# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 4 Proposed IoE Taxonomy
### Knowledge
#### 4.1.3 Trust  
In a hybrid human-based and device-based environment, such as IoE, data's trustworthiness can be estimated mostly by the sensor nodes' reputation [37]. Trust management is a decisive challenge for data access and data storage on IoE applications [49; 108].  
Dynamic and heterogeneous network environments and the diversity of devices connected in the IoT generate an extensive array of potential security threats [27; 60; 61; 109]. The network interoperability level should address concerns such as the security of the data to be transmitted [64], and a coherent IoT architecture would provide a layer of data security [110; 111] since the IoT has no uniform architecture. Approaches and methods to improve users' awareness about the effects of potential IoT threats may mitigate the risk of exposure [53; 65; 68; 112].  
Knowledge assets vary in veracity levels [97], between the extremes of truth and untruth [52]. In some broad sense, the value of knowledge depends on the quality of the sensors' information. Security approaches must be made self-sufficient and autonomic, with the minimal manual human intervention [53]. Sensor networks' applications need support regarding privacy, security accuracy, timeliness, relevance, completeness, and provenance [46; 68]. The data source's reputation represents the source's truthfulness in providing quality content to handle changing external requirements and contexts [101]. Any direct or indirect connections of user information with connected objects within IoT landscapes categorize trust in communication and security issues [108][110]. The trust values are considered based on the reliability of devices and the level of security and trust engaged in implementing and operating the connectivity [113; 114]. Knowledge of sensors and sensor data in IoE applications is either trustful or untrustful:  
* Trustful: Based on protecting both user and service provider privacy precedents [40]. Constituting meaningful identity, using trusted communication paths, and preservingcontextual information is essential to guarantee the protection of users' privacy in the IoE environment [115]. The work in [55] addressed the security of IoT objects and privacy issues by merging identification, authentication, and authorization into one argument: access control. The security dimension encompasses five concepts: access control, confidentiality, integrity, availability, and non-repudiation. Different studies have covered concerns such as anonymity, liability, and moral, ethical, legal, cultural, and regional parameters, among other things [39; 45; 47; 116].
* Untrustful: False or misleading data culminates in wrong decisions and critical consequences and lead to uncertainty at all knowledge transformation levels. Incompleteness in data occurs at the lower layer of the sensor readings or raw data collected. Vagueness frequently appears at a higher level of contextual information [37; 69]. Possible security risks associated with IoT data are the heterogeneity of the smart devices and the nature of sensed data or authentication among different trust domains [56], which further complicates access control decisions.