Biblio

The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.

Blockchain is developing rapidly in various domains for its security. Nowadays, one of the most crucial fundamental concerns is internet security. Blockchain is a novel solution to enhance the security of network applications. However, there are no precise frameworks to secure the Internet of Vehicle (IoV) using Blockchain technology. In this paper, a blockchain-based smart internet of vehicle (BSIoV) framework has been proposed due to the cooperative, collaborative, transparent, and secure characteristics of Blockchain. The main contribution of the proposed work is to connect vehicle-related authorities together to fix a secure and transparent vehicle-to-everything (V2X) communication through the peer-to-peer network connection and provide secure services to the intelligent transport systems. A key management strategy has been included to identify a vehicle in this proposed system. The proposed framework can also provide a significant solution for the data security and safety of the connected vehicles in blockchain network.

Nowadays, developing an intelligent system for segmenting the moving object from the background is essential task for video surveillance applications. Recently, a deep learning segmentation algorithm composed of encoder CNN, a Feature Pooling Module and a decoder CNN called FgSegNET\_S has been proposed. It is capable to train the model using few training examples. FgSegNET\_S is relying only on the spatial information while it is fundamental to include temporal information to distinguish if an object is moving or not. In this paper, an improved version known as (T\_FgSegNET\_S) is proposed by using the subtracted images from the initial background as input. The proposed approach is trained and evaluated using two publicly available infrared datasets: remote scene infrared videos captured by medium-wave infrared (MWIR) sensors and the Grayscale Thermal Foreground Detection (GTFD) dataset. The performance of network is evaluated using precision, recall, and F-measure metrics. The experiments show improved results, especially when compared to other state-of-the-art methods.