**1. Introduction**

The emergence of Internet of Things (IoT) deployments has allowed millions of connected, communicating, and exchanging objects to be embedded seamlessly around the world, generating large amounts of data through sensor monitoring on a timely basis.

The data flow between the physical and the digital world through artificial intelligence can expand the computer's awareness of the surrounding environment, thereby obtaining the ability to act on behalf of humans through ubiquitous services.

In this IoT-based environment, the basis for making wise decisions and providing services is the data collected by sensors and actuators. If the data quality is poor, these automated decisions may be incorrect, ranging from sensor failure to deliberately providing false information with malicious intent. Data quality (DQ) is therefore needed to attract users to participate and accept IoT paradigms and services.

#### *Data Integrity and Quality*

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

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

should be saving or a date in a format that is not adequate.

tees the reliability of the data in physical and logical terms.

*Q* !

The elements of the vector are defined as follows:

• Completeness (*qcmp*): it represents the percentage of missing or the

• Domain integrity is the set of processes that guarantee the veracity of each data in a domain. In this context, a domain is a set of acceptable values that a column can contain. You can incorporate restrictions and other measures that limit the format, type, and amount of data entered. Due to an error in the IoT devices, one of them could be entering data that does not correspond to the correct type in a column of a database, such as saving a number when a date

• User-defined integrity comprises the rules and constraints created by the user to suit their particular needs. Sometimes entity, referential, and domain integrity are not enough to safeguard data. Often times, specific corporate rules need to be considered and incorporated into measures regarding data integrity. In an IoT scenario, a sensor may be giving acceptable values, that is, that they respect the rest of the integrity criteria, however, it may not be meeting a necessary criterion for the correct functioning of the system, such as a sensor that collects percentage values and that you are receiving a value greater

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 guaran-

In this section, we describe the metrics that have been defined to calculate and

The first set of metrics is based on a descriptive analysis. This approach was also used on the IoTCrawler framework [9]. It proposes to integrate quality measures and analysis modules to rate data sources to identify the best fitting data sources to get the needed information. The first step before implementing some quality analysis modules is to identify quality measures, which can be used to rate data sources and the delivered/produced data for their Quality of Information. To measure the QoI, we propose to use the so-called QoI Vector, which is defined in Eq. (1) and gathers the information belonging to all the metrics proposed in this

<sup>¼</sup> *qcmp*, *qtim*, *qpla*, *qart*, *qcon* D E (1)

annotate the QoI for IoT data. Those were previously described on [8].

deletions occur.

*Quality of Information within Internet of Things Data DOI: http://dx.doi.org/10.5772/intechopen.95844*

than 100.

**3. Data quality metrics**

**3.1 QoI basic metrics**

framework

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unusable data.

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

According to [5], ensuring data quality is crucial when deploying and leveraging devices, given that:


The way to tackle this problem is through the use of so-called data quality metrics which are calculated in order to validate the Quality of the Information (QoI).

The aim of this chapter is to define some metrics for DQ and calculate them in IoT scenarios in order to test their viability.
