IoT-Enabled Urban Intelligent Transportation Systems: History
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Contributor:
Sarah Kaleem
,
Adnan Sohail
,
Muhammad Usman Tariq
,
Muhammad Asim

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems (ITS), notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data.

  • federated learning
  • internet of things
  • smart transportation
  • intelligent transportation systems

1. Introduction

The Internet of Things has become a cornerstone in the evolution of a digitally connected world, enabling various sectors to collect and analyze data in real time [1]. By embedding sensors and software in physical objects, IoT technologies allow for unprecedented levels of monitoring and automation, paving the way for more innovative and efficient systems [2]. One of the most impactful applications of IoT is in the domain of Intelligent Transportation Systems. An IoT-enabled ITS aims to optimize traffic flow, improve road safety, and enhance the overall transportation experience for individuals and logistics providers [3] through interconnected sensors, vehicles, and traffic management tools. These systems are becoming particularly crucial in urban environments, where managing complex, congested networks is a growing challenge [4]. One of the most formidable challenges and opportunities posed by IoT-enabled Intelligent Transportation Systems is generating voluminous and highly complex data, often called Big Data [5]. These systems employ interconnected sensors, vehicles, traffic lights, and other IoT devices that continuously collect and transmit real-time data. The data can range from vehicle speed and location to weather conditions, road quality, and driver behavior [6]. The diversity of data types, including structured, semi-structured, and unstructured data, adds another layer of complexity.
The data is generated at an unprecedented velocity, requiring rapid processing for actionable insights. Given the velocity, volume, and variety, which are the three Vs of Big Data, it becomes evident that traditional data processing systems must be equipped to handle the complexities of data flow and analytics in an IoT-enabled ITS [7]. This enormous scale and complexity of data not only necessitate more advanced Big Data analytics but also makes it imperative to address challenges related to data storage, privacy, integration, and real-time processing [8]. IoT-enabled ITSs inherently generate colossal amounts of data due to the continuous real-time collection and transmission of various types of information [9]. This ever-growing mountain of data falls under the category of Big Data, characterized by its high velocity, volume, and variety [10]. While Big Data provides opportunities for deep analytics and insights, it also presents many challenges. Traditional data processing frameworks often need to be revised to handle this data’s sheer scale and complexity, which requires real-time analysis for actionable insights [11]. Furthermore, integrating disparate data types and sources adds a layer of complication to the analytical processes. One of the most pressing challenges in dealing with Big Data from IoT-enabled ITSs is the issue of data privacy.
Since individual vehicles and devices contribute sensitive information, these data’s centralized collection and processing raise significant privacy and security concerns [12]. Federated Learning (FL) offers a novel approach to tackling data privacy challenges. In a federated model, machine learning algorithms are trained across multiple decentralized devices or servers holding local data samples without exchanging them [13]. This allows for practical model training and ensures that the data remain on the local device, thereby maintaining individual privacy. FL is a machine learning paradigm where multiple decentralized devices or servers collaboratively train a shared model while keeping their data locally stored. Unlike the traditional centralized machine learning approach, FL ensures data privacy by transmitting only model updates, rather than raw data, between participating entities. This decentralized approach addresses significant privacy and security concerns, especially in domains with sensitive data. Its core benefit lies in enabling machine learning on edge devices, preserving data ownership, and minimizing data transmission overheads.
Big Data analytics using FL offers a transformative approach that addresses critical challenges like data privacy, scalability, and real-time analysis. By allowing machine learning models to be trained across multiple decentralized devices or servers, FL eliminates the need to move data to a central location. This ensures privacy compliance, optimizes resource usage, and enhances model robustness. Moreover, FL can handle real-time data analytics, non-IID data distributions, and data imbalance and heterogeneity, making it a promising solution for future Big Data analytics [14]. FL emerges as a potent solution for handling Big Data analytics in the context of Big Data produced in the IoT-enabled ITS environment. ITS produces a wealth of data from various IoT sensors embedded in the transportation infrastructure and vehicles. FL offers a decentralized approach to model training, allowing these devices to perform localized analytics without sending sensitive or voluminous data to a central server [15].

2. IoT-Enabled Urban Intelligent Transportation Systems

Big Data analytics involves examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision making. With the increasing importance of data privacy and distributed data sources, FL is emerging as a powerful tool that complements traditional Big Data analytics. Utilizing FL techniques in Big Data analytics allows for decentralized model training across a myriad of data sources without the need for central data aggregation. This provides an efficient and privacy-preserving mechanism for harnessing insights from vast amounts of data scattered across multiple locations or organizations. Unlike traditional machine learning, FL enables model training across multiple decentralized nodes without requiring raw data to be shared centrally, thus ensuring data privacy and reducing data movement [16]. One of the main challenges in Big Data analytics is data privacy. FL stands out as a privacy-preserving method since it enables model training without requiring raw data to be transferred to a central server, aligning with privacy regulations like GDPR and HIPAA [17]. Big Data is often characterized by its enormous volume and the speed at which it is generated. The scalability of FL allows it to handle the challenges of Big Data efficiently by facilitating decentralized training across multiple nodes.
The architecture of FL enables real-time data analytics as data is analyzed at the source, and no latency is involved in sending the data to a centralized location for processing. This feature is crucial for applications requiring immediate insights [18]. Traditional Big Data analytics often requires the assumption that data are independently and identically distributed (IID). FL can handle non-IID data distributions, enabling more personalized and accurate model training. Data transfer over the network is resource-intensive [19]. FL alleviates this issue by localizing the data and reducing the need to send data over the network. Instead, model updates are the only information exchanged, conserving computational resources [20]. In Big Data analytics, one of the goals is to generalize findings across diverse and complex datasets. FL contributes to model robustness by aggregating learning from diverse data sources. The issue of imbalanced and heterogeneous data is also present in Big Data analytics. FL can adapt to these challenges due to its flexible and distributed architecture. Federated Learning presents a promising avenue for tackling the challenges of Big Data analytics, offering solutions for data privacy, scalability, real-time analysis, and more [21]. Its features complement the goals of Big Data analytics, paving the way for more secure and efficient data analysis techniques.
The exponential growth of the IoT has precipitated a revolution in ITS, notably in urban environments. IoT has been a driving force behind significant advancements in ITS, especially within urban settings. ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. This fusion aims to elevate the intelligence and efficiency of transportation networks, making them more responsive to the needs of modern urban environments. One of the groundbreaking integrations in ITS is the incorporation of FL. By leveraging FL, ITS can enable vehicles and transportation infrastructure to engage in collaborative learning. This collaboration is pivotal in optimizing traffic flow, enhancing safety protocols, and improving the overall efficiency of travel routes, as cited in [22]. A standout feature of this approach is its emphasis on data privacy. Unlike traditional systems, FL ensures that data generated by individual vehicles or sensors is not required to be sent to a central repository. Instead, learning and model improvements occur at the edge, ensuring data remains decentralized. All this is accomplished while ensuring data privacy, as the data generated by individual vehicles and sensors does not have to be centrally collected to build and improve the predictive models. The ITS model envisages a network of interconnected vehicles that communicate with each other and intelligent infrastructure [23]. The envisioned model for ITS is a highly interconnected network where vehicles are not isolated entities. They are part of a larger ecosystem, communicating continuously with each other and with smart infrastructure components. However, implementing such a vision is not without challenges. These challenges can be broadly categorized into four main areas:
  • System complexity: The intricate nature of ITSs, with multiple components interacting simultaneously, adds layers of complexity to the system.
  • Model performance: The dynamic and ever-changing environment of ITSs presents challenges in maintaining consistent model performance. Models that rely solely on static local intelligence often find adapting to these dynamic changes challenging, resulting in performance degradation [23].
  • Privacy concerns: Ensuring user and data privacy becomes paramount with the increasing interconnectivity and data sharing. The dynamic nature of ITSs further amplifies these concerns. These obstacles are primarily clustered into four critical areas: system complexity, model performance, privacy concerns, and data management. The dynamic nature of ITS environments poses a significant hurdle regarding privacy concerns [24].
  • Data management: As the ITS network expands, so does the number of nodes capable of processing data. This growth necessitates efficient data management strategies, especially given the constraints of roadside units. Traditional machine learning techniques might face difficulties when applied in such scenarios, particularly during the training phase. The limited data storage capacities of roadside units can hamper the effectiveness of these techniques. Models built on static local intelligence need more flexibility to adapt to such changes, leading to a sharp decline in performance. The growing number of network nodes with data processing capabilities makes data management a significant concern [25]. Since roadside units have limited resource availability, special attention must be paid to efficient data storage strategies. Traditional localized ML techniques may be handicapped during the training phase due to the constraints in data storage capacity at the roadside units.
FedGRU, an algorithm that combines Federated Learning with Gated Recurrent Unit (GRU) networks, is proposed for privacy-focused traffic flow prediction [26]. This approach excels in both preserving privacy and prediction accuracy while employing Federated Averaging to reduce communication overhead. In contrast, another study integrates Federated Learning and blockchain technology to maintain data privacy and integrity in Intelligent Transport Systems (ITSs), using a blockchain-based smart contract to securely aggregate threat-detection models trained on individual vehicles securely [27]. However, this approach shows a slight trade-off with a 7.1% decrease in detection accuracy and precision. A survey offers a comprehensive overview of combining blockchain and Federated Learning to address data privacy and security in the Internet of Vehicles (IoVs), identifying key challenges and future research directions [28]. Similarly, a blockchain-based asynchronous Federated Learning scheme called DBAFL is introduced for intelligent public transportation systems [29]. This scheme balances efficiency, reliability, and learning performance using a committee-based consensus algorithm and a dynamic scaling factor.
A thorough review of Federated Learning applications in Connected and Automated Vehicles (CAVs) analyzes data modalities, evaluates various applications, and outlines future research directions [30]. Another study proposes a contextual client selection pipeline for Federated Learning in transportation systems, using Vehicle-to-Everything (V2X) messages to predict latency and select clients accordingly [31]. A Federated Learning framework designed for autonomous controllers in CAVs is introduced, presenting a novel algorithm called Dynamic Federated Proximal (DFP) that outperforms traditional machine learning solutions in various traffic scenarios [32]. Transformation of the Internet of Vehicles into Intelligent Transportation Systems through advancements like 5G networks is discussed, identifying key challenges such as scalability and data privacy while proposing Federated Learning as a solution [33]. A study addresses the non-identical data distribution across clients in Federated Learning systems, introducing a new FedOT scheme based on the Optimal Transport theory [34]. Lastly, communication challenges in Federated Learning within dynamic and dense vehicular networks are addressed, introducing a Communication Framework for Federated Learning (CF4FL) that reduces training convergence time by 39% [35].
Federated Optimal Transport (FedOT) is introduced to address data distribution issues in Federated Learning, validated through numerical tests [36]. Selective Federated Reinforcement Learning (SFRL) aims to improve the efficiency and adaptability of Connected Autonomous Vehicles through a unique selection process, confirmed by extensive simulations [37]. FedSup employs Bayesian Convolutional Neural Networks for fatigue detection in the Internet of Vehicles, showcasing reduced communication costs and improved training [38]. Federated Transfer-Ordered-Personalized Learning (FedTOP) is tailored for driver monitoring, demonstrating improved accuracy, efficiency, and scalability across two real-world datasets [39][40]. A Hybrid Federated and Centralized Learning (HFCL) framework merges the advantages of federated and centralized learning, achieving up to 20% higher accuracy and 50% less communication overhead [41]. Driver Activity Recognition (DAR) is explored through a Federated Learning model, showing competitive performance in centralized and decentralized settings while considering data privacy and computational resources [42].

This entry is adapted from the peer-reviewed paper 10.3390/su152115333

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