As the importance of vehicle data increases, it has become very important to safely store them. However, because onboard diagnostics scanners generally used to store vehicle data are IoT devices, security and capacity issues exist to store data safely and efficiently. To address this, we propose a system that stores vehicle data safely and efficiently using blockchain and IPFS. Users can access the system through DApp, an Ethereum-distributed application, and manage their vehicle data. Various experiments have been conducted to demonstrate the superior performance of this system, and the experimental results show its advantages in terms of data-processing speed and cost.

As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).

Recently, the machine learning research trend expands to the system performance optimization field, where it has still been proposed by researchers based on their intuitions and heuristics. Compared to conventional major machine learning research areas such as image or speech recognition, machine learning-based system performance optimization fields are at the beginning stage. However, recent papers show that this approach is promising and has significant potential. This paper reviews 11 machine learning-based system performance optimization approaches from nine recent papers based on well-known machine learning models such as perceptron, LSTM, and RNN. This survey provides a detailed design and summarizes model, input, output, and prediction method of each approach. This paper covers various system performance areas from the data structure to essential system components of a computer system such as index structure, branch predictor, sort, and cache management. The result shows that machine learning-based system performance optimization has an important potential for future research. We expect that this paper shows a wide range of applicability of machine learning technology and provides a new perspective for system performance optimization.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

A dashboard camera (Dashcam) is attached to the front or rear of a vehicle to record images and video. As it is effective in crime prevention and accident management and it can also be used as learning data for Traffic Accident Detection technology for Autonomous Vehicles the use of Dashcam is increasing. However, the video data is stored on a memory card or a cloud server, so there is a high possibility of data loss and forgery. Although it is possible to prevent forgery and falsification using distributed storage based on blockchain technology, the authenticity and privacy of the image data stored in the blockchain cannot be guaranteed. In this study, to solve this problem, we propose a multi-signature-based access control method by grouping and storing video data of multiple vehicles based on GPS (Global Positioning System) data. To ensure the privacy of the video data stored in the blockchain, only users uploading video belonging to the relevant GPS can access nearby Dashcam videos. Through these experimental results, it show that Experimental results proposed method can maintain low latency in large-scale request environment to the privacy and reach data management efficiently in a distributed file system. According to our experiments, the dashcam video data distributed storage latency was fast enough, with an average of 25 ms for uploads and less than 15 ms for downloads. The signature generation time is 10ms, and the verification time required for access control is also less than 100 ms, which is the same even if the number of nodes increases, so scalability is not affected. The blockchain transaction processing took about 2 seconds, but it does not affect the V2V network. Also, client registration time does not affect performance.