Weirong Liu

A suboptimal and analytical solution to mobile robot trajectory generation amidst moving obstacles

In this paper, we present a suboptimal and analytical solution to the trajectory generation of mobile robots operating in a dynamic environment with moving obstacles. The proposed solution explicitly addresses both the robot kinodynamic constraints and the geometric constraints due to obstacles while ensuring the suboptimal performance to a combined performance metric. In particular, the proposed design is based on a family of parameterized trajectories, which provides a unified way to embed the kinodynamic constraints, geometric constraints, and performance index into a set of parameterized constraint equations. To that end, the suboptimal solution to the constrained optimization problem can be analytically obtained. The solvability conditions to the constraint equations are explicitly established, and the proposed solution enhances the methodologies of real-time path planning for mobile robots with kinodynamic constraints. Both the simulation and experiment results verify the effectiveness of the proposed method.

Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing

Nowadays, in order to deal with the increasingly complex applications on mobile devices, mobile cloud offloading techniques have been studied extensively to meet the ever-increasing energy requirements. In this study, an offloading decision method is investigated to minimize the energy consumption of mobile device with an acceptable time delay and communication quality. In general, mobile devices can execute a sequence of tasks in parallel. In the proposed offloading decision method, only parts of the tasks are offloaded for task characteristics to save the energy of multi-devices. The issue of the offloading decision is formulated as an NP-hard 0-1 nonlinear integer programming problem with time deadline and transmission error rate constraints. Through decision-variable relaxation from the integer to the real domain, this problem can be transformed as a continuous convex optimization. Based on Lagrange duality and the Karush-Kuhn-Tucker condition, a solution with coupled terms is derived to determine the priority of tasks for offloading. Then, an iterative decoupling algorithm with high efficiency is proposed to obtain near-optimal offloading decisions for energy saving. Simulation results demonstrate that considerable energy can be saved via the proposed method in various mobile cloud scenarios. A task offloading decision method is proposed among multi-devices for energy saving.The problem is formalized as a 0-1 nonlinear integer programming problem.An iterative decoupling algorithm that combines with decision-variable relaxation and convex optimization is proposed for near-optimal decisions.

Dynamic resource reservation via broker federation in cloud service: A fine-grained heuristic-based approach

In cloud computing, Infrastructure-as-a-Service (IaaS) cloud providers can offer two types of purchasing plans for cloud users, including on-demand plan and reservation plan. Generally reservation price is cheaper than on-demand price, while reservation plan may cause highly underutilized capacity problem. How to joint optimize the service cost and the resource utilization for clouds is a critical issue. To address this issue, a novel steady broker federation is developed to coordinate service demands in this paper. And the optimal reservation problem can be formulated as a nonlinear integer programming model. Then a fine-grained heuristic algorithm is proposed to reduce its computational complexity and obtain quasi-optimal solutions. Numerical simulations driven by large-scale Parallel Workloads Archive demonstrate that the proposed approach can save considerable costs for cloud users and improves the resource utilization for IaaS cloud providers.

A Newton-Based Extremum Seeking MPPT Method for Photovoltaic Systems with Stochastic Perturbations

Microcontroller based maximum power point tracking (MPPT) has been the most popular MPPT approach in photovoltaic systems due to its high flexibility and efficiency in different photovoltaic systems. It is well known that PV systems typically operate under a range of uncertain environmental parameters and disturbances, which implies that MPPT controllers generally suffer from some unknown stochastic perturbations. To address this issue, a novel Newton-based stochastic extremum seeking MPPT method is proposed. Treating stochastic perturbations as excitation signals, the proposed MPPT controller has a good tolerance of stochastic perturbations in nature. Different from conventional gradient-based extremum seeking MPPT algorithm, the convergence rate of the proposed controller can be totally user-assignable rather than determined by unknown power map. The stability and convergence of the proposed controller are rigorously proved. We further discuss the effects of partial shading and PV module ageing on the proposed controller. Numerical simulations and experiments are conducted to show the effectiveness of the proposed MPPT algorithm.

A distributed q learning spectrum decision scheme for cognitive radio sensor network

Cognitive spectrum management can improve the utilization efficiency of spectrum while increasing the energy consumption of sensor network nodes. Hence, how to balance the energy consumption and spectrum efficiency has become a critical challenge in the resource-constrained cognitive radio sensor networks. In this paper, by analyzing the channel characteristics and the energy efficiency of networks, a joint channel selection and power control spectrum decision algorithm based on distributed Q learning is proposed. To evaluate the performance of the proposed framework, an optimal Q value subject to communication efficiency index is formulated. Then, the learning strategy selection scheme is designed to solve the optimization problem by establishing a learning model. In this learning model, each node can get the strategy of other nodes to select the optimal strategy by introducing distributed strategy estimation. The simulation results show that the proposed algorithm has better performance than the existing methods.

Joint Relay and Jammer Selection and Power Control for physical Layer Security in Two-Way Relay Networks with Imperfect CSI

Physical layer security is an emerging security paradigm that can achieve secure information transmission from the source to the intended destination with the existence of malicious eavesdroppers. In this paper, joint relay/jammer selection and power control with friendly jammers for physical layer security in two-way relay networks are studied. First, several relay/jammer selection schemes are proposed to increase the secrecy capacity for source nodes and degrade the eavesdropper links simultaneously. The presented schemes select one conventional relay node and at most two friendly jammers among a number of intermediate nodes. The impact of channel estimation error on the wiretap channel is also considered. After the selection of optimal relay node and friendly jammers, a game-theoretic power control approach is introduced to deal with the interaction between the source nodes and the friendly jammers. The proposed power control approach is proven to be able to converge to the Stackelberg equilibrium, and both source nodes and friendly jammers can obtain their optimal benefits. The simulation results demonstrate the effectiveness of the relay/jammer selection schemes to enhance the secrecy capacity of the system, and validate the properties of optimization and convergence for the game-theoretic power control approach.

Flocking control for multi-agent systems with communication optimization

In this paper, we consider the flocking control and communication optimization problem for multi-agent systems in a realistic communication environment. In flocking control, it is common to specify the formation by setting the separation distance between neighboring agents and then design the control to maintain the desired formation. However, since communication quality will generally change during flocking in a physical environment, a predefined separation distance might not always be the desired communication distance. In this paper, a distributed scheme integrating potential function and extremum seeking algorithm is proposed to obtain the desired separation distance between neighboring agents in real time. A comprehensive performance index related to the environment is proposed, which captures a trade-off between formation tasks and communication quality. Since it is difficult to calculate the gradient of the performance in physical environments, an adaptive model-free extremum seeking algorithm is designed to solve for the optimal separation distances, which calls for no knowledge of the gradient of performance function. We illustrate the proposed method through simulations.

Distributed formation control for a cooperative multi-agent system using potential function and extremum seeking algorithm

The formation control and communication optimization problem of a multi-agent system is considered in this article. A classical approach for the formation control problem is potential function method. With potential function, agents are kept a given separation distance with neighbors. Since communication quality will generally change due to the physical environment during formation, a given separation distance might not always be the desired communication distance. In this paper, a distributed scheme integrating potential function and extremum seeking algorithm is proposed to obtain the desired separation distance between neighboring agents in real-time. A comprehensive performance index related to the environment is presented first, capturing a trade-off between formation tasks and communication quality. Since it is difficult to predict the gradient of the performance in physical environments, an adaptive model-free extremum seeking algorithm is developed, which calls for no knowledge of the gradient of the performance. Then the desired separation distance can be obtained dynamically by maximizing the performance with extremum seeking algorithm. Simulation results demonstrate effectiveness of the proposed scheme.

A joint subcarrier selection and power allocation scheme using variational inequality in OFDM-based cognitive relay networks

Introducing orthogonal frequency division multiplexing OFDM into cognitive radio CR can potentially increase the spectrum efficiency, but it also leads to further challenges for the resource allocation of CR networks. In OFDM-based cognitive relay networks, two of the most significant research issues are subcarrier selection and power allocation. In this paper, a non-cooperative game model is proposed to maximize the system throughput by jointly optimizing subcarrier selection and power allocation. First, taking the direct and relay links into consideration, an equivalent channel gain is presented to simplify the cooperative relay model into a non-relay model. Then, a variational inequality method is utilized to prove the existence and uniqueness of the Nash equilibrium solution of the proposed non-cooperative game. Moreover, to compute the solution of the game, a suboptimal algorithm based on the Lagrange function and distributed iterative water-filling algorithm is proposed. The proposed algorithm can jointly optimize the process of subcarrier selection and power allocation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed joint subcarrier selection and power allocation scheme. Copyright

Energy-Efficient Node Scheduling Method for Cooperative Target Tracking in Wireless Sensor Networks

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks.