**2. Data integrity**

Data integrity refers to the accuracy and reliability of data. The data must be complete, without variations or compromises from the original, which is considered reliable and accurate. Therefore, this term is closely related to the quality of the data and this in turn to the quality metrics [6].

There are several types of data integrity [7]:


Data Quality refers to how well data meet the requirements of data consumers [1]. In a similar manner, Quality of Information (QoI) relates to the ability to judge

From such a well-known and accepted definition, we understand that it refers to a perception or an evaluation of the suitability of the data to fulfill its purpose in a given context, subject to the requirements of the consumer. On the literature, the quality of the data is determined by factors such as availability, usability, reliability

According to [5], ensuring data quality is crucial when deploying and leveraging

• Decision-making is only possible if the data available are correct and

• Serious problems are practically unapproachable without an adequate data

The way to tackle this problem is through the use of so-called data quality

The aim of this chapter is to define some metrics for DQ and calculate them in

Data integrity refers to the accuracy and reliability of data. The data must be complete, without variations or compromises from the original, which is considered reliable and accurate. Therefore, this term is closely related to the quality of the data

• Physical integrity is the protection of the integrity and accuracy of data as they are stored and extracted. That is, it is related to the physical layer of the systems. In the context of IoT, a physical integrity problem comes from the physical degradation of the sensors, whether due to a breakdown or sabotage.

differently in a relational database. Logical integrity protects data from human

• The integrity of the entity is based on the creation of primary keys, or unique values, that identify data to ensure that it is not listed more than once and that there is no field in a table considered null. It is a feature of relational systems that store data in tables that can be linked and used in very different ways. In an IoT scenario, an entity integrity problem can arise in case of a sensor failure which produces redundant measurements or by a human failure in which two

• Referential integrity is a series of processes that ensure that data is stored and used consistently. The rules built into the database structure about how foreign

• Logical integrity preserves the data without any change, since it is used

errors and also from hackers, but in a very different way than physical

different sensors are assigned the same identifier, which produces

metrics which are calculated in order to validate the Quality of the

whether information is adequate for a particular purpose [2, 3].

accuracy, completeness, relevance, and novelty [4].

IoT scenarios in order to test their viability.

and this in turn to the quality metrics [6].

There are several types of data integrity [7]:

devices, given that:

*Data Integrity and Quality*

appropriate.

source.

Information (QoI).

**2. Data integrity**

integrity.

**78**

redundancies in databases.

keys are used to ensure that only appropriate data changes, additions, or deletions occur.


In this section, Data Integrity has been defined, however, it is necessary to note what is the difference between this term and the term Data Quality. Data quality is related to the reliability of the information, which is necessary for planning and decision making for a specific operation. Whereas, the integrity of the data guarantees the reliability of the data in physical and logical terms.
