Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 2) E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Serie: Federated learning for Internet of Vehicles: IoV Image Processing, Vision and Intelligent Systems
- Sprache: Englisch
Federated Learning for Internet of Vehicles: IoV Image Processing, Vision, and Intelligent Systems (Volume 3) explores how federated learning is revolutionizing the Internet of Vehicles (IoV) by enabling secure, decentralized, and scalable solutions. Combining theoretical insights with practical applications, this book addresses key challenges such as data privacy, heterogeneous information, and network latency in IoV systems.
This volume offers cutting-edge strategies to build intelligent, resilient vehicular systems, from privacy-enhanced data collection to blockchain-based payments, smart transportation systems, and vehicle number plate recognition. It highlights how federated learning drives advancements in secure data sharing, identity-based authentication, and real-time road safety improvements.
Key Features:
- In-depth exploration of federated learning applications in IoV.
- Solutions for privacy, security, and scalability challenges.
- Practical examples of blockchain integration and smart systems.
- Insights into future research directions for IoV.
Readership:
Ideal for researchers, graduate students, and practitioners in intelligent transportation, IoT, AI, and blockchain technologies.
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Seitenzahl: 378
Veröffentlichungsjahr: 2025
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Preface
In an era where the Internet of Vehicles (IoVs) is altering our transportation environment, the demand for intelligent systems capable of effectively processing and analyzing massive volumes of data has never been more. The convergence of IoVs with powerful machine learning algorithms has opened up new opportunities to improve road safety, efficiency, and user experience. However, this rapid evolution presents its own set of obstacles, ranging from data privacy concerns to the intricacies of real-time decision-making.
By examining the cutting-edge federated learning paradigm, this book, Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs, aims to answer these urgent problems. Federated learning, in contrast to conventional centralized methods, permits decentralized data processing, allowing cars to jointly learn from local data while maintaining privacy. This approach not only reduces the hazards connected with data exchange, but also improves the adaptability of intelligent systems under a variety of driving situations.
We explore the major issues that IoVs are now confronting throughout this work, such as data heterogeneity, network latency, and the requirement for strong security measures. Each chapter mixes theoretical ideas with practical examples, showing how federated learning can be used to develop resilient, intelligent systems that can thrive in the dynamic environment of connected automobiles.
We encourage you to consider the revolutionary possibilities of these technologies as you set out on this journey through the nexus of federated learning and IoVs. Our hope is that this book will not only be a valuable resource for researchers and practitioners but will also stimulate more innovation in the sector, paving the way for smarter, safer transportation systems.
We are grateful to the authors, scholars, and practitioners who have contributed their skills to this work. We are building the foundation for a time when intelligent technologies prioritize privacy and safety over transportation.
List of Contributors
Federated Learning on Wheels: A Decentralized Approach to Privacy-Enhanced Data Collection in Internet of Vehicles
Neha Sharma1,*,Urvashi Sugandh2,Jyoti Agarwal3,Arvind Panwar2,Priyanka Gaba4Abstract
Due to privacy issues and the scattered nature of data produced by vehicles, the Internet of Vehicles (IOV) poses considerable hurdles for data collecting. In this chapter, we examine the idea of “Federated Learning on Wheels” (FLoW), which provides a decentralised method for IOV data collection with a focus on privacy. FLOW makes use of the onboard computer resources of cars to carry out model training locally, making sure that private information stays on the cars and is not shared with a centralised server. This strategy overcomes the shortcomings of conventional centralised data collecting approaches while simultaneously protecting user privacy. We examine the fundamentals of federated learning and how they relate to IOV, highlighting the advantages of maintaining privacy. We also look at secure aggregation procedures and confidentiality safeguards as additional methods for privacy-enhanced data acquisition in FLOW. Additionally, we emphasise the significance of accuracy and performance issues in decentralised contexts and use examples that illustrate FLOW's usefulness. We also explore security and trust issues, talking about possible weaknesses and methods to secure the reliability of participants and model updates. We also consider how blockchain technology may be incorporated for improved security and openness. We conclude by discussing FLOW future directions, difficulties, and ethical issues in order to shed light on its possible significance and legal ramifications. Overall, this chapter clarifies the relevance of Federated Learning to Wheels as a ground-breaking approach to data collecting with increased privacy in the Internet of Vehicles.
INTRODUCTION
The IOV, a paradigm that facilitates seamless connection and communication among vehicles, infrastructure, and other entities in the transportation ecosystem, has emerged as a result of the fast growth of technology. Vehicles produce enormous volumes of data in the IOV on their location, speed, driving style, and sensor readings. The potential to increase traffic control, road safety, and transit effectiveness is enormous [1, 2]. However, there are several obstacles to the acquisition and use of this data, especially when it comes to privacy issues and the need for efficient data-collecting techniques.
An Overview of the Internet of Vehicles (IOV) and the Difficulties it Faces in Collecting Data
The IOV is made up of a complicated web of linked motor vehicles, roadside equipment, and infrastructure for transit. This network creates a wide variety of data, including sensor data, position data, and vehicle telemetry. However, there are several difficulties in gathering this data for analysis and decision-making. Traditional methods often depend on data gathering that is centralised, meaning that information is acquired and kept on a single server. This centralised strategy, meanwhile, raises questions about data security, privacy, and scalability. Additionally, there is a risk of data breaches when large volumes of sensitive data are sent from cars to a central server due to high connection costs.
Privacy is one of the main problems with collecting IOV data. Sensitive information including location histories, driving habits, and personal identifiers are often found in vehicle-generated data. Maintaining individual privacy is essential to earning the public's confidence and abiding by data protection laws. Concerns regarding unauthorised access, data breaches, and the possibility for abuse of personal information are brought up by the centralization of data collecting. Additionally, the transmission of massive amounts of data from automobiles to a central computer may result in security flaws and privacy issues [3-5].
The efficiency and scalability of centralised data collection constitute another difficulty. Massive volumes of data are produced by the IOV, and when they are sent to a central server, they might result in high communication costs and bandwidth use. Data transmission may experience delays, increased latency, and congestion as a consequence. Furthermore, managing the amount, diversity, and velocity of IOV data may provide difficulties for centralised infrastructures [6, 7].
Overview of Federated Learning as a Strategy for Improving Privacy
Federated learning has become a viable privacy-enhancing strategy to overcome the privacy issues connected to centralised data collecting. Federated learning eliminates the need to send raw data to a centralised server and enables model training on local devices, such as vehicles. Models are instead sent to the automobiles, who train themselves using their data. A global model is then created by averaging model updates. Federated learning maintains the privacy and security of the data by keeping it on the devices and only exchanging model updates. This decentralised method of machine learning encourages cooperation while reducing privacy concerns [8, 9]. Fig. (1) shows the concept of federated learning vs centralized learning.
Fig. (1)) Federated Learning vs centralized learning.The Significance of Decentralised Data Collection in IOV
Given the sensitivity of vehicle-generated data, decentralised data gathering in IOV is of utmost importance. Vehicles gather information that may contain personally identifying data, driving habits, and past locations. It is essential to secure this sensitive data in order to uphold data protection laws and safeguard individual privacy [10, 11]. Federated learning makes it possible to gather data decentralised, protecting user privacy while keeping control over private data with the vehicle owners. With less dependence on centralised infrastructure and communication capacity, this strategy improves privacy while also enabling IOV to scale more easily and do real-time data analysis.
In the framework of the IOV, this book chapter offers a thorough examination of FLOW as a decentralised method for a more secure collection of data. We go through the fundamental ideas behind federated learning, consider how they apply to IOV situations, and emphasise the value of decentralised data collection in the IOV ecosystem. We provide the groundwork for comprehending FLOW's function as a privacy-enhancing strategy in the Internet of Vehicles by looking at the value of privacy, difficulties in data collecting, and the introduction of federated learning.
Here is how this book chapter is structured: The chapter starts by addressing the privacy issues related to the gathering of IOV data, going through how sensitive IOV data is, and the difficulties with using standard centralised systems. It proposes technologies that protect privacy as viable remedies. The chapter then explores the ideas and tenets of federated learning, emphasising the method's decentralised character and essential elements. We emphasise the advantages of federated learning for protecting privacy in the IOV ecosystem. The next section of the chapter discusses FLOW, a cutting-edge idea that makes use of onboard computing resources for decentralised model training in IOV. We talk about the difficulties while adopting FLOW. In FLOW, methods for ensuring data privacy and confidentiality are investigated along with safe aggregation processes and data collection systems. In a decentralised scenario, performance and accuracy issues are discussed, along with methods for improving model correctness. Examples of FLOW implementation for privacy-enhanced data collecting are given, together with real-world use scenarios. An outline of future prospects and difficulties, such as scalability and computing limitations, as well as ethical questions and legal ramifications of FLOW in the IOV ecosystem, is provided in the chapter's conclusion. The overall examination of FLOW and its prospective effects on privacy-enhanced data collection in the IOV sector is presented in this book chapter.
Privacy Concerns in IOV Data Collection
The sensitive nature of the data created and gathered in the IOV raises privacy issues. Personal information leaks, surveillance threats, and possible damage to people may result from the abuse or exposure of details like driving habits, location records, and personal identifiers. Data privacy is a concern for traditional centralised data-gathering methods because of data security threats, possible monitoring, and loss of data management. Data anonymization, encryption, pseudonymization, and access control are privacy-enhancing technologies that provide ways to safeguard individual privacy, reduce risks, and promote ethical and privacy-preserving IOV data-gathering practices [12].
Discussion of the Privacy Concerns and Sensitivity of IOV Data
The Internet of Vehicles (IOV) creates a large amount of data, including several kinds of sensitive data. In addition to past locations and driving habits, this information also contains personal identifiers and maybe even health-related information gathered through in-vehicle sensors. The sensitive nature of IOV data creates serious privacy issues since it may provide in-depth insights into people's lives, routines, and preferences [13, 14].
The possibility of unauthorised access and improper use of personal information is one of the main privacy issues of IOV data gathering. Malicious actors might possibly follow people's movements, observe their driving habits, or even identify particular people based on distinctive patterns in the data if they had access to IOV data. Such privacy violations pose substantial risks to people's safety and well-being since they may result in stalking, harassment, or identity theft [6, 44].
Additionally, dangers associated with profiling and monitoring might arise from the collection and processing of IOV data. It is possible to deduce human traits, habits, or preferences by connecting various data points from several motor vehicles. For instance, merging location data with timestamps might reveal people's commute habits or regularly frequented sites, invading their privacy and perhaps allowing for discriminating or targeted advertising [15].
Problems with Conventional Centralised Data-collecting Methods
In the context of IOV, traditional centralised data gathering methods include combining and storing data from several vehicles on a centralised server or cloud architecture. However, there are a number of privacy issues with this centralised architecture.
First, the transmission and storage of vast amounts of sensitive data from automobiles to a central server raise questions regarding data security. It is possible to use encryption techniques to safeguard data secrecy while it is in transit. The danger of unauthorised access or data breaches must also be decreased by using secure storage procedures and access restrictions [16, 17].
Second, centralised data collecting may expose users to dangers from spying and data profiling. Data from several automobiles may be combined and analysed to identify people's driving habits, routines, or particular areas [18]. If utilised improperly, this data may violate their privacy and lead to price discrimination, targeted advertising, or even changes in insurance premiums based on specific driving patterns.
Last but not least, problems with data ownership and control may arise in centralised designs. Individuals in a centralised system no longer have direct access to their data once it has been gathered and kept. Concerns about how data are used, shared, and maybe even monetized without people's awareness or permission are raised by this lack of control.
An Introduction to Technologies that Increase Privacy
The use of privacy-enhancing technology is essential to addressing the privacy issues raised by the acquisition of IOV data. These technologies seek to effectively gather and analyse data while preserving its confidentiality, integrity, and control [19].
You may use data anonymization methods to de-identify personally identifiable information and safeguard people's identity, such as generalisation, suppression, or randomization. In order to generalise, individual identifiers must be eliminated or replaced with larger categories (for example, replacing actual ages with age ranges). Redacting or erasing certain data pieces is the process of suppression. By making it harder to connect data back to specific people, randomization methods, such as adding noise to data or replacing values with random ones, further protect privacy.
Data transmission and storage may be made safe while also being kept out of the hands of unauthorised persons by using encryption methods. The danger of data interception or unauthorised access is reduced by using strong encryption methods and encrypting data while it is in transit [20, 21].
Another privacy-enhancing method is pseudonymization, which includes using pseudonyms in place of personally identifying information. This permits data analysis while protecting people's privacy. Pseudonyms are distinctive identifiers that are not directly associated with human identities, making it difficult to relate data to particular people [22].
Furthermore, by only allowing authorised parties access to data, access control technologies like fine-grained permissions and authentication protocols support data privacy. Only authorised people should have access to and control over IOV data, therefore role-based access control implementation and the use of secure authentication techniques like multi-factor authentication may assist in guaranteeing this.
In order to build trust, safeguard individual privacy rights, and adhere to data protection laws, privacy-enhancing technologies are crucial in IOV data collection. It is conceivable to achieve a balance between data usefulness and privacy protection in developing Internet of Vehicles environment by using these technologies.
The sensitive nature of the data created and the possible privacy ramifications for people give rise to privacy issues in the collecting of IOV data. Data privacy is a concern for traditional centralized data-gathering methods because of data security threats, possible monitoring, and loss of data management. Data anonymization, encryption, pseudonymization, and access control are privacy-enhancing technologies that provide ways to safeguard individual privacy, reduce risks, and promote ethical and privacy-preserving IOV data-gathering practices [23]. It is conceivable to achieve a balance between data usefulness and privacy protection in the developing Internet of Vehicles environment by using these technologies.
Federated Learning: Concepts and Principles
A decentralised machine learning method called federated learning allows cooperative model training across several devices or entities, such as the automobiles involved in the IOV. Federated learning enables model training to take place locally on individual devices as opposed to sending raw data to a central server. Clients (devices), a central server, and model updates are the essential elements. Clients use their own local data to train the model, then submit encrypted changes to the central server for aggregate. By storing data on individual devices, limiting data exposure, and enabling collaborative model training without disclosing raw data, federated learning protects privacy [24, 25]. Fig. (2) shows the Federated Learning architecture.
Overview of Federated Learning and its Decentralized Nature
By allowing decentralised model training, the federated learning paradigm for machine learning tackles the problems of privacy and data centralization. Federated learning spreads the model training process over numerous devices or entities, such as automobiles in the IOV setting, in contrast to conventional centralised systems, where data is gathered and stored in a single server.
Without the requirement to send the raw data to a central server, model training may be done locally in federated learning since the data stays on the devices. Federated learning's decentralised structure has various benefits, especially in terms of maintaining privacy. Federated learning minimises the danger of disclosing confidential data to centralised systems, lowering the possibility of data breaches or unauthorised access. Federated learning enables collaborative model training in the setting of IOV, where cars create enormous volumes of data, without sacrificing [26]. Each vehicle trains the model using its local data while functioning as a client in the federated learning process. A central server then aggregates the different models without having direct access to the raw data. Sensitive data is kept on the devices via this aggregation process, and only model changes are communicated.
Fig. (2)) Federated learning architecture.Federated learning's decentralised structure also allows for the effective use of dispersed resources. Federated learning makes use of the computing capacity of the participating devices rather than depending on a central server for processing power. This method facilitates scalability and real-time analysis in the IOV ecosystem by distributing the computing burden and reducing dependency on centralised infrastructure [27]. Federated learning also encourages device cooperation without requiring users to divulge private information. Each device adds its local information to the collective learning process, improving the performance and accuracy of the overall model. Even when the data sources show variances and heterogeneity, this collaborative feature enables the development of reliable and generalizable models. The decentralised nature of federated learning in the IOV environment provides a novel method for data collecting that is improved for privacy. Federated learning provides effective and privacy-preserving model training in the Internet of Vehicles by storing data on the devices, using dispersed resources, and encouraging cooperation.
Key Components of Federated Learning: Clients, Server, and Model Updates
Three essential elements make up federated learning: clients, a central server, and model updates. To fully comprehend federated learning in the context of privacy-enhanced data gathering in the IOV, it is crucial to comprehend these elements. Fig. (3) shows the proposed architecture.
Fig. (3)) Proposed architecture. Clients: Individual devices or other entities that take part in federated learning are referred to as clients. Vehicles having onboard computer capability serve as clients in the context of IOV. Data that is kept locally by each client and is not shared with the central server or other clients. This information might include telemetry from the vehicle, sensor readings, data on driving style, and other pertinent IOV information. Customers participate actively in the federated learning process through local model training with their own data [15].Central Server: In the federated learning process, the central server serves as the orchestrator. It controls client interaction, coordination, and communication. Distributing the initial model to clients, gathering model updates from clients, and combining the changes to produce a global model are all responsibilities of the central server. The privacy and security of each client's data are protected by the fact that the central server does not have direct access to the raw data of the clients. Instead, it gets anonymized or becomes encrypted model updates, enabling cooperation while protecting privacy [28].Model Updates: In federated learning, model updates serve as the foundation for communication between clients and the main server. Each client produces model updates after local model training that reflect the changes made to the model based on its local data. These updates provide details on any adjustments made to the model's weights or parameters. To protect the disclosure of sensitive information, model updates are often anonymized or encrypted. The central server collects the model updates once they are made and uses them to build a global model that represents the knowledge of all involved clients.The fundamental tenet of federated learning is that the model may be improved repeatedly without sharing raw data by exchanging model updates. Through a decentralised system, collaborative model training is made possible while yet protecting client data privacy and security. Federated learning facilitates the development of a strong and accurate global model that captures the collective intelligence of the dispersed devices in the IOV ecosystem by integrating the contributions of many clients via their model updates.
For privacy-enhanced data gathering in the Internet of Vehicles, it is crucial to comprehend the fundamental elements of clients, a central server, and model updates. Federated Learning on wheels offers a decentralised and privacy-preserving solution to model training by efficiently using these components, facilitating better data analysis and decision-making in the IOV domain.
Benefits of Federated Learning in Preserving Privacy in IOV
In terms of protecting privacy in the context of data gathering for the Internet of Vehicles (IOV), federated learning provides a number of noteworthy advantages. Federated learning solves privacy issues raised by conventional centralised data-collecting techniques by using a decentralised approach to model training. Fig. (4) shows the Benefits of federated learning in preserving privacy in IOV. Key advantages of federated learning for protecting privacy in the IOV ecosystem include the following:
Fig. (4)) Benefits of federated learning in preserving privacy in IOV. Data Privacy and Confidentiality: Federated learning reduces the amount of time that raw data from cars must be sent to a centralised server. Instead, model updates are communicated with the central server after being encrypted or made anonymous. This method guarantees that private information, including driving habits, location records, and personal identifiers, stays on the cars and is not disclosed to the central server or other customers [29]. Federated learning maintains data privacy and confidentiality by retaining the data locally and minimising its transfer.Reducing Data Security Risks: Because all data is processed and stored on a single server, centralised data gathering systems run the risk of data breaches or unauthorized access. By dispersing the data over several platforms and doing away with the necessity for a single store of raw data, federated learning reduces these concerns. The decentralised structure of federated learning safeguards the privacy of other cars' data even if the data of one vehicle is hacked [30].Minimization of Communication Overhead and Bandwidth Requirements: With federated learning, the necessity for bulk data transfers is replaced by the transmission of model changes. This greatly lowers communication overhead and bandwidth needs compared to conventional centralised methods. Federated learning reduces the amount of data exchanged by just sending the model changes, which improves the IOV ecosystem's performance. This decrease in data transmission results in less network connection dependence and reduced latency.Retaining Ownership and Control of Local Data: Federated learning makes sure that each vehicle maintains ownership and control over its locally stored data. Federated learning protects people's right to privacy by retaining the data on the cars and gives them the choice to keep control of their data. This feature promotes confidence between vehicle owners and the IOV ecosystem by increasing openness [31].Collaborative Learning without Raw Data Sharing: Collaborative model training without the requirement to exchange raw data among cars or with a central server is possible thanks to federated learning. Only model updates, which include the model's improvements, are shared, and each vehicle trains its own model based on its own data. By working together, the cars' aggregate intelligence may contribute to the global model's development while maintaining the confidentiality of each vehicle's data [32].Differential Privacy and Secure Aggregation: Differential privacy and safe aggregation are two privacy-preserving methods that may be included in federated learning. Differential privacy ensures that individual data points cannot be reconstructed or traced back to particular cars by adding controlled noise to the model updates. The central server may aggregate the model updates using secure aggregation methods without having access to the raw data, thereby strengthening privacy and secrecy [33].Federated learning provides a decentralised method for the Internet of Vehicles' increased data collection privacy. Federated learning ensures privacy preservation while enabling collaborative model training and utilising the collective intelligence of the vehicles in the IOV ecosystem. This is done by keeping data on the vehicles, minimising data transfer, respecting data ownership and control, and incorporating privacy-preserving techniques.
Federated Learning on Wheels
FLOW is a decentralised method of federated learning that has been specially designed for the Internet of Vehicles. FLOW allows automobiles to actively take part in model training while protecting data privacy by using onboard computer capabilities. With this method, model training may be done in the IOV's dynamic and resource-constrained environment in real-time, at scale, and with privacy enhancements. Fig. (3) shows the proposed architecture.
Introduction to the Concept of FLOW
A new idea called FLOW blends federated learning with the decentralised aspect of the IOV. FLOW enables decentralised model training while protecting privacy in the IOV ecosystem by using the onboard computing capacities of cars. With FLOW, cars may actively contribute to the federated learning process while maintaining their privacy by improving the global model with the help of locally obtained data.
Utilizing On-board Computing Capabilities for Decentralized Model Training
The use of onboard computer resources for decentralised model training is one of the core features of FLOW. Without depending on external infrastructure, vehicles having computing capabilities, such as processors and memory, may do local model training. With the use of this distributed methodology, cars may train models using their own data, capturing the distinctive qualities and insights particular to each vehicle's surroundings and driving habits [34].
FLOW makes it possible for the IOV ecosystem's models to be trained quickly and effectively by using on-board computer resources. Using locally stored data, vehicles may make model updates and improvements without constantly communicating with a central server. Due to the decreased latency and bandwidth needs, FLOW is ideally suited for the IOV's dynamic environment and resource limitations.
Additionally, as the number of participating cars grows, scalability is made possible by FLOW's decentralised model training. To enable parallelized model training across many devices, each vehicle contributes its computing power to the entire training procedure. Because of its scalability, FLOW can manage the expanding amount of data produced by cars in the IOV while still delivering accurate and quick model updates [35].
Moreover, FLOW improves data security and privacy by conducting decentralised model training on-board. The danger of revealing personal information during data transfer or storage in a central server is reduced since the sensitive data stays within the cars. FLOW's decentralised design guarantees that every vehicle maintains ownership over its data, promoting privacy and confidence in the IOV ecosystem.
Challenges and Considerations in Implementing FLOW in IOV
For effective implementation, there are a number of issues and problems that must be taken into account while implementing FLOW in the context of the IOV. These difficulties include technological, operational, and legal issues, all of which are essential to ensuring FLOW's efficacy and privacy protection. Fig. (5) shows the challenges and considerations in implementing FLOW in IOV. The following issues and factors must be taken into account while adopting FLOW in IOV:
Fig. (5)) Challenges and considerations in implementing FLOW in IOV. Heterogeneity of Vehicle Data: The IOV ecosystem includes automobiles from different manufacturers, models, and generations, leading to a substantial amount of variability in the data gathered. The variability makes it difficult to achieve consistency and compatibility while training models in FLOW. To harmonise the many data sources and promote productive cooperation in the federated learning process, standardisation initiatives are required, such as the creation of standard data formats or preprocessing methods [36].Resource Constraints: The processing power, memory, and energy available to vehicles in the IOV are often constrained. These resource limitations may affect FLOW's effectiveness and expandability. To make sure that model training and communication overhead are within the capabilities of the vehicles, these limits must be taken into account throughout the design and optimisation of federated learning algorithms and protocols. These problems may be solved with the use of methods like model compression, resource-aware scheduling, and adaptive learning algorithms (Lim et al., 2020).Connection and Communication: Implementing FLOW poses difficulties due to the dynamic nature of vehicle connection and the variable quality of communication channels. Vehicles may only have spotty or limited network access, which might interfere with the federated learning process' synchronisation and communication. To accommodate the fluctuating connection circumstances of cars and assure robust and effective cooperation in FLOW, strategies including asynchronous communication, dependable communication protocols, and adaptive data synchronisation methods must be used [37].Privacy and Security: Although FLOW seeks to improve IOV data collecting privacy, maintaining data privacy and security is still a top priority. Sensitive data must be adequately protected throughout transmission, storage, and processing. Encryption, differential privacy, and secure aggregation protocols are examples of privacy-enhancing technologies that must be included in FLOW to protect data privacy and reduce the possibility of privacy breaches or unauthorised access.Regulatory Compliance:
