## 6 Evaluation of the USN-Platform Semantic Extension
### Overhead of Semantic Information  
First, taking as reference the original O&M language encoded in XML, we analyzed the overhead of including semantic RDF annotations into smart device observations using a RDF Scheme based on the O&M-OWL ontology and encoded also in XML. Three of the most common types of messages in our connected car use case were considered. Message A, a simple observation where an electric vehicle communicates its current battery charge level; message B, containing two observations: the specific recharge station where the vehicle wants to stop and its current charge level; and message C, containing several general vehicle observations including current velocity, acceleration, number of passengers, tire pressure, motor temperature and brake status, battery level and the desired recharge station. It is relevant to point out that all of the observations in these messages B and C are related to  
Figure 6: Implementation of the Telco USN-Platform to provide semantic web functionality. the same feature of interest (_i.e._, the electric vehicle in question) and are attached to the same location and sampling time. Table 1 compares the size (in bytes) of the three types of message with the observations represented in O&M, and also when they are semantically described using O&M-OWL (in all cases an XML format with no compression was used). Aiming to compensate for the overhead required by O&M-OWL observations encoded in XML, we tested different compression mechanisms that are commonly used in this sort of scenario (for example when encoding SOAP XML messages exchanged by Web Services [36]). We compared some of the most common binary encoders Gzip, Fast Infoset (FI) and Efficient XML Interchange (EXI) to encode and compress the three types of message considered (A, B and C). Gzip (www.gzip.org) is a general lossless compression mechanism than can be used satisfactorily when the data has statistical redundancy. However, when the volume of information is small, Gzip does not always perform well enough with XML data, even to the point of increasing the size of the file after compression. FI [37] and EXI (www.w3.org/XML/EXI) are specifications of binary encoding of XML data, which they can represent in compact form with reasonable overhead. In addition, while Gzip is mainly directed to size optimization and the processing time for compressing and expanding files can outweigh its benefits, EXI is able to achieve not only good compression rates but also better performance than Gzip. Table 2 shows the size in bytes and the relative compression compared to the raw (_i.e._, XML) format of each one of our three message types using Gzip, FI and EXI. From the results in Table 1 we can see that the overhead of adding RDF annotations to O&M depends on the type of message. Message A, which contains only one observation, has a relative overhead of around 118% when including semantic information. This overhead is mainly due to the need to describe the semantic relationships between the different entities that are involved in the observation, according to the O&M-OWL ontology. However, for messages containing several observations related to the same feature of interest and linked to a same location and sampling time (as is the case of Message B and Message C), the relative overhead decreases because many RDF relationships need only to be defined once, as they are common to all of the observations. Therefore, as the values in Table 1 show, the relative overhead of their semantic description is lower than that of Message A containing a single observation. Looking at the results in Table 2 we can conclude that despite the overhead of semantically annotated observations in O&M-OWL, by using binary encoding it is possible to reduce the size of the observations to maintain very efficient information exchanges with the USN-Platform. The best performance was achieved using the EXI encoder, which is the binary encoder that has been proposed by the W3C for smart devices (see, e.g., [38]).