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Since the flight of Unmanned Aerial Vehicle (UAV) greatly consumes the limited on-board energy and affects the ability to afford computation services, it is urgent and challenging to minimize UAV’s endurance time while satisfying varieties of constraints from the UAVs and users, such as energy budgets, cache capacities, computation resources, and latency requirements. In this paper, the UAVs are endowed with the capability to temporarily buffer some offloaded tasks and process them later, then adopt cooperative computation and cache scheduling to minimize their total endurance time. Following this, the formulated problem is a mix-integer nonlinear programming problem, which can be further transformed into three more tractable subproblems: 1) PO, offloading decision and scheduling strategy for computing & caching; 2) PH, hovering trajectory design; 3) PC, computation resource allocation, and then iteratively tackled in a sequence. Due to the high complexity of solving PO, the low-complexity algorithm based on the K-means clustering and Penalty Method is proposed to reduce complexity and facilitate the processing procedure, which can save about 91.04%-189.36% running time and ONLY sacrifice 8.05%-22.08% of the total endurance time. Simulation results show that cooperative computation and cache scheduling has a great effect on saving endurance time.

As the power density of modern CPUs is gradually increasing, thermal management has become one of the primary concerns for multicore systems, where task scheduling and dynamic voltage/frequency scaling (DVFS) play a pivotal role in effectively managing the system temperature. In this paper, we propose CARTAD, a new reinforcement learning (RL) based task scheduling and DVFS method for temperature minimization and latency guarantee on multicore systems. The novelty of CARTAD framework is that we exploit machine learning technique to analyze the applications’ intermediate representations (IRs) generated by compiler and identify an important feature which is critical for predicting the application’s performance. With the newly explored feature, we construct a RL-based scheduler with the more effective state representation and reward function such that the system temperature can be minimized while guaranteeing applications’ latency. We implement and evaluate CARTAD on real platforms in comparison with the stateof-the-art approaches. Experimental results show CARTAD can reduce the maximum temperature by up to 16∘C and the average temperature by up to 10∘C.

Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions.

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.

With the growing concerns about energy saving and security problems in wireless sensor networks (WSNs), we investigated a joint energy saving scheduling and secure routing algorithm for critical event reporting in WSN. When a critical event has been detected, the notification must be safely sent to the sink node through the uplink transmission, and the sink node needs to broadcast the alarm messages to the whole network through the downlink transmission. In the uplink, a joint power allocation and secure routing strategy (JPASR) has been proposed to maximize the routing secure connection probability (RSCP) under the constraint of power. Meanwhile, combined with the level-by-level sleeping scheduling method, the energy-saving and secure uplink transmission can be guaranteed. In the downlink, an energy-first multi-point relays set selection mechanism (EFMSS) is designed to choose the backbone nodes to broadcast messages, and the backbone nodes are waken up by the same level-by-level sleeping scheduling method as the uplink transmission. With the two-step procedure, the critical events are appropriately dealt with and the responses are broadcast to the whole network. Extensive simulations results demonstrate that the energy saving and security performances of the proposed method are superior to the existing ones.

Mobile edge computing (MEC) provides users with a high quality experience (QoE) by placing servers with rich services close to the end users. Compared with local computing, MEC can contribute to energy saving, but results in increased communication latency. In this paper, we jointly optimize task offloading and resource allocation to minimize the energy consumption in an orthogonal frequency division multiple access (OFDMA)-based MEC networks, where the time-sensitive tasks can be processed at both local users and MEC server via partial offloading. Since the optimization variables of the problem are strongly coupled, we first decompose the original problem into two subproblems named as offloading selection (PO), and subcarriers and computing resource allocation (PS), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for PO, and deal with PS by an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate, IEEE GLOBECOM 2020. arXiv admin note: substantial text overlap with arXiv:2003.12719

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.

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data offloading is studied where mobile date can be computed at both local devices and the edge cloud with the consideration of time-sensitive tasks for users. Since the NP-hardness of the considered optimization problem, we propose an iterative algorithm to decide the proportion of data to offload and design the resource allocation strategy in a sequence. Simulation results show that the proposed algorithm achieves better performance than the reference schemes.

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 standby-sparing (SS) is a promising technique which deploys the dual-processor platform, i.e., one primary processor and one spare processor, to achieve fault tolerance for real-time systems. In the existing SS framework, all applications have their backup copies on the spare processor, but, in practice, not all applications on a system are equally important to the system. Some low critical tasks may be traded off for other system objectives. Motivated by this, in this paper, we integrate the concept of criticality into the SS framework. Such integration enables the SS framework to further reduce energy consumption. We propose an offline approach to determine an energy-efficient frequency for the primary processor. Additionally, as the cluster systems are emerging as the mainstream computing platform, we consider the SS technique on the cluster/island systems and propose an algorithm to determine the energy-efficient algorithm for such systems. We evaluate the proposed approach on synthetic tasks and real-platforms. The experimental results demonstrate the effectiveness of our proposed framework in terms of energy efficiency.

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

In this work, we maximize the secrecy rate of the wireless-powered untrusted relay network by jointly designing power splitting (PS) ratio and relay beamforming with the proposed global optimal algorithm (GOA) and local optimal algorithm (LOA). Different from the literature, artificial noise (AN) sent by the destination not only degrades the channel condition of the eavesdropper to improve the secrecy rate, but also becomes a new source of energy powering the untrusted relay based on PS. Hence, it is of high economic benefits and efficiency to take advantage of AN compared with the literature. Simulation results show that LOA can achieve satisfactory secrecy rate performance compared with that of GOA, but with less computation time., Comment: Submitted to GlobeCom2015