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To minimize the average latency of users to complete tasks, this letter jointly optimizes offloading decision, computation and bandwidth resource allocation for Mobile Edge Computing (MEC) networks via partial offloading. Since the considered problem is strongly non-convex with coupled variables, we decompose the original problem into two subproblems: (1) offloading decision; (2) communication bandwidth and computation resources allocation, and further employ Block Coordinate Descent (BCD) method to tackle them sequentially with linear computation complexity. Simulation results demonstrate that the proposed algorithm can achieve better performance on the average latency, and converge speedily.

This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, multiple base stations, and a malicious eavesdropper. Specifically, the users adopt the partial offloading strategy by partitioning the computation task into several parts. One is executed locally and the others are securely offloaded to multiple MEC servers integrated into the base stations by leveraging the physical layer security to combat the eavesdropping. We jointly optimize power allocation, task partition, subcarrier allocation, and computation resource to maximize the secrecy offloading rate of the users, subject to communication and computation resource constraints. Numerical results demonstrate that our proposed scheme can respectively improve the secrecy offloading rate 1.11%--1.39% and 15.05%--17.35% (versus the increase of tasks' latency requirements), and 1.30%--1.75% and 6.08%--9.22% (versus the increase of the maximum transmit power) compared with the two benchmarks. Moreover, it further emphasizes the necessity of conducting computation offloading over multiple MEC servers., Double-column, 5 pages, 3 figures, accepted for publication at the IEEE Communications Letter

Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($$\mathbf {P}_{\mathbf {T}}$$ P T ), resource allocation at UAV ($$\mathbf {P}_{\mathbf {R}}$$ P R ) and offloading decisions at IoT devices ($$\mathbf {P}_{\mathbf {O}}$$ P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration, therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.

The physical-layer security issue in the multiple non-regenerative wireless-powered relay (WPR) networks is investigated in this letter, where the idle relay is treated as a potential eavesdropper. To guarantee secure communication, the destination-based artificial noise is sent to degrade the receptions of eavesdroppers, and it also becomes a new source of energy powering relays to forward the information with power splitting (PS) technique. We propose an efficient algorithm ground on block-wise penalty function method to jointly optimize PS ratio and beamforming to maximize the secrecy rate. Despite the nonconvexity of the considered problem, the proposed algorithm is numerically efficient and is proved to converge to the local optimal solution. Simulation results demonstrate that the proposed algorithm outperforms the benchmark method., 5 pages, 4 figures, accepted by IEEE Communications Letters

For a user cooperation system with multiple antennas, we consider a trust degree based cooperation techniques to explore the influence of the trustworthiness between users on the communication systems. For the system with two communication pairs, when one communication pair achieves its quality of service (QoS) requirement, they can help the transmission of the other communication pair according to the trust degree, which quantifies the trustworthiness between users in the cooperation. For given trust degree, we investigate the user cooperation strategies, which include the power allocation and precoder design for various antenna configurations. For SISO and MISO cases, we provide the optimal power allocation and beamformer design that maximize the expected achievable rates while guaranteeing the QoS requirement. For a SIMO case, we resort to semidefinite relaxation (SDR) technique and block coordinate update (BCU) method to solve the corresponding problem, and guarantee the rank-one solutions at each step. For a MIMO case, as MIMO is the generalization of MISO and SIMO, the similarities among their problem structures inspire us to combine the methods from MISO and SIMO together to efficiently tackle MIMO case. Simulation results show that the trust degree information has a great effect on the performance of the user cooperation in terms of the expected achievable rate, and the proposed user cooperation strategies achieve high achievable rates for given trust degree., 15 pages,9 figures, to appear in IEEE Transactions on Wireless communications

To reduce cooperation overhead in Amplify-and-Forward (AF) relay networks, a group-sparse penalty is introduced into the objective function to design beamformers for balancing the size of active relays and the signal-to-noise ratio (SNR) at the destination. To deal with the non-convexity and non-smoothness of the problem, the proximal gradient algorithm (PGA) is proposed, which can converge to a local optimal solution in theory. The simulation results show that the proposed algorithm can yield the high level of SNR performance with a small set of cooperative relays to reduce cooperation overhead and economize power.