An IoT-Based School Bus and Vehicle Tracking System Using RFID Technology and Mobile Data Networks

Raad, Muhammad Wasim; Deriche, Mohamed; Sheltami, Tarek R.

doi:10.1007/s13369-020-05111-3

Cited by 26 publications

(4 citation statements)

References 27 publications

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“…Radio Frequency Identification (RFID) technology has been proven cost-effective and successful in a variety of domains. Its applications extend from supply chain [2], traffic signage inventory management [3], in health care [4], [5], wildlife research [6], smart agriculture [7], smart homes [8], vehicle localization and tracking [9], [10], to its evolution in logistics field and other industry 4.0 applications [11], [12]. RFID technology can be an effective and adequate solution for smart parking systems since it has been employed in various disciplines to identify and detect objects.…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Vacant Parking Slot Detection System Using Inkjet-Printed RFID Tags

Lubna

Zahid

Mufti³

et al. 2023

IEEE Sensors J.

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Section: Introductionmentioning

confidence: 99%

IoT-Enabled Vacant Parking Slot Detection System Using Inkjet-Printed RFID Tags

Lubna

Zahid

Mufti³

et al. 2023

IEEE Sensors J.

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“…IoT technology has the potential for usage in transport systems shortly. By the year 2025, it is estimated that a mark of 75 billion device connections to the internet will be achieved [9]. The components which are responsible for data-collection help to gather all useful information about the transportation system, e.g.…”

Section: Introductionmentioning

confidence: 99%

Internet of things time saving and management system for bus passengers

Alawad,

Chughtai,

Alanzi

2023

IJAAS

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<span>This article presents a novel type of mobile phone application approach using the wireless fidelity (WiFi) system. Which is capable of facilitating the public transport user and its drivers efficiently. The application support passengers when there are scenarios where commuters face problems while using public transport. If users get bus information beforehand, such as bus location and arrival time, the number of passengers in the public transportation system. This application has been developed for the Android operating system. The application includes bus details like real-time bus location, bus arrival time, bus routes, bus stops, bus numbers, and so on. After the trip is booked, users' devices allow real-time geolocation to be shared in the application, helping the bus driver decide to stop at one stop or move to another when there are no users.</span>

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“…Target tracking is another critical area where the sensors can be utilized to collect the target real-time position and report it to a server with its relevant information. The practice of tracking one or multiple targets has vast applications in different research areas, such as object tracking (e.g., player, vehicle) [ 5 , 6 , 7 ], border monitoring to prevent illegal crossing, or battlefield surveillance [ 8 ], infrared target recognition [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

Sultan

Waleed

Pyun

et al. 2021

Sensors

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The Internet of Things (IoT)-based target tracking system is required for applications such as smart farm, smart factory, and smart city where many sensor devices are jointly connected to collect the moving target positions. Each sensor device continuously runs on battery-operated power, consuming energy while perceiving target information in a particular environment. To reduce sensor device energy consumption in real-time IoT tracking applications, many traditional methods such as clustering, information-driven, and other approaches have previously been utilized to select the best sensor. However, applying machine learning methods, particularly deep reinforcement learning (Deep RL), to address the problem of sensor selection in tracking applications is quite demanding because of the limited sensor node battery lifetime. In this study, we proposed a long short-term memory deep Q-network (DQN)-based Deep RL target tracking model to overcome the problem of energy consumption in IoT target applications. The proposed method is utilized to select the energy-efficient best sensor while tracking the target. The best sensor is defined by the minimum distance function (i.e., derived as the state), which leads to lower energy consumption. The simulation results show favorable features in terms of the best sensor selection and energy consumption.

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An IoT-Based School Bus and Vehicle Tracking System Using RFID Technology and Mobile Data Networks

Cited by 26 publications

References 27 publications

IoT-Enabled Vacant Parking Slot Detection System Using Inkjet-Printed RFID Tags

IoT-Enabled Vacant Parking Slot Detection System Using Inkjet-Printed RFID Tags

Internet of things time saving and management system for bus passengers

Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning

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