# A Smart Parking Solution Architecture Based on LoRaWAN and Kubernetes
## 2 Related Works
In order to ensure proper communication both the raspberry pi and mobile application must be subscribed to a particular channel on IBM MQTT server. The chip used to connect parking sensors on each parking lot has a maximum of 26 different connection, even though this number can increase using a multiplexer, it will also increase cost on a large scale since it would have to be done on each parking lot depending on the number of slots that these would handle. In [16; 19], the described infrastructures are similar since both sense the information from each parking lot slot. Then, send it to a gateway that will not necessarily be for a single parking lot. Later, information is sent to a network server. Finally, the networks server will be in charge of delivering the information to end users through an application or dashboard. In terms of sensors, the most widely used option is ultrasonic sensors as described in [8; 11; 15; 18; 24]; however, these sensors present some problems: noise can interfere with the reading of the parking slot status, and for the general purpose of the sensor, it can detect large objects, even if it is not a car. Induction Proximity Sensor [24] is used to detect when a vehicle enters or leaves from a parking lot. This sensor is usually buried under the ground. It detects vehicles by measuring the change in the earth's magnetic field as the vehicle passes over. If the magnetic field varies by a certain threshold value, the sensor determines that a vehicle has passed over. This sensor is very good at detecting metal objects over the sensor (such as vehicles) and will not send a false positive if someone walks over the sensor. The disadvantage is that it needs to be buried under the parking spot. Radio Frequency Identification sensors (RFID) [24], can be used to detect if a vehicle is occupying a parking spot. An advantage of these sensors is that they do not detect other objects, such as people or animals. A disadvantage of these sensors is that it is required that the RFID must be present in the vehicle. The authors also propose LIDAR sensors which are very similar to ultrasonic sensors but use pulses of light instead of RF signals. These sensors are more precise, and their range is greater, but they are more expensive than an ultrasonic sensor. The solution in [22] implements RFID as sensors. Readers are at the entrance and exit of the parking lot, opening the barriers for the allowed cars with RFID tags. These tags are for registered and temporal users. Each tag belongs to a parking slot, also it helps to collect information about occupation, entrance and exit time. The information collected is sent through Wi-Fi to a cloud server that saves it in a database for future study. Finally, cameras in [25] are also used to detect parking slots, but they have to be strategically positioned to cover a wide range of spaces, these require more processing and the designed algorithms are not a 100% reliable. Although this solution aims to detect vehicle presence, it lacks of further components to share data with other elements like application servers or user devices. The following table (see Table 1) provides a summary of the technologies, smart parking layers, features and issues identified among the related works reviewed. The table is structured in the following is composed of five columns. First column is Reference which is used to identify the reviewed work. The Technology column contains the described technology used in the proposed work of the authors pointed out in first column. This technology applies to the components described for deploying a smart parking system. Then, Smart Parking Layer column points out the layer (Sensing, Network Infrastructure and Application) where the technology is used. Finally, the last column is used to identify if the technology used represents a positive feature or represents an issue for that particular implementation by using a \(\mathcal{O}\) to mark the feature as an issue, whilst a \(\sqrt{}\) to identify a positive feature that is remarkable from the reviewed work.