Chunlin Li

Bio: Chunlin Li is an academic researcher from Wuhan University of Technology. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 12, co-authored 46 publications receiving 362 citations. Previous affiliations of Chunlin Li include State Ethnic Affairs Commission & University of Architecture, Civil Engineering and Geodesy.

Lightweight blockchain consensus mechanism and storage optimization for resource-constrained IoT devices

Abstract: Blockchain is a distributed digital ledger with features such as tamper-proof and privacy protection, which can provide reliable security solutions for the Internet of Things, smart home, and other scenarios. However, most of the devices in these scenarios only have limited computing, storage, bandwidth, and other resources, which makes it difficult to bear the burden of the blockchain consensus process and subsequent storage of the blockchain ledger. Therefore, a lightweight blockchain needs to be designed to suit for resource-constrained device scenarios. In order to achieve lightweight blockchain, an improved PBFT blockchain consensus mechanism based on reward and punishment strategy is proposed in this paper. Moreover, to reduce the storage overhead on the premise of ensuring the recoverability of blockchain, a blockchain storage optimization scheme based on RS erasure code is proposed in this paper. Experimental results show that the strategies proposed in this paper can reduce the delay of the consensus, the communication resources required by the consensus, and the cost of blockchain storage.

Augmentation Techniques for Mobile Cloud Computing: A Taxonomy, Survey, and Future Directions

Abstract: Despite the rapid growth of hardware capacity and popularity in mobile devices, limited resources in battery and processing capacity still lack the ability to meet increasing mobile users’ demands. Both conventional techniques and emerging approaches are brought together to fill this gap between user demand and mobile devices’ limited capabilities. Recent research has focused on enhancing the performance of mobile devices via augmentation techniques. Augmentation techniques for mobile cloud computing refer to the computing paradigms and solutions to outsource mobile device computation and storage to more powerful computing resources in order to enhance a mobile device’s computing capability and energy efficiency (e.g., code offloading). Adopting augmentation techniques in the heterogeneous and intermittent mobile cloud computing environment creates new challenges for computation management, energy efficiency, and system reliability. In this article, we aim to provide a comprehensive taxonomy and survey of the existing techniques and frameworks for mobile cloud augmentation regarding both computation and storage. Different from the existing taxonomies in this field, we focus on the techniques aspect, following the idea of realizing a complete mobile cloud computing system. The objective of this survey is to provide a guide on what available augmentation techniques can be adopted in mobile cloud computing systems as well as supporting mechanisms such as decision-making and fault tolerance policies for realizing reliable mobile cloud services. We also present a discussion on the open challenges and future research directions in this field.

Resource Scheduling in Edge Computing: A Survey

Abstract: With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.