# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers
CA satisfies the subjectivity requirement, because the service requester defines and broadcasts the task and its requirements, and after the task's execution, the service requester gives feedback to the service provider, rating its performance, and the service provider then decides whether the task was successfully executed, modifying the weight of the relevant connection, based on the feedback received. **Context Awareness**. Since trust is context-dependent, a service provider may be trustworthy for one task but unreliable for another. The context typically refers to the task type, but it can also refer to an objective or execution environment with specific requirements [31]. CA ensures context awareness by allowing trustees to maintain distinct trust connections for different tasks requested by the same trustor. **Dynamicity**. Trust is inherently dynamic, continuously evolving in response to events, environmental changes, and resource fluctuations. Recent trust information is much more important than historical information [31]. The CA model satisfies the dynamicity requirement, since trust update is event-driven, taking place after each task execution, making it well suited for dynamic environments, where agents may frequently join, leave, or alter their behavior, as evidenced by the experiments in this study. **Availability**. A trust model should ensure that all entities and services remain updated [35] and fully available, even in the face of attacks or resource constraints [32]. In the CA approach, the service requester does not assign a task to a specific service provider but instead broadcasts the task to all nearby service providers. This ensures that even if a particular service provider is unavailable due to resource limitations (e.g., battery depletion), another service provider that receives the request can still complete the task. Consequently, our approach effectively meets the availability requirement. **Integrity and Transparency**. All transaction data and interaction outcomes should be fully recorded, efficiently stored, and easily retrieved [33]. Trust values should be accessible to all authorized network nodes whenever they need to assess the trustworthiness of any device in the system [34]. In the CA approach, trust information is stored locally by the trustee in the form of connections (trust relationships). This ensures that trust information remains available even when entities cross application boundaries or transition between systems. Additionally, any trustee can readily access its own trust information to evaluate its reliability for a given task, thereby fulfilling the integrity and transparency requirement. **Scalability**. Since scalability is linked to processing load and time, a trust model must efficiently manage large-scale networks while maintaining stable performance, regardless of network size, and function properly when devices are added or removed [31]. Large-scale networks require increased communication and higher storage capacity, meaning that trust models must adapt to the growing number of nodes and interactions [26; 35; 36]. However, many existing trust management algorithms struggle to scale effectively in massively distributed systems [22]. The CA model is designed for highly dynamic environments, ensuring strong performance despite continuous population changes. Previous research [7] has experimentally demonstrated CA's resilience to fluctuations in consumer populations. In this study, we present simulation experiments showing that the updated CA algorithm has significantly improved resilience, even when provider populations change. Additionally, since agents in the CA approach do not exchange trust information, the model avoids scalability issues related to agent communication. However, we have yet to evaluate how CA scales with an increasing number of nodes. **Overhead**. A trust model should be simple and lightweight, as calculating trust scores may be impractical for IoT devices with limited computing power [26].