Blockchain-based Data Trading in Edge-cloud Computing Environment
Introduction
With the continuous growth in the quantity of data generated in the network, data caching and trading in the edge-cloud environment can greatly reduce the data center's transmission cost and load (Sadiq et al., 2020). However, data in edge data centers are easy to lose and leak, and the confidentiality and integrity of data cannot be guaranteed (Zhao et al., 2020, Roman et al., 2018). Blockchain technology can maintain information transparency in caching and trading processes and build trust between participants through a consensus mechanism (Christidis and Devetsikiotis, 2016, Berdik et al., 2021). Therefore, by using the Blockchain, it is possible to ensure that the data caching and trading platform have the characteristics of confidentiality, integrity, and trusted data access (Crosby et al., 2016, Li et al., 2020).
The research scope of this paper is blockchain-based data caching and trading in an edge-cloud computing environment. With the increasing demand for low-latency services from users, due to the limited cache capacity at the edge and the complexity of security management, how to place the content to guarantee that the required content is closer to the user to ensure high-quality and secure cache services, reduce the cost of transmission consumption and delay during the user's access process, have become urgent problems to be solved (Dai et al., 2020). When trading data, on the one hand, the current data trading market is still in its infancy and lacks appropriate regulations. On the other hand, data agents are selfish and seek to maximize their utility rather than the overall system efficiency. Therefore, protecting private information, ensuring efficiency, and real data trading to achieve the desired economic benefits must be considered (Chuang et al., 2020). In a word, it is of great significance to study caching and trading methods of decentralized data based on Blockchain.
With the rapid development of on-board telematics technology in the realm of smart vehicles, massive data has been generated from vehicles. For instance, a self-driving car can produce 1 GB of data per second from systems such as GPS and cameras. By using the on-board unit and sensor equipment, the vehicle can not only record driving information (location, driving video et al.) regularly but also provide road traffic conditions and weather conditions in real-time of the current area, which is helpful for traffic planning and road system design and traffic signal light control, etc. Therefore, vehicles can cooperate to share and trade data of common interest (Sadiq et al., 2020). However, as the scale of vehicle data surges, network capacity is limited, and data cannot be effectively transmitted to vehicles. Moreover, some sensor data has its spatial range and limited service life, such as current traffic information and traffic congestion information at intersections, which require low waiting time to realize vehicle data trading (Kang et al., 2018). The traditional data transmission through the remote cloud will make the transmission quality difficult to guarantee. The transmission cost is too high, and it will also occupy a lot of bandwidth, causing network congestion. At present, edge computing can greatly reduce data transmission costs and a load of data centers by processing data at the edge close to users instead of uploading it all to the cloud. However, edge computing still faces many risks in terms of Security (Alwarafy et al., 2020). For example, during data trading, by caching some of the data required by users in the edge environment, the cost of data transmission can be reduced, and the transmission delay during user access can be reduced. But data caching at the edge also brings some security issues. Because edge devices are placed at the edge side and are physically closer to the attacker. For instance, in the realm of smart vehicles, the transmitted data generally contains very sensitive information of its creator, so vehicle users may be unwilling to store their content in an untrusted cache storage space. At the same time, the cached content is easy to have tampered with it. The widespread malicious data will do great damage to the cache system. (Oham et al., 2021).
As previously stated, the question of auditing the data security during the data caching process when the edge cache capacity is limited in the decentralized network is needed to be solved. Therefore, a secure cost-aware data caching scheme based on Blockchain is proposed. In this scheme, an objective function is established by considering the transmission cost and content caching gain. The particle position detection and correction algorithm and QPSO algorithm are adopted to optimize the data caching problem. In the traditional data trading market, the selfishness of data agents that only seek to maximize their own utility rather than the overall welfare and the easy disclosure of user privacy hinders the development of data trading. A secure decentralized data trading model based on Blockchain is proposed to solve the above problem and increase incentives for users to trade data. In this model, users' privacy information can be protected during data trading by using blockchain encryption technology and smart contracts. The main contributions are concluded as follows:
- (1)
To minimize the transmission delay and cost of data caching and ensure data security in the data caching process, a secure cost-aware data caching algorithm based on Blockchain is proposed. In this algorithm, considering the transmission cost and the content caching gain, the QPSO mechanism is designed to optimize the data caching placement problem.
- (2)
Because of the distrust of the traditional data trading market, a secure decentralized data trading algorithm based on Blockchain is proposed. In this algorithm, users' private information can be protected, and the Security of data trading can be protected by using smart contract and encryption technology in Blockchain. Moreover, an iterative double auction mechanism is proposed to increase incentives for users to trade data and maximize social welfare.
- (3)
The performance of the algorithm is estimated through a great number of experiments. The experiments reveal that the proposed data caching scheme can reduce data transmission delay and cost in acquiring content cache. The proposed data trading scheme can improve the social welfare of the data trading market.
This paper studies the problem of secure decentralized data caching and trading based on Blockchain. Section 2 presents related work. The model of data caching and trading is shown in Section 3. The pseudo-code of the proposed algorithms is given in Section 4. The experiment settings and results are given in Section 5. The conclusion is discussed in section 6.
Section snippets
Related work
With the development of IoT and edge computing, users and trading data are also increasing. In the data trading process, when the amount of data is large, it will increase the burden of the network, resulting in a larger transmission delay. Therefore, the burden of data traffic can be reduced by caching content.
Methodology
Firstly, in this section, we have shown the whole picture of our method structure in Section 3.1. Secondly, the detailed components (i.e., the edge layer, the blockchain layer, and the edge user layer) of blockchain-based data caching and data trading schemes are elaborated in Figs. 2 and 3. Moreover, a data caching algorithm based on quantum particle swarm optimization and the particle position detection and correction algorithm is proposed in Section 3.2. Finally, a data trading algorithm is
The data caching based on quantum particle swarm optimization
The specific steps of the QPSO algorithm are as below: (1) The number of groups and the dimension of particles are determined. Then the position of the particle swarm is randomly set using 0 or 1 and the number of iterations is set. The individual and group optimum values are then initialized. (2) The particle position detection and correction algorithm are used to detect and correct the particle position. (3) The fitness value of each particle is solved. (4) The individual and group optimal
Experiments
In this section, the experiment environment, test cases, benchmark algorithm and evaluation metrics are introduced. Besides, numerical results are given to evaluate the performance of the proposed algorithms.
Conclusion
This paper is mainly focusing on studying blockchain-based secure data cache placement and data trading in the edge-cloud environment. To solve the problem of data caching, data caching gain and data transmission cost are taken into consideration, and QPSO algorithm is used to obtain the best data cache placement scheme with the largest data caching gain. For the problem of data trading, to guarantee the safety and credibility of both parties, and to maximize the utility of the system while
Acknowledgment
The work was supported by the National Natural Science Foundation of China (NSFC) under grants (No. No.62171330, 61873341), Key Research and Development Plan of Hubei Province, China (No.2020BAB102), China National Light Industry, Beijing Technology and Business University (KLC-2021-YB1). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.
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