Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

Hybrid Approach for Improving the Performance of Data Reliability in Cloud Storage Management.

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research.

An Innovative Method for Preserving Privacy in Internet of Things.

Preservation of privacy of users' personal data has always been a critical issue to deal with. This issue in the Internet of Things (IoT), which facilitates millions of applications, has become even more challenging. Currently, several approaches and methods are available to safeguard privacy but each of them suffers from one or more anomalies. In particular, Trusted Third-Party approach relies on the trust of a third-party server, Cooperation needs the trust of other peers, Obfuscation is known to return inaccurate results, and Dummy generates too much overhead. Moreover, these and most of the other well-known approaches deal only with specific types of applications linked to the location-based services. In this paper, we present two new methods, namely: Blind Third Party (BTP) and Blind Peers ( B L P ), and combine them to form a new one to be known as the Blind Approach ( B L A ). With the help of simulation results we shall demonstrate the effectiveness and superiority of B L A over the other available methods. The simulation results also exhibit that B L A is free from all the existing problems of the other approaches. However, B L A causes a slight increase in the average (response) time, which we consider to be a minor issue. We shall also discuss the capability and superiority of the Blind Approach in the cases of E-health, Smart Transportation, and Smart Home systems.