Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

Abstract

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment.

Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review

“…Integrated BC-FL frameworks present overall formal models, besides the use of others (i.e., Fedtrust [83], AFFIRM [84]), frameworks like Blade-FL and VBFL are underrepresented, suggesting the need for further exploration. Applications span diverse sectors, prominently healthcare (35%), industrial management (30%), smart cities (30%), and others (i.e., smart grid [83], disaster response [86], BC reliability prediction [88], Internet of Battle Things (IoBT) [96], Internet of Drone Things (IoDT) [103], Agriculture [98], MEC [101], Society [105], Gamming [109], Industry 5.0 [114]) (43%) indicating the versatility and potential impact of this integration. Within Cloud Features (EC5), a hybrid cloud deployment model (48%) and container-based cloud server design (43%) stand out, emphasizing flexibility and scalability.…”

“…Journal [108] 2022 P34 [109] 2022 P35 [110] 2023 P36 [111] 2020 P37 [112] 2023 P38 [113] 2022 P39 [114] 2023 P40…”

Federated Learning Incentivize with Privacy-Preserving for IoT in Edge Computing in the Context of B5G

“…Traditional cloud computing is difficult to satisfy the needs of IoT devices due to some critical factors such as bandwidth shortages, high operating costs, a lack of real-time services and responses, and a lack of security and privacy because it serves as a general computing system. Despite the fact that edge computing makes computing more practical for smart city applications and beyond, it also poses significant security vulnerabilities by increasing the real-world attack surface regarding weak computation power, heterogeneity of the operating system and communication protocol of edge devices, making it difficult to provide a protective mechanism for edge computing, and attack unawareness because edge devices don't support a user interface or a system to show whether the device is shutdown, malfunctioned, or compromised [24].…”

Blockchain technology for advanced therapy medicinal products: Applications in tracking, data sharing, and supply chain automation

“…"As this technology continues to evolve, it has the potential to significantly impact how data-driven decisions are made in healthcare and beyond." [18,21].…”

“…Access policies will co-evolve with institutional learning and regulatory guidance. But rationalizing identity on blockchain networks promises to balance transparency with confidentiality imperatives across ATMP development pipelines needing accountability [21].…”