# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 9 Concluding Remarks  
The IoT vision comes as a result of connecting heterogeneous nodes ranging from physical entities to smart devices. As stated in [42; 43], there are 9 billion interconnected nodes and this number is expected to reach 20 billion by 2020. To benefit from the vision of IoT, data sources are the first step to be scrutinized. Usually, WSNs are utilized to gather and disseminate data. Data fusion techniques are then applied to discover desirable features or to enhance decision making. Finally, the fused data is consumed by smart applications in order to provide intelligent services.  
Data sources' misbehavior renders IoT smart applications unreliable. Therefore, during data collection and data fusion, we must select only trusted data sources and fusion centers. Since these sensors are the resource/information providers to fusion centers, a sensor could be captured by an adversary, which may lead to its non-cooperative behavior or misbehavior. In addition, a sensor can itself become untrustworthy. As such, IoT trust models are an essential requirement for information fusion and hence, for the success of IoT intelligent services. Designing an efficient architecture faces many issues such as heterogeneity, scalability, mobility, and constrained capabilities of various IoT entities.  
Centralized IoT architectures [33; 44; 61; 63; 64; 66; 75], where a central trust manager computes and stores trust values, have common issues including single-point of failure and the difficulty of maintaining the global view of all involved IoT devices due to the dynamicity nature of IoT systems. Furthermore, traffic bottleneck is another issue when a large number of IoT sensors interact with the centralized trust manager consuming energy and disrupting communication bandwidth.  
On the other hand, distributed trust models for IoT environments can be divided into three class, namely object-layer-based, fog-layer-based, or cloud-layer-based. Distributed architectures for  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multicolumn{4}{c}{**Simulation Tool**} & \multicolumn{4}{c}{**Simulation Metrics**} \\ \cline{2-10}  & **NS2** & **NS3** & **Cooja** & **Matlab** & **Others** & **Accuracy** & **Convergence** & **Resiliency** & **Others** \\ \hline [68] & & & & & ✓ & ✓ & ✓ & ✓ & \\ [39] & ✓ & & & & ✓ & ✓ & ✓ & \\ [69] & & & & & ✓ & ✓ & ✓ & ✓ & ✓ \\ [82] & & ✓ & & & & ✓ & ✓ & ✓ & \\ [84] & & ✓ & & & & ✓ & ✓ & & \\ [81] & &  
modeling trust such as [68; 69; 70] implement the trust model at the things layer which has hardware constraints in terms of limited computing and energy resources. As such, these things will not be able to support basic functions like trust computation, trust propagation, trust updates, and trust storage [91]. To the best of our knowledge, the only distributed architecture that implements the trust model in the fog layer is [7]. This work allows smart objects to utilize the blockchain as storage to advertise their trust levels about other nodes. Scalability and mobility support of this architecture are inherited from the blockchain technology. Some functionalities, such as trust storage, of a trust model can also be implemented at the cloud layer. This implementation inherits similar disadvantages as the centralized IoT architectures.