# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 5 Trust: State of the Art in Smart Grid
### Research Areas  
[PERSON] et al. sought to detect the credibility of data from different sources by establishing trust from the said sources [29]. Though they were not specific about which part of Smart Grid they were working on, their work implied that it could be used in all areas of the Smart Grid because it deals with big data. In their paper, they used trust and credibility interchangeably. Even though the knowledge component exists in terms of previous trust values and a forgetting rate, the measure of risk on the data from the data source and the data source itself was not computed. There were no tests against trust-based attacks. Moving away from big data to secure routing, another paper sought to compute trust for secured routing in wireless-based communications in the Smart Grid [30]. Network-based features such as the average transmission rate, buffering capacity and time-to-live (TTL) are used to compute trust. Their algorithm first computes direct trust between nodes; indirect trust based on recommendations from other nodes; and finally uses that information to compute how to route information from one node to another within the Smart Grid communication infrastructure. This algorithm would work best in AMI but not in the generation and distribution domains of the Smart Grid where communications are more wired than wireless. This paper improved their previous trust model to identify benign and malicious nodes based on various features using a combination of Bayes, Dempster-Schafer and Fuzzy theory [31]. They employed a water cycle algorithm (WCA) to improve its efficiency and tested it using an NS-2 simulator. The parameters used are clear indicators of the knowledge component of trust; however, there was no measurement for risk to show the impact should a node be wrongfully trusted. The algorithm was also not tested against trust-based attacks. Another paper also proposed a fuzzy logic-based trust model to ensure secure routing in the network [32]. It computes a global trust value by computing direct and indirect trust to allow nodes to make decisions on compromised nodes. They tested their work against trust-based attacks, but their algorithm had no risk component. Still focusing on routing, [PERSON] et al. presented a trust-based geographical routing protocol which placed trusted nodes in a trust list [33; 34; 35]. To be part of the trusted list, the node was required to have a good performance ratio as well as a good recommendation from other nodes. Based on that list, a routing algorithm is implemented to route from one trusted node to another. Their work did not include a risk component and was not tested against trust-based attacks, even though it was tested against WSN-based DOS attacks. Their experiment was simulated using a Java-based simulator called J-Sim. Though not creating their trust model, [PERSON] et al. explored the impact of transitivity in network topology in the performance evaluation of the famous EigenTrust model [36]. They demonstrated that a network containing established transitivity connections implied that a benevolent node was quickly identified by a node, thereby reducing the average energy consumption. An improved version was tested against trust-based attacks and showed that structural similarity has an impact on robustness against trust-based attacks and malicious nodes [37]. In trying to detect a compromised node in a network, a trust management model was proposed based on fuzzy logic using the packet error rate, interaction duration and packet loss rate as features [38] to compute trust. There was no risk component in the calculation, and neither was the algorithm tested against trust-based attacks. The trust model was simulated using Xfuzzy-3.5. Moving away from networks, and still within AMI, [PERSON] et al. computed trust based on three features, namely consumption, polling, and connection to detect malicious devices [39]. The continuous-time Markov chain was utilized to compute the trust value of a node. It was purely tested with numerical parameters.