Mobile edge computing (MEC) has attracted extensive studies recently due to its ability to augment the computational capabilities of mobile devices. This paper considers a cache-enhanced multiuser MEC system where the task can be cached in the MEC servers to avoid the transmission of duplicate data. To further improve the energy efficiency and satisfy the users’ requirement on delay, we jointly optimize caching, computation, and communication resources in this system. The formulated problem is a mixed integer non-convex optimization problem that is very challenging to solve. We thus propose an efficient iterative algorithm by jointly applying the block coordinate descent and convex optimization techniques, which is guaranteed to converge at least a suboptimal solution. Specifically, the formulated joint optimization problem is decomposed into two subproblems to optimize caching policy and resource allocation, respectively, which are alternately optimized by convex optimization in each iteration. To further speed up the algorithm convergence, an efficient initialization scheme based on the linear weighted method is proposed for caching policy. The extensive simulation results are provided to demonstrate that the proposed jointly optimizing caching, computation, and communication method can improve the energy efficiency with lower time cost compared with other benchmark methods.

In order to improve resource utilization and network capacity, we propose the Device-to-Device (D2D) enabled Mobile Edge Computing (MEC) system, where multiple Smart Devices (SDs) transmit the offloading data to the MEC server with the help of wireless access point (WAP) selected from multiple WAPs. The SD uses the chosen WAP as the communication relay between the MEC server and itself. Aimed to minimize the total energy consumption of the system and satisfy the SDs‘ demand on delay, we jointly optimize relay selection and resource allocation in D2D-enabled MEC system. The problem is formulated as an integer-mixed non-convex optimization problem which is a NP-hard problem. We thus propose a two-phase optimization algorithm that jointly optimizes relay selection policy and resource allocation strategy. In first phase, the original problem is converted into a convex optimization problem by using convex optimization techniques, and the optimal relay selection policy can be achieved by solving the relay selection problem. After obtaining the relay selection policy, the original problem is transformed into a resource allocation problem solved by leveraging the Lagrange Method in the second phase. Furthermore, the proposed algorithm is a low-complexity algorithm which is associated with the root finding method. The optimal relay selection policy and resource allocation strategy can be found in polynomial time. The extensive simulation results are provided to indicate that the D2D-enabled MEC system achieves remarkable results in energy saving. Compared with other baseline methods, our proposed algorithm can not only achieve the optimal solution with less time cost, but also improve the energy efficiency and network capacity.

With the rapid development of the Internet of things(IoT), massive terminals and emerging applications bring a large amount of data. As an essential prerequisite for mining potential value from these data, data sharing is crucial and getting more and more attention. However, security and privacy concerns, and the lack of effective incentive mechanisms in the decentralized environment hinder the data owners from sharing their data. In this paper, we propose a federated learning and blockchain based privacy-preserving data sharing system, which solve the privacy leakage problem of federated learning and the scalability problem of blockchain by their technical complementarity. Considering the resource shortage and the heterogeneity of terminals, we first design a cross-layer architecture by exploiting the cloud-edge-terminal collaboration, and innovatively propose differential data sharing by dividing the providers into origin data providers and model providers. Then, we design a targeted incentive mechanism to maximize the utility of requesters and two types of data providers by formulating it into a two-stage Stackelberg game. Finally, a gradient-based algorithm is designed to get the optimal solution. Theoretical analysis and numerical simulations show that the proposed data-sharing system could eliminate privacy concerns and motivate more participants to share data. With 500 nodes, DASC is 1.72 seconds faster than SMC and 2.59 seconds faster than SDC. In terms of utility of requster, DASC is 2.0×103 higher than SDC, and 9.27 higher than SMC in average utility of providers.

As an effective method to increase the computing capability of vehicles, vehicle-to-vehicle (V2V) assisted vehicular edge computing (VEC) has great potential to reduce infrastructure spending and better adapt to mobility. However, its feasibility depends heavily on the sharing willingness, how to incentive selfish vehicles in such decentralized architecture is a vital problem. In this paper, we jointly consider the pricing strategy and the users’ concerns about security and privacy to incentive more participants. We first construct a P2P resource trading system based on consortium blockchain to build trust between strange vehicles and guarantee transaction security and user privacy without an authority center. To overcome the resource constraint on blockchain deployment and enhance the scalability of blockchain, the proposed system is built on edge-terminal architecture and improves the consistency algorithm. In addition, the V2V trading process is formalized as a two-stage Stackelberg game model to maximize the utility of both requesters and providers. The optimal pricing and trading volume strategy are derived from efficient optimization algorithms. Finally, we conduct a security analysis to show the performance of the trading process in terms of security and privacy. Numerical simulations show the effectiveness of the proposed strategy to motivate more participants.

As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning-assisted FL framework. Then, the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a Deep Q-Network (DQN)-based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but also has stronger robustness to resist malicious nodes.

The ocular albinism type 1 (OA1), a pigment cell-specific integral membrane glycoprotein, is a member of the G-protein-coupled receptor (GPCR) superfamily that binds to heterotrimeric G proteins in mammalian cells. We aimed to characterize the physiological functions an insect OA1 from Lymantria dispar (LdOA1) employs in the regulation of insecticide tolerance. In the present study, we investigated the roles of LdOA1 in response to deltamethrin exposure in both L. dispar and Drosophila melanogaster. LdOA1 was expressed at the lowest level during the 4^th instar stage, while LdOA1 was significantly upregulated in the 5^th instar and male stages. Knockdown of LdOA1 by injecting dsRNA of LdOA1 into gypsy moth larvae caused a 4.80-fold higher mortality than in control larvae microinjected with dsRNA of GFP under deltamethrin stress. Nine out of 11 L. dispar CYP genes were significantly downregulated under deltamethrin stress in LdOA1 silenced larvae as compared to control larvae. Moreover, the LdOA1 gene was successfully overexpressed in D. melanogaster using transgenic technique. The deltamethrin contact assay showed that the LdOA1 overexpression in flies significantly enhanced the tolerance to deltamethrin compared to the control flies. Furthermore, the downstream Drosophila CYP genes were upregulated in the LdOA1 overexpression flies, suggesting LdOA1 may play a master switch role in P450-mediated metabolic detoxification. This study is the first report of an insect OA1 gene regulating insecticide tolerance and potentially playing a role in the regulation of downstream cytochrome P450 expression. These results contribute to the future development of novel insecticides targeting insect GPCRs.