In recent years, recommendation systems have gained popularity in the financial services industry. These systems offer personalized recommendations to consumers based on their distinct preferences, behaviors, and historical data. Centralized data storage and processing used in traditional recommendation systems can raise privacy and security concerns. In light of these challenges, in this paper, a federated learning-empowered recommendation model (FLRM) is proposed that utilizes federated learning and blockchain technology. In our proposed model, the central server coordinates model aggregation and communicates with the blockchain network. In FLRM, financial institutions hold their data in private blockchains while participating in the federated learning process. Federated learning provides a solution to these challenges by enabling privacy-preserving collaborative model training across multiple distributed data sources. Blockchain technology enhances the security and transparency of the federated learning model by providing a decentralized and tamper-proof mechanism for data storage and management. By decentralizing the recommendation system, FLRM enhances user privacy, reduces data transfer overhead, and builds trust through transparency. The integration of smart contracts in this proposed model facilitates secure and automated transactions. The proposed approach represents a significant step forward in creating a more secure, privacy-preserving, and effective recommendation system model.

Blockchain technology has the potential to revolutionize the way Intelligent Transportation Systems (ITS) operate in smart cities. By providing a secure and decentralized platform for data exchange and storage, blockchain can enhance the security, privacy, and interoperability of ITS systems. Blockchain technology can be used for various applications in ITS, including secure data exchange between vehicles, infrastructure, and service providers, smart contracts for autonomous vehicles, and decentralized marketplaces for transportation services. However, implementing blockchain in ITS comes with its own set of challenges, including scalability and high computational power requirements. Despite the challenges, blockchain technology offers significant opportunities for ITS in smart cities, enabling new business models and promoting innovation in transportation services. In this paper, we study existing challenges, applications, and future requirements for ITS. We discuss the challenges of the ITS and their impact on smart cities. Blockchain-enabled applications are provided with performance analysis based on the critical parameters of ITS. We also derive the security requirements for future ITS. Finally, we provide some opportunities and possible research areas within the ITS to develop smart cities.

In recent years, the corporate and industrial sectors have been experiencing significant transformations in vehicle-to-vehicle (V2V) communication. It can improve vehicle safety by giving signals to other vehicles wirelessly. The latest software, hardware, and technologies are applied to develop trusted applications that make V2V communication more believable. Today, various technologies are incorporated into vehicles to remove the barrier to existing challenges. The connected vehicles in V2V communication use sensors, data storage, and communication devices. Vehicles can communicate using the latest secure and trusted Cellular Vehicle-to-Everything (C-V2X) technology using direct and network communication modes. Even connected vehicles suffer from data security, user privacy, reliable environment, and vehicle security. Blockchain can help to eliminate those issues in V2V communication systems. Herein, a secure blockchain-enabled V2V communication system (BVCS) is proposed to enhance the security of vehicles and secure data sharing and communication among vehicles. The developed smart contracts in this paper can authenticate users and their vehicles automatically. In this paper, the proposed algorithms can authenticate users, detect unauthorized access, and establish secure communication between vehicles. The proposed system can enhance data security, user privacy, and vehicle security and provide a trusted environment in V2V communication systems.

Fifth-generation (5G) wireless networks are now operational to deploy all over the world. The technology of 5G’s objective is to link heterogeneous machines and devices with significant improvements high quality of service (QoS), internet bandwidth, and improved system throughput to enable several upright applications. Despite all these benefits that 5G will provide, still, significant issues need to be resolved, such as decentralization, transparency, and risks associated with data interoperability, network privacy, and security vulnerabilities. Modern networks link an enormous number of devices to the Internet, and in this complicated situation, the use of BC and SDN has been effectively advocated to assure security, privacy, and secrecy. This study offers a blockchain-enabled SDN framework for securing transactions that makes use of Software Defined Network (SDN) and Network Function Virtualization (NFV) to overcome these issues. The proposed framework can address the man-in-the-middle attack between control and data plane in SDN networks. A controller authentication scheme is provided using smart contracts. Smart contracts automatically authenticate the SDN controller to increase the efficiency of controller verification. The communicated data can also be authenticated using smart contracts. The proposed framework can enhance network transparency, data security, and user privacy. Each SDN controller can access verifiable data using the proposed framework.

In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption.