The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices’ limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT–edge–cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free–bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments

Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attentions from both the academia and industry in recent years. As an integration of embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates cyber processes into physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing coupling effects among the three interacted CPS components, i.e., communication, computation and control, is envisioned as the key to properly utilize all available resources and hence improve the performance of the UAV network. In this paper, we present a comprehensive survey on UAV networks from a CPS perspective. Firstly, we review the basics and advances of the three CPS components in UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying UAV networks into three hierarchies, i.e., cell level, system level, and system of system level. Furthermore, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we intend to provide a novel insight into the state of the art in UAV networks.

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications

Low-cost drones represent an emerging technology that opens the horizon for new smart Internet-of-Things (IoT) applications. Recent research efforts in cloud robotics are pushing for the integration of low-cost robots and drones with the cloud and the IoT. However, the performance of real-time cloud robotics systems remains a fundamental challenge that demands further investigation. In this paper, we present DroneTrack, a real-time object tracking system using a drone that follows a moving object over the Internet. The DroneTrack leverages the use of Dronemap planner (DP), a cloud-based system, for the control, communication, and management of drones over the Internet. The main contributions of this paper consist in: (1) the development and deployment of the DroneTrack, a real-time object tracking application through the DP cloud platform and (2) a comprehensive experimental study of the real-time performance of the tracking application. We note that the tracking does not imply computer vision techniques but it is rather based on the exchange of GPS locations through the cloud. Three scenarios are used for conducting various experiments with real and simulated drones. The experimental study demonstrates the effectiveness of the DroneTrack system, and a tracking accuracy of 3.5 meters in average is achieved with slow-speed moving targets.

https://www.cister.isep.ipp.pt/docs/dronetrack__cloud_based_real_time_object_tracking_using_unmanned_aerial_vehicles_over_the_internet/1365/view.pdf

DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet

DroneTrack: Cloud-Based Real-Time Object Tracking using Unmanned Aerial Vehicles

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing thecomputation to the cloud. However, exploiting the potential ofcloud resources in robotic software is difficult, because it in-volves complex code modification and extensive (re)configurationprocedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. Inthis paper, we propose a transparent and QoS-aware softwareframework called Cloudroid for cloud robotic applications. Thisframework supports direct deployment of existing robotic soft-ware packages to the cloud, transparently transforming theminto Internet-accessible cloud services. And with the automati-cally generated service stubs, robotic applications can outsourcetheir computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintainthe specific QoS property such as request response time, evenin a highly dynamic and resource-competitive environment. Weevaluated Cloudroid based on a group of typical robotic scenariosand a set of software packages widely adopted in real-worldrobot practices. Results show that robots capability can beenhanced significantly without code modification and specific QoSobjectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitudecompared with the robot native setup.

Cloudroid: A Cloud Framework for Transparent and QoS-Aware Robotic Computation Outsourcing

The invention discloses a semantic map construction method based on cloud robot mixed cloud architecture, and aims to achieve a proper balance for improving object identification accuracy and shortening identification time. The technical scheme of the method is that mixed cloud consisting of a robot, a private cloud node and a public cloud node is constructed, wherein the private cloud node obtains an environment picture shot by the robot and milemeter and position data on the basis of an ROS (Read-Only-Storage) message mechanism, and SLAM (Simultaneous Location and Mapping) is used for drawing an environmental geometric map in real time on the basis of the milemeter and position data. The private cloud node carries out object identification on the basis of an environment picture, and an object which may be wrongly identified is uploaded to the public cloud node to be identified. The private cloud node maps an object category identification tag returned from the public cloud node and an SLAM map, and the corresponding position of the object category identification tag on a map finishes the construction of a semantic map. When the method is adopted, the local calculation load of the robot can be lightened, request response time is minimized, and object identification accuracy is improved.

Semantic map construction method based on cloud robot mixed cloud architecture

The invention discloses a method for accessing a software kit of a robot via cloud service protocols A cloud robot computing node environment composed of robot computing nodes and a cloud computing center is established, whether a theme message type corresponding a publishing theme name of the robot computing nodes is the same with a theme message type of an identical subscribing theme name of the cloud computing center is determined, and whether a theme message type of a subscribing theme name of the robot computing nodes is the same with a theme message type of a publishing theme name of the cloud computing center is determined; and if both are YES, the robot computing nodes subscribe an ROS message of the identical theme of the cloud computing center by using the subscribing theme name, and the robot computing nodes publish the ROS message to the cloud computing center according the publishing theme name Thus, the ROS robot software kit can be disposed in the cloud computing center needless of modification, and WebSocket protocols are used to realize interaction of the ROS message between the robot computing nodes and the cloud computing center

Method for accessing software kit of robot via cloud service protocols

Incremental learning is a machine learning paradigm that the training dataset grows dynamically at runtime and the prediction accuracy can be improved incrementally. Since reallife datasets are usually open-ended and dynamic, incremental learning is important to many image classification applications. In existing incremental algorithms, KNN (K Nearest Neighbors) has the advantage of dealing with the hugely and incrementally multiclass nature of visual object recognition. However, it provides good prediction only when the query image is similar to one of the images in the dataset, which results in low accuracy in many cases. In this paper, we propose a hybrid incremental learning algorithm named IKNN-SVM which can improve the recognition accuracy while reserving the advantages of KNN, i.e., quickly learning from the increasing number of data. To achieve this goal, our incremental algorithm combines SVM (Support Vector Machine) with high recognition accuracy and KNN together. And to promote the incremental learning efficiency, IKNN-SVM introduce kd-tree to implement fast searching. The experimental result based on the open dataset Caltech256 shows that the recognition accuracy of our method improves by 12%26% compared with incremental KNN but with short time consumption. Keywords-component; support vector machine; kd-tree; k nearest neighbors; image classification; incremental learning

/pdf/iknn-svm-a-hybrid-incremental-algorithm-for-image-4wdw1ai38d.pdf

IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected objects of which it has no knowledge. It will lead to inevitable failures in traditional environment modeling software. The abundant knowledge being accumulated on the Internet has the potential to assist robots to handle such kind of uncertainly. However, existing researches have not touched this issue yet. This paper proposes a cloud-based semantic mapping engine named SemaCloud, which can not only augment robot's environment modeling capability by the rich cloud resources but also cope with uncertainty by utilizing the Internet knowledge on necessary. It adopts a state-of-art Deep Neural Network (DNN) for real-time and accurate recognition of pre-trained objects. If an object is beyond the knowledge of this DNN, a special mechanism named QoS-aware cloud phase transition is triggered to seek help from existing recognition services on the Internet. By a set of carefully-designed algorithms, it can maximize benefits and minimize the negative impacts on the Quality of Service (QoS) properties of robotic applications, which is essential to many robot scenarios. The experiments on both open datasets and real robots show that our work can handle uncertainly successfully in robotic semantic mapping without sacrificing critical real-time constraints.

Huimin Che

Papers

Cloudroid: A Cloud Framework for Transparent and QoS-Aware Robotic Computation Outsourcing

Semantic map construction method based on cloud robot mixed cloud architecture

Method for accessing software kit of robot via cloud service protocols

IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

Learning from Internet: Handling Uncertainty in Robotic Environment Modeling