## 2 Related Research
### Quality Management in Agent Systems  
The solutions to managing quality or non-functional requirements, such as trust, reputation, and privacy, for agent systems were identified in a systematic literature review [10]. Trust and reputation management models, such as REGRET [11], A Fuzzy Reputation Agent System (AFRAS) [12], FIRE+ [13], Nusrat [14], and ScubAA [15], have previously been proposed. Table 1 presents the mapping of agent quality management literature with the quality properties. For managing trust and reputation, the FIRE+ model [13] incorporated information from multiple sources to manage trust and reputation and to comprehensively assess agent performance in open systems. [PERSON] and [PERSON] [14] proposed multi-faceted trust values to be implemented in decentralized user modeling to manage trust and reputation. ScubAA [15] was presented as an agent trust management framework for open systems. The framework considers multiple sources of evidence to find the most trusted service agents in open agent systems. [PERSON] et al. [19] discussed a rule-based fuzzy ranking model to evaluate trust between a seller and buyer. Fuzzy Logic Based Trust Establishment (FTE) [20] and Trust Establishment Model Using Indirect Feedback (TEIF) [21] manages trust in the situation where direct feedback is not available and uses an agent behavior model to establish trust for agent interaction. [PERSON] et al. [22] used temporal difference learning method to evaluate trust based on agent interaction experience. [PERSON] and [PERSON] [23] proposed an enhanced trust algorithm by implementing a hybrid multi-faceted computational trust model for an online social network. The previously proposed privacy management models for privacy properties include i*framework [16], Privacy as Contextual Integrity in Decentralized Multi-Agent Systems (PrivaCIAS) [18], and Privacy Management [17]. The privacy management using i*framework defines the agent privacy requirements and reasoning of solutions via a systematic framework [16]. The PrivaCIAS privacy model considers requirements and solutions to detect and prevent privacy violations [18]. [PERSON] et al. [17] proposed a user-centered privacy management that deals with the privacy policy. [PERSON] et al. [24] presents an ontology for privacy requirements for systems that deal with personal information. The existing works mentioned above have proposed solutions for some agent quality issues in general agent systems. Agent systems in smart city applications, however, have more complex interconnected data from multiple data collection sources and dynamic contextual information. Other quality requirements, such as confidentiality and integrity, also have not been extensively focused on. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{
\begin{tabular}{c} **Agent Quality** \\ **Management Literature** \\ \end{tabular} } & \multicolumn{5}{c}{**Quality Properties**} \\ \cline{3-6}  & & **Trust** & **Reputation** & **Privacy** & **Confidentiality** & **Integrity** \\ \hline
1 & REGRET [11] & - & ✓ & - & - & - \\
2 & AFRAS [12] & ✓ & ✓ & - & - & - \\
3 & i*framework [16] & - & - & ✓ & - & - \\
4 & FIRE+ [13] & ✓ & ✓ & - & - & - \\
5 & Privacy Management [17] & - & - & ✓ & - & - \\
6 & [14] & ✓ & ✓ & - & - & - \\
7 & PrivaCIAS [18] & - & - & ✓ & - & ✓ \\
8 & ScubAA [15] & ✓ & ✓ & - & - & - \\
9 & [19] & ✓ & - & - & - & - \\
10 & FTE [20] & ✓ & ✓ & - & - & - \\
11 & TEIF [21] & ✓ & ✓ & - & - & - \\
12 & [22] & ✓ & - & - & - & - \\
13 & [23] & - & - & ✓ & - & - \\ \hline \hline \end{tabular}  
* AFRAS—A Fuzzy Reputation Agent System; FTE—Fuzzy Logic Based Trust Establishment; TEIF—Trust Establishment Model Using Indirect Feedback; PrivaCIAS—Privacy as Contextual Integrity in Decentralized Multi-Agent Systems.  
\end{table} 
Table 1: The agent quality management literature with their associated quality properties. Furthermore, the issues of agent quality properties violations that occur during run-time have not been extensively researched.