# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 4 Background
### Major Challenges of Trust Models  
Heterogeneity is considered a significant challenge to sensor-based system design for two reasons. First, computational power and storage capacity vary from IoT device to another such as RFID tags, sensors, laptops and smartphones. Second, connectivity method to the network varies from device to device such as Wi-Fi, cables, 3G, Bluetooth, and near field communication. Trust models manage the trust for these tiny wireless sensors located at the things layer of IoT systems. These devices have limited computation, storage, and power resources. This is a big challenge for IoT trust models. Also, sensor-based devices with various capabilities from various manufacturers must be able to communicate. As a consequence, existing trust protocols do not perform well to adapt this requirement [9; 13; 38].  
The dynamicity nature of sensor-based systems caused by the continues joining and leaving of sensors as well as smart applications is another challenge that needs to be considered when modeling trust [40]. In addition, sensor mobility can also create several challenges in terms of network and protocol efficiency [41].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Attack Type** & **Description** \\ \hline Self-promotion attack (SPA) & A dishonest node provides good recommendations for itself to promote its importance in order to be selected as a service provider. Then, it exploits its reputation to provide malicious service. An example of this attack occurs when a dishonest node positively fabricates fake feedback about itself or adjusts its own reputation during data dissemination. \\ \hline Bad-mouthing attack (BMA) & A dishonest node can ruin the trust level of well-behaved nodes by giving bad recommendations about them. Consequently, their reputation is negatively affected and the chance of these well-behaved nodes to be selected for service is reduced. \\ \hline Ballot-stuffing attack (BSA) & A dishonest node can boost the trust levels of other untrustworthy or dishonest nodes by giving good recommendations. As such, boosting their reputation. \\ \hline Opportunistic service attack (OSA) & An untrustworthy node with a bad reputation may provide good service at a certain time to improve its reputation. \\ \hline Collusion attack (CA) & This attack occurs when one or more nodes conspire together to defraud the trust level of one or more nodes. \\ \hline On-off attack (OOA) & An untrustworthy node can randomly perform trustworthy service to hide its untrustworthy behavior. \\ \hline Whitewashing attack (WWA) & An untrustworthy node can disappear and rejoin the application to wash away its bad reputation. \\ \hline Discriminatory attack (DA) & An untrustworthy node can discriminate against specific nodes. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trust-related attack types with their descriptions.  
The growing nature of sensors should be considered when designing trust models. As stated in [42; 43], there are 9 billion interconnected sensors and this number is expected to reach 20 billion by 2020. Hence, a trust model for smart applications should be scalable [7; 44].  
The extensive daily use of smart applications by humans is another challenge. Trust models have to consider social relationships among device holders and QoS attributes [13; 38] to increase the success of these sensor-based smart applications.