A Novel Study on IoT and Machine Learning-Based Transportation

Introduction

There has been a steady increase in the intelligence, connectivity, and versatility of mobile devices throughout the past decade. The number of internet-enabled devices has increased dramatically since 2008 (Swan, 2012), when it first topped the number of people on Earth]. Smartphones, embedded systems, wireless sensors, and other gadgets are just few examples of how pervasive network connectivity has become in the Internet of Things era. The more gadgets there are, the more information they will gather. In order to further AI, researchers are turning to machine learning (ML) algorithms to create new programmes that analyses data in order to draw inferences and draw conclusions.

Everything that can be connected to the internet is called a “thing” in the IoT. Embedded systems with a central processing unit are commonplace in things, together with physical sensors and actuators. There needs to be communication between machines since physical objects need to collaborate. Short-range wireless technologies include Wi-Fi and Bluetooth whereas long-range wireless technologies (Vangelista et al., 2015). Since there are so many uses for Internet of Things devices, it's crucial that they don't cost too much. Data collection, M2M connectivity, and even some data pre-processing may also be required of IoT devices, depending on the use case. When designing or purchasing an IoT device, it is essential to strike a balance between price, processing capability, and energy usage. There is no separating the Internet of Things (IoT) from “big data,” as IoT gadgets are always collecting and exchanging vast volumes of information. Therefore, methods of processing, storing, and analyzing massive volumes of data (Ibrahim et al., 2016; Naik, 2017) are frequently included into an IoT infrastructure. The use of an IoT platform like Kaa, Thingsboard, DeviceHive, Thingspeak, or Mainflux to facilitate M2M communication using protocols including MQTT, AMQP, STOMP, CoAP, XMPP, and HTTP has become widespread in IoT systems (Naik, 2017). Monitoring, node management, data analysis, storage, data-driven programmable rules, and more are all part of IoT systems. In some use cases, data processing must occur locally on IoT devices rather than at a centralized node, as in the “cloud computing” architecture. So, a new computer paradigm called “edge computing” is developed (Satyanarayanan, 2017) as computation moves to the terminal nodes of networks. Although convenient, low-end devices may not be up to the task of doing complex computations. “Fog nodes” (Tarek, 2018) provided the solution. Fog nodes enable IoT devices to manage massive volumes of data by offering networking, processing, and storage capabilities. The data is then stored in the cloud, where it may be enhanced, evaluated using various machine learning techniques, and shared with other devices; this process ultimately results in the development of new smart apps. In the so-called “smart city,” various Internet of Things applications have already surfaced. Smart healthcare, smart transportation, smart monitoring of environmental condition, smart supply chain management, and a smart surveillance system are among the most essential uses (Talari et al., 2017). Figure 1 properly depicts the core components of the IoT infrastructure as they have been utilized in the great majority of applications, despite the fact that much work remains in the area of standardization of IoT design and technology.

Figure 1.

Key elements of machine learning-based IoT infrastructure