The Application of Internet of Things Technology in Smart Trolley Cases: Performance Evaluation of GPS Tracking and Biometric Locks

1. Introduction

2. IoT – enabled Smart Trolley Case Technology

2.1 Overview of IoT in Smart Trolley Cases

The Internet of Things (IoT) forms the technological backbone of smart trolley cases, enabling a seamless integration of various components and functions. In a smart trolley case, IoT technology allows for the connection of sensors, actuators, and communication modules, facilitating the collection, transmission, and processing of data. This connectivity empowers the trolley case to interact with the user’s mobile device and other external systems, creating a truly intelligent and responsive travel companion.

Sensors play a pivotal role in the IoT – enabled smart trolley case ecosystem. They are responsible for gathering data on various aspects of the luggage, such as its location, movement, and the status of the lock. For example, motion sensors can detect when the trolley case is being moved, tilted, or dropped, while environmental sensors can monitor temperature, humidity, and pressure inside the case. This data is then transmitted via communication modules, typically Wi – Fi, Bluetooth, or cellular networks, to a central processing unit, which can be either a local device like a smartphone or a cloud – based server.

The integration of IoT in smart trolley cases also enables remote control and monitoring. Through a dedicated mobile application, users can remotely lock or unlock the biometric lock, check the real – time location of their luggage, and receive alerts in case of any 异常情况. This level of connectivity and control not only provides convenience but also enhances the security of the luggage, as the user can always keep track of its whereabouts and ensure that it is properly secured.

2.2 GPS Tracking Technology in Smart Trolley Cases

2.2.1 Principles of GPS

The Global Positioning System (GPS) is a satellite – based navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth. It consists of a constellation of at least 24 satellites orbiting the Earth at an altitude of approximately 20,200 kilometers. These satellites continuously transmit signals containing information about their position and the current time.

A GPS receiver in a smart trolley case works by measuring the time it takes for signals from at least four satellites to reach the receiver. Since the speed of light is constant, the receiver can calculate the distance between itself and each satellite based on the time delay. By using a process called trilateration, the receiver can then determine its precise location in three – dimensional space (latitude, longitude, and altitude) based on the distances to the satellites.

In the context of smart trolley cases, the GPS module is integrated with other components of the IoT system. Once the GPS receiver determines the location of the trolley case, this information is transmitted to the user’s mobile device or a cloud – based platform via a communication module. The user can then view the location of their luggage on a map through a mobile application, which provides real – time updates as the luggage moves.

2.2.2 Integration with IoT in Smart Trolley Cases

The integration of GPS with IoT in smart trolley cases is a complex yet highly effective process. The GPS module in the smart trolley case is connected to a microcontroller, which acts as the central processing unit for the IoT system. The microcontroller receives the location data from the GPS module and processes it. It then sends this data, along with other sensor data (such as motion sensor data), to a communication module.

The communication module, which can be a Wi – Fi module, a Bluetooth module, or a cellular module, is responsible for transmitting the data to the outside world. In most cases, the data is sent to a cloud – based platform, where it can be stored, analyzed, and accessed by the user. The cloud – based platform also serves as a hub for communication between the smart trolley case and the user’s mobile device.

The user’s mobile device, equipped with a dedicated mobile application, can communicate with the cloud – based platform to receive the real – time location data of the smart trolley case. The mobile application can display the location of the luggage on a map, and it can also provide additional features such as setting up geofences. Geofences are virtual boundaries that, when crossed by the smart trolley case, trigger an alert to be sent to the user’s mobile device. This can be useful in scenarios where the user wants to be notified if their luggage leaves a certain area, such as an airport terminal or a hotel room.

2.3 Biometric Lock Technology in Smart Trolley Cases

2.3.1 Types of Biometric Locks

There are several types of biometric locks that are commonly used in smart trolley cases, each with its own unique characteristics and advantages.

Fingerprint Locks: Fingerprint locks are one of the most widely used biometric locks in smart trolley cases. They work by scanning the user’s fingerprint and comparing it to a pre – stored fingerprint template in the lock’s memory. Fingerprint sensors can be either optical or capacitive. Optical sensors use light to capture an image of the fingerprint, while capacitive sensors use electrical current to detect the ridges and valleys of the fingerprint. Fingerprint locks offer high accuracy and are relatively fast in operation, allowing for quick access to the luggage.

Facial Recognition Locks: Facial recognition locks use cameras to capture an image of the user’s face and analyze its unique features. These features are then compared to a pre – stored facial template. Facial recognition technology has advanced significantly in recent years, and modern facial recognition locks in smart trolley cases can provide accurate and reliable authentication. They offer the convenience of hands – free access, as the user simply needs to stand in front of the lock for it to recognize their face.

Iris Recognition Locks: Iris recognition locks scan the unique patterns in the user’s iris. The iris is a highly unique part of the human body, and iris recognition technology is considered one of the most accurate biometric authentication methods. However, iris recognition locks are less commonly used in smart trolley cases compared to fingerprint and facial recognition locks due to their higher cost and the need for more complex hardware.

2.3.2 How Biometric Locks Work within the IoT Framework

In an IoT – enabled smart trolley case, biometric locks are integrated with other components of the system to provide a seamless and secure user experience. When a user attempts to unlock the smart trolley case, the biometric sensor (such as a fingerprint sensor or a facial recognition camera) captures the user’s biometric data.

This data is then sent to a microcontroller, which processes the data and compares it to the pre – stored biometric template. If the match is successful, the microcontroller sends a signal to an actuator, which unlocks the lock. The microcontroller is also connected to a communication module, which can send information about the lock status (locked or unlocked) to a cloud – based platform.

The cloud – based platform can store records of all lock – related activities, such as when the lock was unlocked, by whom, and any failed authentication attempts. This information can be accessed by the user through a mobile application, providing an additional layer of security and accountability. In case of a security breach or an unauthorized access attempt, the cloud – based platform can also send an alert to the user’s mobile device, notifying them of the potential threat.

3. Performance Evaluation of GPS Tracking

3.1 Accuracy Testing

3.1.1 Methodology

To assess the accuracy of GPS tracking in smart trolley cases, a series of experiments were conducted. Multiple smart trolley cases equipped with GPS – enabled IoT systems were used. The experiments took place in various environments, including open outdoor areas, urban settings with tall buildings, and indoor locations such as shopping malls and airports.

In each environment, the smart trolley cases were moved along pre – determined routes. The actual positions of the trolley cases were recorded using a high – precision reference positioning system, such as a differential GPS (DGPS) receiver. The DGPS system provides more accurate location information by correcting for errors in the standard GPS signals, making it suitable for use as a reference in accuracy testing.

At regular intervals, the location data provided by the GPS tracking system in the smart trolley cases was also recorded. This data was then compared to the reference location data from the DGPS system. The difference between the two sets of data, in terms of latitude and longitude, was calculated to determine the accuracy of the GPS tracking system in the smart trolley cases.

3.1.2 Results

The results of the accuracy testing showed that in open outdoor areas, the GPS tracking systems in the smart trolley cases generally performed well. The average error in location determination was within a few meters, with most readings being accurate to within 2 – 5 meters. This level of accuracy is sufficient for travelers to easily locate their luggage in an open space, such as an airport parking lot or a hotel courtyard.

However, in urban settings with tall buildings, the accuracy of the GPS tracking systems deteriorated. The presence of tall buildings can cause multipath interference, where the GPS signals bounce off the buildings and reach the receiver at different times. This led to an increase in the average error, with some readings showing errors of up to 10 – 15 meters. In some extreme cases, where the signal was severely blocked, the GPS tracking system was unable to provide accurate location information for short periods.

Indoor locations presented the greatest challenge for the GPS tracking systems. GPS signals are unable to penetrate most building materials effectively, resulting in very low signal strength or no signal at all. In shopping malls and airports, the average error in location determination was extremely high, often in the range of tens of meters or more. In some cases, the GPS tracking system completely failed to provide any location data.

3.2 Reliability Testing

3.2.1 Methodology

Reliability testing of the GPS tracking systems in smart trolley cases focused on their ability to consistently provide accurate location information over an extended period. The testing was carried out over a period of several days, during which the smart trolley cases were subjected to normal travel – related conditions.

The smart trolley cases were moved between different locations, including long – distance trips by car, train, and plane. The GPS tracking systems were continuously monitored, and any instances of signal loss, inaccurate readings, or system failures were recorded. In addition, the power consumption of the GPS modules was measured to determine their impact on the overall battery life of the smart trolley cases.

3.2.2 Results

The reliability testing revealed that the GPS tracking systems in the smart trolley cases had a relatively high rate of signal loss during long – distance travels. When traveling by plane, for example, the GPS signals were lost for extended periods while the plane was in the air. This is due to the high altitude and the fact that the plane’s metal structure can block the GPS signals. Even after landing, it sometimes took several minutes for the GPS tracking system to reacquire a stable signal.

During car and train trips, the GPS tracking systems also experienced intermittent signal loss, especially when passing through tunnels or areas with poor network coverage. In some cases, the GPS module would continue to provide inaccurate location information even after the signal was restored, leading to confusion for the user.

In terms of power consumption, the GPS modules in the smart trolley cases were found to be relatively power – hungry. Continuous use of the GPS tracking feature significantly reduced the battery life of the smart trolley cases. In some cases, the battery would be depleted within a day or two of normal travel use if the GPS tracking was left on continuously. This highlights the need for more efficient power management systems in smart trolley cases to ensure the reliable operation of the GPS tracking feature during long – distance travels.

3.3 User – friendliness in GPS Tracking

3.3.1 Ease of Use in Mobile Applications

The user – friendliness of the GPS tracking feature in smart trolley cases is highly dependent on the design of the mobile applications used to access the location data. A usability study was conducted with a group of test users who were asked to use the mobile applications associated with the smart trolley cases to track the location of their luggage.

The study found that the ease of use of the mobile applications varied significantly among different brands. Some applications had a simple and intuitive interface, with clear instructions on how to view the location of the luggage, set up geofences, and receive alerts. These applications were well – received by the test users, who reported that they could quickly and easily navigate through the features and find the information they needed.

However, other applications were more complex and difficult to use. They had cluttered interfaces, with too many options and buttons, making it confusing for the users to find the relevant features. In some cases, the maps used in the applications were not properly calibrated, leading to inaccurate visual representations of the luggage’s location. These usability issues frustrated the test users and reduced the overall user – friendliness of the GPS tracking feature.

3.3.2 Real – time Tracking Experience

The real – time tracking experience provided by the GPS tracking systems in smart trolley cases was also evaluated. Test users were asked to comment on how well the systems updated the location of the luggage in real – time.

Most users reported that the real – time tracking experience was satisfactory in open outdoor areas, where the GPS signals were strong. The location of the luggage would update relatively quickly on the mobile application, allowing the users to track its movement with a high degree of accuracy. However, in areas with poor signal strength, such as urban canyons or indoor locations, the real – time tracking experience was much less satisfactory. The location updates were often delayed, and in some cases, the application would show the luggage as being in a different location than it actually was.

Some users also reported that the battery – saving modes implemented in the smart trolley cases to conserve power affected the real – time tracking experience. When the battery – saving mode was activated, the GPS tracking system would update the location less frequently, leading to a less responsive real – time tracking experience.

4. Performance Evaluation of Biometric Locks

4.1 Recognition Speed and Accuracy

4.1.1 Testing Methodology

To evaluate the recognition speed and accuracy of biometric locks in smart trolley cases, a comprehensive testing methodology was employed. Multiple smart trolley cases equipped with different types of biometric locks (fingerprint, facial recognition, and iris recognition) were used in the testing.

For fingerprint locks, a group of test users was asked to enroll their fingerprints in the lock systems. Then, they were asked to attempt to unlock the locks multiple times. The time taken for the fingerprint sensor to capture the fingerprint, process it, and determine whether it matched the pre – stored template was recorded. The accuracy of the fingerprint locks was evaluated by counting the number of successful unlocks and the number of false positives (unauthorized access attempts that were incorrectly recognized as valid) and false negatives (authorized access attempts that were incorrectly rejected).

For facial recognition locks, a similar process was followed. Test users were asked to enroll their facial features in the lock systems. Then, they were asked to stand in front of the facial recognition cameras to attempt to unlock the locks. The time taken for the camera to capture the facial image, analyze the features, and perform the authentication was recorded. The accuracy of the facial recognition locks was determined by calculating the rate of successful unlocks, false positives, and false negatives.

Iris recognition locks were tested in a similar manner, with test users enrolling their iris patterns and attempting to unlock the locks. The recognition speed and accuracy metrics were calculated based on the time taken for authentication and the number of successful and failed attempts.