# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 3 GNSS Data Compression Review and Related Work  
Today, the processors are very powerful and can process data and RAM rapidly. Nevertheless, a busy communication channel can be challenging. The busier the communication channel and the more information passes through it, the more information can be delayed. The transmission time is significantly greater than the processing time, which is negligible in the transmission time [31]. That is why previous investigations have assessed the amount of information transferred and not the processing times [32], A procedure we follow later in the results section. Among several recent studies and articles about GNSS data compression, one that was recently carried out examined the compression of GNSS data in maritime usage by ships and vessels. To the best of our knowledge, there is no study of data compression originating from GNSS in combination with autonomous vehicles. This work reports on the ability to compress data at a very high efficiency' obtaining a compression ratio of about 4% of the raw information [33]. However, naval vessels generally avoid turns due to their dimensions and usually perform only prearranged turns in very wide areas. Commonly, ships do not navigate many turns or U-turns, unlike vehicles, but rather go in straight lines [34]. Therefore, the changes in the GNSS information are typically small and yield a much better compression ratio. A recently published paper explored and proposed GNSS data compression in IOT components and trajectory reconstruction. Unlike in our data, the authors of this paper consider several different trajectory typologies. They compress this data employing a combination of their suggested technique with a lossless compression method, trying as a lossless method the well-known methods--Huffman, LZ77 and LZW. Nonetheless, they came to a similar conclusion that combining the Huffman codes with another method indeed gives better results than just compressing the data using Huffman codes [35]. The subject of GNSS data compression has already previously been studied; however, the studies are from different directions with usually dissimilar approaches, unlike our research. Some of the studies that have been done on the subject compress completely different information [36]. One proposed compression algorithm [37] requires accompanying hardware and other supporting equipment such as a server that performs data analysis, compression and transmission. Working with a server cannot be practical for our research because it works in a real time environment. An algorithm more similar to our work is suggested in [38]. It analyzes NMEA data compression using some combinations with LZ77 and Huffman coding. The compression results in this paper are an output of about 30% of the original information. Furthermore, the writers have taken out some of the information so that all the records will have the same fields and the compression ratio will be enhanced. Even with these features, this paper achieved significantly inferior results because the differences method, such as H.264, which can substantially improve the performance, is not employed. In our research, we made use of H.264 and as a result were able to get much better results of about 13%. The goal of compression is to efficiently reduce the amount of information transmitted by GNSS. Raw data transmitted by GNSS is very expensive, costing thousands of dollars per day and millions of dollars per year for only about 4000 vehicles that use it [39]. Today every vehicle (even non-autonomous) has built-in GNSS components [40], but the topic becomes very significant when we talk about GNSS in autonomous vehicles [41]. The amount of information that these vehicles transmit will be significantly bigger. One of the components constantly changed and transmitted across bandwidths is the location data of the vehicle. This research presents a method for compressing GNSS data as a difference between location and time.