In recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

Vehicular ad hoc networks (VANETs) has become an important part of modern intelligent transportation systems (ITS). However, under the influence of malicious mobile vehicles, offloading vehicle tasks to the cloud server is threatened by security attacks. Edge cloud offloading (ECCO) has considered a promising approach to enable latency-sensitive VANET. How to solve the complex computation offloading of vehicles while ensuring the high security of the cloud server is an issue that needs urgent research. In this paper, we studied the safety and offloading of multi-vehicle ECCO system based on cloud blockchain. First, to achieve consensus in the vehicular environment, we propose a distributed hierarchical software-defined VANET (SDVs) framework to establish a security architecture. Secondly, to improve the security of offloading, we propose to use blockchain-based access control, which protects the cloud from illegal offloading actions. Finally, to solve the intensive computing problem of authorized vehicles, we determine task offloading via jointly optimizing offloading decisions, consensus mechanism decisions, allocation of computation resources and channel bandwidth. The optimization method is designed to minimize long-term system of delays, energy consumption, and flow costs for all vehicles. To better resolve the proposed offloading method, we develop a new deep reinforcement learning (DRL) algorithm via utilizing extended deep Q-networks. We evaluate the performance of our framework on access control and offloading through numerical simulations, which have significant advantages over existing solutions.

With the evolution of mobile phone sensing and wireless networking technologies, mobile crowd sensing (MCS) has become a promising paradigm for large-scale sensing applications. MCS is a type of multi-participant sensing that has been widely used by many sensing applications because of its inherent capabilities, e.g., high mobility, scalability, and cost effectiveness. This paper reviews the existing works of MCS and clarifies the operability of MCS in sensing applications. With wide use and operability of MCS, MCS's industrial applications are investigated based on the clarifications of: 1) the evolution of industrial sensing and 2) the benefits MCS can provide to current industrial sensing. As a feasible industrial sensing paradigm, MCS opens up a new field that provides a flexible, scalable, and cost-effective solution for addressing sensing problems in industrial spaces.

We propose a task-oriented multi-objective scheduling method based on ant colony optimization (MOSACO) to optimize the finite pool of public and private computing resources in a hybrid cloud computing environment according to deadline and cost constraints. MOSACO is employed to minimize task completion times and costs using time-first and cost-first single-objective optimization strategies, respectively, and to maximize user quality of service and the profit of resource providers using an entropy optimization model. The effectiveness of the MOSACO algorithm based on multiple considerations of task completion time, cost, number of deadline violations, and degree of private resource utilization is verified using simulation and three application examples. Comparisons with similar scheduling methods demonstrate that MOSACO provides the highest optimality, and that the time-first and cost-first strategies provide definite advantages for minimizing completion time and cost, respectively.

To improve the quality of service (QoS) for mobile users (MUs) and the quality of experience (QoE) of mobile devices (MDs), mobile edge computing (MEC) is a promising approach that offloads a part of the computing task from MDs to nearby MUs. In this paper, we study computation offloading involving multiple users and multiple base stations (BSs), where the MD that is connected to the MU is wirelessly charged and BSs are available to be selected for computation offloading. We model the process of solving an optimal computation offloading policy into a Markov decision process (MDP), in which our goal is to maximize the long-term utility performance. Therefore, a computation offloading policy is obtained based on the energy queue state, the task queue state, and the channel states between the MUs and BSs. To address the problem of high dimensionality in the state space, we decompose the MDP into a series of single-agent MDPs with reduced state spaces and apply an online local learning algorithm to learn the optimal state value functions. Inspired by the structure of the utility function, we propose an algorithm based on combining Q-function reconstruction with the post-decision state. It is proved that the proposed algorithm can converge to an optimal computation offloading policy. The experimental results show that our algorithm achieves significant performance in computation offloading and schedule compared with the other three basic policies.