With the rapid development of intelligent Internet of Things (IoT) technology, many intensive computing workflow applications have been generated every day. Edge-cloud collaboration computing is a promising computational paradigm that combines the advantages of both edge and cloud to improve the application QoS, shorten the time latency, and reduce the energy of the terminals. However, joining the heterogeneous resources for edge-cloud workflows efficiently and safely is still challenging. In this article, we develop an Energy-aware and Trust-collaboration Cross-domain Resource Allocation algorithm (ETCRA) for edge-cloud workflows. The objective is to minimize the Comprehensive System Function (CSF) while guaranteeing the latency constraints of the workflows and trust constraints of the cross-domain edges. A dynamic algorithm is proposed to solve the formulated problem and to obtain the optimal task-resources mapping decision. It consists of two phases: 1) initial resource allocation decision-making based on Particle Swarm Optimization (PSO) statically; and 2) real-time updating decision-making based on the trust value assessment dynamically. Simulation results verify the effectiveness of ETCRA and prove that the proposed scheme significantly outperforms other baselines on four key measurements, including the CSF, total execution time, total energy consumption, and reliability performance.

We consider the position control problem of the planar 3R manipulator with the middle passive joint, whose dynamic constraint is caused by the second-order non-holonomic characteristic. A drift suppression control method with two stages is proposed for positioning the system. First, we give the dynamic model and the target angle of the system solution algorithm. Then, the controllers are designed to make the active links reach to their desired states, and the second link is rotating freely caused by the drift. So, the parametric trajectory is planned to suppress the drift of the second link and tracking this trajectory can makes all links reach to their desired states. Next, we use the differential evolution algorithm to search for the suitable parameters of the parametric trajectory taking the coupling constraints among all links. Then the controllers are designed to track the planned trajectories, so that all links can be controlled to the desired states simultaneously. Finally, numerical simulation results illuminate the effectiveness of the proposed method.

With the rapid growth of the Industrial Internet of Things (IIoT), the Mobile Edge Computing (MEC) has coming widely used in many emerging scenarios. In MEC, each workflow task can be executed locally or offloaded to edge to help improve Quality of Service (QoS) and reduce energy consumption. However, most of the existing offloading strategies focus on independent applications, which cannot be applied efficiently to workflow applications with a series of dependent tasks. To address the issue, this paper proposes an energy-efficient task offloading strategy for large-scale workflow applications in MEC. First, we formulate the task offloading problem into an optimization problem with the goal of minimizing the utility cost, which is the trade-off between energy consumption and the total execution time. Then, a novel heuristic algorithm named Green DVFS-GA is proposed, which includes a task offloading step based on the genetic algorithm and a further step to reduce the energy consumption using Dynamic Voltage and Frequency Scaling (DVFS) technique. Experimental results show that our proposed strategy can significantly reduce the energy consumption and achieve the best trade-off compared with other strategies.

Mobile cloud computing (MCC) which can invoke cloud services and offload tasks from the mobile device to the cloud has become an appropriate computing paradigm to provide many useful and complex workflow applications. However, offloading tasks needs extra communication time and energy, which leads to a conflict between saving the energy and improving the QoS. Thus, how to allocate these different resources (mobile or cloud) efficiently during the workflow execution process to optimize the system energy consumption under the deadline constraint is still a huge challenge in MCC. To address such an issue, this article proposes an energy-efficient resource allocation strategy for workflow applications in MCC. Firstly, we formulate the resource allocation problem into an optimization problem with the goal of minimizing the UtilityCost which represents the trade-off between energy consumption and the total execution time. Then, we use an energy-efficient resource allocation algorithm shortened as EERA to solve the problem. The algorithm firstly finds all the Partial critical paths of the workflow graph to segment the whole optimization problem into several local problems, and then uses the discrete particle swarm optimization algorithm to find the local optimal solution for every local problem, finally the local optimal solution is integrated to obtain the global one. In addition, we have adapted several representative algorithms for evaluation and comparison purpose. Comprehensive experimental results show that our EERA based strategy achieves the best overall performance on three key measurements including the energy consumption of the device, the execution time of the workflow and the UtilityCost.