Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We present a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast and is easy to deploy, requiring only one-hop local network geometry information. And it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

With an aim to resolve the problem of the increasing costs of supply chain in Manufacture Company, an integrated supply chain management model, driven by market demand, is brought forward in this paper, based on the analysis of existing integrated models of supply chain. The management model integrates purchase, production and sale plans with logistics plans used JIT. In order to build an integrated supply chain system which contains the inside management system and the outside supply chain system of manufacturing firm and a conceptual framework of integrated supply chain planning is also designed. This paper uses a three-stage integrated supply chain planning model to make supply chain plans, and a classification evaluation model is also suggested to analyze the feasibility of planning by comparing the planned cost with the anticipated cost. Research results indicate that this three-stage integrated supply chain planning model can optimize and reduce the costs of supply chain for manufacturing firms very well.