negatives. In order to appreciate this fact, we consider another small set of n = 6 received recommendations with values 0.2, 0.25, 0.8, 0.8, 0.79, and 0.8. Obviously, the first recommendation (i.e. 0.2) is an erroneous reputation calculation due to bad channel communication between an honest recommender and the evaluated node. The second recommendation is, on the other hand, an outlier (i.e. …show more content…

Moreover, they may lie about their identity [46]. In our case, malicious sensors may lie about the assigned reputation value by sending dishonest recommendations to the evaluating node. In Bee-Trust Scheme, the recommenders must transmit the logs associated with their judgment regarding the reputation value assigned to the evaluated node as evidence of the transactions that caused their judgments. Whereas truth-telling involves only sending truthful logs associated with the corresponding reputation, lying additionally involves a decision to lie followed by the construction of a falsehood [44] namely divergence between the information contained in the log file and the assigned reputation value which make incompatible situation whether between logs as well as between logs and reputation. These incompatible tasks are more difficult and take longer to complete correctly; hence, slower responses diagnose dishonesty [44]. Dishonest recommender’s problem is a somewhat similar situation to that used in TARA. Recent research now abundantly confirms that, when responding to direct inquiries in a structured manner, people take longer on average to lie than, to tell the truth [43].Using the same reasoning, we introduce the response speed as an index of deception to detect dishonest recommenders. For each recommender, the response speed index can be calculated as: 〖RSI〗_(R_i )=D_(tr(A,R_i))/〖RT〗_(R_i ) (17)

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