# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 5 Trust-Based Information Fusion  
Information is vital in enhancing smart IoT applications. Sensors observe their environment by gathering and disseminating data to decision making nodes [45; 46; 47; 48]. However, some information sources might be malicious when they share their opinions. Along these lines, decision making nodes should wipe out opinions gathered from such malicious information sources [48; 49]. Hence, many trust-based fusion methods are suggested in the literature. Some methods use only the subjective logic's cumulative fusion operator [50; 51] to fuse trust opinions. These methods do not consider the trustworthiness of information providers before fusion. For example, no discount of opinions is performed before fusion. Other methods estimate the trust of information sources using threshold [51] and then discount and fuse the opinions. Subjective logic discounting and cumulative fusion operators are utilized in order to form the fused opinion. During information fusion, some approaches model the trustworthiness of information providers and utilize the estimated trust in order to discount opinions. For example, [PERSON] and [PERSON] [52] introduced a reputation system to assess the likelihood of a proposition utilizing probability density functions. As input parameters to their system, they considered a beta distribution with collected ratings of information sources. An extension to [52] is done by [PERSON] et al. [53] to manipulate misleading opinions from malicious information sources using a majority-based filtering technique. This method expects that malicious sources are in minority and hence, are ineffective. To overcome this limitation, another method proposed by [PERSON] et al. [54] utilizes individual observations about information sources to estimate their trustworthiness. However, lacking evidence about historical behaviour of information sources will make this method ineffective [48]. Other fusion methods in the literature consider various behaviors of malicious information sources and exploit them during fusion. These methods consider just the expected behaviors' probabilities during fusion. For example, the Hierarchical And Bayesian Inferred Trust model (HABIT) [55] and Bayesian Learning to Adapt to Deception in E-Marketplaces (BLADE) [56] exploit the change of information sources' behavior (such as flipping their opinions). These methods empower a node to utilize all available reports with minimizing the need for discounting or discarding opinions even if the opinions sources are viewed as malicious. [PERSON] et al. [49] proposed a trust-based fusion technique to estimate recommenders' honesty. This approach integrates trust into the fusion process to estimate the honesty of its recommenders. A likelihood model of the trustworthiness of nodes is used to scale the uncertainty of multiple estimates with parameters of trustworthiness. [PERSON] et al. [21] proposed a trust-based information fusion approach by diversifying the set of information sources to increase the heterogeneity of information providers and hence, reduce collusion attacks. Authors in [46] utilize trust-based information fusion strategy to appraise the trustworthiness of the information sources. This strategy utilizes subjective logic for discounting and fusing the source opinions. The strategy uses a nonlinear constrained optimization algorithm to detect and resolve conflicts during the discounting process. [PERSON] et al. [51] proposed an approach to derive trust evidence of information providers by exploiting conflicts and consistencies between individual observations of information providers. This approach starts by using the derived trust evidence to model the trustworthiness of information providers. The trustworthiness of the information providers is used to filter unreliable observations. During the aggregation process, a subjective logic cumulative fusion operator is used to fuse the filtered observations. Table 2 classifies trust-based information fusion based on different dimensions.