Ziqiong Lin

With the development of intelligent technology, data in practical applications show exponential growth in quantity and scale. Extracting the most distinguished attributes from complex datasets becomes a crucial problem. The existing attribute reduction approaches focus on the correlation between attributes and labels without considering the redundancy. To address the above problem, we propose an ensemble approach based on an incremental information level and improved evidence theory for attribute reduction (IILE). Firstly, the incremental information level reduction measure comprehensively assesses attributes based on reduction capability and redundancy level. Then, an improved evidence theory and approximate reduction methods are employed to fuse multiple reduction results, thereby obtaining an approximately globally optimal and a most representative subset of attributes. Eventually, using different metrics, experimental comparisons are performed on eight datasets to confirm that our proposal achieved better than other methods. The results show that our proposal can obtain more relevant attribute sets by using the incremental information level and improved evidence theory.

The application of blockchain technology on mobile devices requires significant computing resources. However, the limited capacity of mobile devices cannot meet the high computing power requirements of blockchain technology, which limits the development of blockchain technology. Mobile Edge Computing (MEC) is capable of alleviate the limited computing capacity of IoT terminals. This paper introduces an edge computing-enabled blockchain system consisting of an edge computing allocation center (ECAC), multiple edge service providers (ESPs), and multiple miners. To efficiently allocate resources between these miners and ESPs, we propose a hierarchical computation offloading strategy based on contract theory and matching game. During contract design phase, ECAC designs a contribution-reward contract to attract ESPs to join the trading market, and provide services to miners. By analyzing the attributes and conditions of feasible contracts of feasible contract, the optimal contract is devised using Lagrange multiplication. During matching phase, an iterative matching algorithm (IMA) is proposed to achieve the matching between ESPs and miners by constructing preference sets for miners and ESPs. Both the stability and convergence have been proved by theoretical analysis. Finally, we conduct experiments to validate the feasibility and effectiveness of the design contract. Experimental results also demonstrate the stability of IMA matching algorithm.

To address the bottleneck of energy performance, this paper considers an energy harvesting (EH)-enabled Mobile Edge Computing (MEC) system with contract users (CUs) and random users (RUs). The resource demands of CUs are known in advance, while RUs are mobile and randomly arriving. We propose a resource reservation framework based on contract theory with the goal of maximizing the utility of agent node (AN) under information asymmetry and demand uncertainty. First, for symmetric information scenario, the relationship between the optimal resource reservation and the demands of CUs is obtained. Then, for asymmetric information scenario, a contract-based resource reservation scheme is proposed to motivate wireless access point (WAP) to share the demand information of CUs and take part of over-reservation risk cost. Based on this, the theoretical expression for the optimal reserved resource is obtained by exploring the features of the feasible contracts under individual rationality and incentive compatibility constraints. The effectiveness of proposed contract-based resource reservation scheme is validated through simulation results.