Architecting for Adaptive Resource Management in Mobile Augmented Reality Systems: Models, Metrics and Prototype Software Solutions

Augmented reality simulations aims to provide realistic blending between real world and virtual objects. One of the important factors for realistic augmented reality is correct illumination simulation. Mobile augmented reality systems is one of the best options for introducing augmented reality to the mass market due to its low production cost and ubiquitousness. In mobile augmented reality systems, the ability to correctly simulate in realtime the illumination direction that matches the illumination direction of the real world is limited. Developing a mobile augmented reality systems with the ability to estimate illumination direction presents a challenge due to low computation power and dynamically changing environment. In this paper, we described a new method that we have developed for realtime illumination direction estimation for mobile augmented reality systems, using analysis of shadow produced by a reference object that doubles as a 3D augmented reality marker. The implementation of the method could estimate the direction of a single strong light source in a controlled environment with a very good degree of accuracy, with angular error averaging lower than 0.038 radians. The current implementation achieved 2.1 FPS performance in a low-end Android mobile device, produced proper estimation within 15 seconds using a uniform surface, and demonstrated scalability potential.

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

1 IntroductionNowadays, the institutes already use in the process of the advantages of mobile technology, such as portability (the students have easy access at any time to information), the use of digital assets (audio and video files) and huge amount of textual information. This study proposes a new system approach for improving the process by Mobile Augmented Reality. The features of this technology stimulate the environment and consider that the student's motivation is the most important factor in learning.The existing literature [1] highlights two different approaches for augmented reality (AR). Firstly, the authors consider that AR is a system which has the following characteristics: Combines real and virtual; Interactive in real time; Registered in 3-D. Secondly [1] considers Augmented Reality part of Virtual Continuum. This context is composed of two elements: Real Environment - which contains all representations from real life and Virtual Environment - which combines different elements from virtual life. ARs are considered closer to reality than virtual environment because this type of systems are supposed to extend the real objects with virtual elements.According to [2] Augmented Reality is a technology that extends the real world elements with 2D or 3D computer generated objects and lets the users interact with them. Also, the authors present three types of devices which can be used to view the result of AR technology: a Head Mounted Display (HMD), a mobile device - with at least one back camera and a desktop or a laptop computer.Similarly, [2] considers mobile technology as an alternative teaching resource because the users have easy access in real time to information. Also, mobile devices in the process two main problems of traditional are solved: the location and time. Likewise, [1] considers that mobile is a type of eLearning without restrictions of time and space and this technology improves the quality of classical environments, but does not replace them.Other researches, such as [3] and [4] highlight the benefits of a Mobile Augmented Reality system based on tracking objects. In [3] is proposed a mobile augmented reality system based on visual recognition. According to the authors the visual recognition consists into tracking objects identified in real world with the camera of the mobile device and augmenting the scene with virtual objects created by MAR technology. Similarly, [4] presents a Mobile Augmented Reality system bases on 3D registration technology. According to the authors the 3D registration represents the tracking of the position and pose of the mobile device in the real time and real life and finally use these information to insert in the real world the virtual scene created by MAR. The 3D registration technology is based on two type of approaches: markers and natural features. Recently, the 3D registration for MAR uses GPS and sensors from mobile devices to provide different information about position and pose of the camera and combine them with computer vision to improve the speed and accuracy of the system.Other studies, such as [5] propose a makerbased Mobile Augmented Reality solution. The application is used by a Vocational Educational and Training (VET) program for supporting the maintenance process of a car. The authors define VET as education programs that are designed for learners to acquire the knowledge, skills and competencies specific to a particular occupation, trade or class of occupations or trades(p. 49-50). Moreover, in [5] is presented an AR application used by VET students for basic mathematics skills, which is based on videogames.More studies, such as [6] present a Mobile Augmented Reality solution for French language which is based on gamification didactic method. The author, Bernadette Perry, describes gamification as using gamebased mechanics, aesthetics and game thinking to engage people, motivate action, and promote learning (p. …

Purpose of ReviewTheoretical frameworks for adaptive natural resource management are quite common, whereas documented examples showing successful implementation of adaptive management and learning through multiple time intervals have remained uncommon. Measures of quality of adaptive natural resource management processes are needed to examine potential factors driving the successful implementation. To address this gap, we developed a multimetric index composed of 22 metrics to assess quality of case studies using quantitative decision support (QDS) to inform adaptive forest management (AFM). Metrics represented three main tasks, including conceptual setup, modeling, and application. We further distinguished these into subtasks: definition of objectives and management options (setup); specifying uncertainty, prediction, and optimization (modeling); and stakeholder involvement along with practice and learning (application). We used a multimetric index to examine temporal and geographic variation in quality of reviewed case studies using QDS to inform AFM. We then conducted a structured literature review of 179 articles, wherein 34 case studies met a priori criteria.Recent FindingsWhen applying the multimetric index to these case studies, we found that over the past decade the index has been intermediate and annual average scores declined by 33% from 4.5 to 3.0 of 10 (where 10 is the highest possible quality score). Aligning with reviews of adaptive natural resource management, reported on-ground application of QDS to inform AFM was rare (n = 2). We also confirmed the expectation that there has been a substantial lack of stakeholder engagement during QDS development tasks.SummaryOur multimetric index provides a novel tool to examine gaps in the use of QDS for adaptive management in diverse domains including but not limited to forests.

This article presents a mobile Augmented Reality system, called DiedricAR, aimed at the learning of Descriptive Geometry. Thanks to its ability to recreate virtual models in real space, Augmented Reality is a technology suitable for making Descriptive Geometry comprehension and interpretation easier. The DiedricAR application allows students to learn in autonomously way by using their own mobile devices (smartphones and tablets), that work as Augmented Reality displays over training material (DiedricAR exercise workbook) specially designed for the new learning model defined by the European Higher Education System. Compared to some of the existing Augmented Reality systems used to learn Descriptive Geometry, DiedricAR offers the advantage of being specifically developed for mobile devices giving the students the possibility of using ubiquitous learning to its ultimate extent by interacting with the didactical content (i.e. showing the desired intermediate step when solving dihedral exercises). The presentation of DiedricAR is completed by exploring some key items such as the potential benefits for students' spatial ability, the relationship between application design and user experience, and software performance on several mobile devices.

Context-aware computers rely on user and physical models to describe the context of a user. In this paper, we focus on the problem of developing and maintaining a physical model of the environment using a mobile computer. We describe a set of tools for automatically creating and modifying three-dimensional contextual information. The tools can be utilized across multiple hardware platforms, with different capabilities, and operating in collaboration with one another. We demonstrate the capabilities of the tools using two mobile platforms. One of them, a mobile augmented reality system is used to construct a geometric model of an indoor environment which is then visualized on the same platform.

Support for intelligent, autonomous, adaptive and distributed resource management is a key to the success of scalable and dynamic wireless sensor network applications. Distributed independent reinforcement learning (DIRL) is a micro-learning framework that enables distributed, adaptive resource management using only local information at individual sensor nodes. In this paper we propose COllective INtelligence (COIN) a macro-learning paradigm that aims to specifically address the problem of designing utility functions for individual agents in order to achieve higher system wide utility. We extend DIRL, by combining it with COIN based macro-learning paradigm to steer the system towards global optimization, improving performance with minimal communication overheads. We present results of simulation to compare our approach with other existing approaches such as team game and DIRL in an example object tracking application. Simulation results demonstrate that a combination of micro- and macro-learners is two times more energy-efficient than micro-learners (DIRL) alone and four times more energy-efficient than macro-learners (TEAM game) alone.

Adaptive resource management (ARM) is an approach to managing that allows decision makers to learn about a system and subsequently change management actions based on new information about system processes (i.e., adapt) to better meet fundamental objectives. This is not to be confused with changing management actions when the state of the system changes. For example, changing a harvest regulation when populations decline is not ARM. This dynamic decision making may be fortuitously optimal, but if the effect of harvest is uncertain then changing regulations may be suboptimal—for example, weather may have caused the decline. Adaptive resource management can be implemented along a spectrum of passive to active to reduce system uncertainty. Active ARM is when explicit hypotheses are posited then implemented to test them, and monitoring occurs to elucidate whether the effect of the management action achieved a given result. Passive ARM uses current management practices, natural variation in the system, and monitoring to reduce uncertainty. Even though northern bobwhite (Colinus virginianus) have been studied for a century, uncertainty regarding optimal management strategies still exists. The Albany Quail Project (AQP) has used both modes of ARM to learn about northern bobwhite populations to better meet the hunting objectives of stakeholders. Between 1992 and 2019 the AQP radio-tagged 5,182 unique individuals and banded an additional 5,008 birds on the primary study area near Albany, Georgia, USA. Additionally, 1,724 nests have been monitored and population surveys conducted in the spring and autumn. Active ARM occurred with tests of supplemental feeding, hardwood cleanup, and predator control whereas passive ARM was used to learn about prescribed fire regimes, brood habitat preferences, small mammal cycles, and raptor migrations. We built an integrated population model (IPM) that combined known-fate survival data, nesting records, dead recoveries from harvest, and population surveys to model the system. The preliminary results of the IPM demonstrated that populations have increased during the project. The 5 most abundant autumn populations have occurred in the most recent 10 years of the project when bobwhite populations throughout their range have continued to decline. Unlike typical ARM applications, the AQP has not had an explicit model to predict consequences of future management actions; nonetheless, the essence of ARM has been followed. Since its inception uncertainty has been reduced, management actions have been changed, and bobwhite populations have responded. Improvements to AQP and similar projects could include explicit hypotheses and predictive models about the system

Elasticity is a key feature of current cloud computing platforms. Dependent on their demand tenants can dynamically scale up and down their applications. To increase their revenue, cloud providers are used to over-provision their clusters, but they still have to reserve capacity to avoid that services get unresponsive and cause SLO violation during bursts. In this paper, we propose CLOUDFARM, a PaaS architecture with an adaptive SLO-based resource management mechanism. It introduces new flexible SLAs backed with a respective development model and management interface for end-user services. According to their SLAs and the price tenants pay, services can be selectively downgraded to overcome short-term peaks, e.g. While preparing for scale-out. Providers can deploy services optimistically and thus maximize their data center utilization and revenue.

Compared with conventional phased array radar, co-located MIMO radar can effectively control transmitting beam width by means of sub-array division, providing greater flexibility in system resource management. To realize the effective resource management for the co-located MIMO radar in multi-target tracking, the resource management optimization model is proposed, where four working parameters can be controlled, including sub-array number, sampling period, transmitting waveform energy and working mode. The objective function integrates the average time and energy resource consumption of the system, and constraints are used to ensure that targets can be effectively detected. When solving the optimization problem, consider fully the influence of sub-array number on the other working parameters, the originally four-dimensional optimization problem is decomposed into several three-dimensional optimization problems, and finally the adaptive resource management algorithm for the co-located MIMO radar is obtained. The simulation results demonstrate that the proposed algorithm can control the working parameters of the co-located MIMO radar effectively. Furthermore, with the proposed adaptive resource management algorithm the average resource consumption of the co-located MIMO radar can be reduced effectively.

This paper presents two visual registration solutions for a mobile augmented reality system. The first one is a marker based solution whereas the second one is a hybrid approach. The hybrid method combines a coded marker technique for the initialization in the first frame, and a markerless registration in the next frames thanks to a 3-D model based tracking method. Because this mobile augmented reality system is designed for use in the industrial context of maintenance assistance for instance- robustness, accuracy, real-time and user comfort are the main concerns. For the different stages of the proposed solutions, various algorithms were evaluated to determine which one offers the best robustness and efficiency.KeywordsAugmented RealityReprojection ErrorMobile Augmented RealityCollaborative Augmented RealityInter Frame MotionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Adaptive water resources management recognizes the centrality of learning to its effective implementation.However, the literature on learning in Adaptive Management (AM) is disjointed and lacks rigorous treatment.The purpose of this paper is to clarify how learning associated with AM is distinctive and to explain the importance of successfully integrating it into policy design.Because adaptive learning holds a unique position within the adaptive management and assessment process, it should serve as the basis for a systematic redesign of water policy processes.Adaptive environmental assessment and management processes, which highlight the role of adaptive learning, are built upon in this paper.For the purpose of this paper, adaptive learning is defined as "an ongoing process of inquiry that incorporates new knowledge with the aim to continually improve management policies".In order for adaptive learning to live with and profit from nature's dynamism, it is argued that it must be problem-centered, decision relevant, reflective, and inherently transformative.When this kind of learning becomes a fully integrated component of AM, water resource institutions have a greater likelihood of maturing into actual "learning systems".The specific objectives of this paper are to: 1) redefine what adaptive learning is, 2) explain how this particular type of learning is distinctive and 3) outline the role of adaptive learning in policy formation.Regarding policy design, the paramount issue is determining how to respond to the speed, scale, and complexity of the planetary challenges.This paper draws on a number of related areas of research to present propositions for increasing the adaptive capacity of water organizations.These research topics include: demonstrating how adaptive learning should be integrated into policy design; establishing new standards of practice that are based on adaptive learning rather than achieving 'optimal' end-game solutions; using the breakthrough views in order to transform learning into effective action; framing management problems in non-reductionist ways so that nature may serve as an ally and pilot; using shadow networks to inspire innovation, encourage institutional learning, and improve governance rules; and creating a new social reality that is more future-responsive to problems and more hospitable to new ways of thinking about water management.

This paper presents a new context-aware mobile augmented reality system that provides rapid and robust on-site access to construction project information such as drawings, specifications, schedules, and budgets. The mobile augmented reality system does not need any RF-based location tracking (e.g., GPS or WLAN) or optical fiducial markers for tracking a user’s position. Rather, the user’s location and orientation are automatically and purely derived by comparing photographs from the user’s phone to a 3D point cloud model created from a set of site photographs. After generating a 3D point cloud model of construction site, field personnel can use mobile devices to take pictures of building elements and be presented on-site with a detailed list of project information related to the visible construction elements in an augmented reality format. The experimental results show that (1) the underlying 3D reconstruction module of the system generates more complete 3D point cloud models, and faster than other state-of-the-art Structure-from-Motion(SfM) algorithms; (2) the localization method is an order of magnitude more accurate than the state-of-the-art solutions, and can provide acceptable tolerances of most on-site engineering applications. Using an actual construction case study, the perceived benefits and limitations of the proposed method for on-site context-aware applications are discussed in detail.

Resource management strategy in adaptive cloud provisions the needed resources dynamically to the end-users. To improve the runtime performance of adaptive cloud for service-based applications, two aspects of technical issues are required to be addressed. The first one is the balancing of a large amount of data on existing resources and the second is resource provisioning which can adjust the number of resources optimally to adapt the time-varying workload. As the growth of data is increasing tremendously, efficient resource management is the need in cloud computing. We build a cloud framework to process data in automation with adaptive resource and workload management strategy. Numbers of approaches are reviewed and applied for workload prediction. We developed the model Auto-Regressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning (ARIMA-PERP) and evaluated the results that can satisfy the on-demand need of end-users for efficient resource utilization. To serve the maximum number of user requests, performance metrics of the proposed approach are evaluated. It is observed that our evaluated results achieved an accurate prediction by 91.11%, which meets the efficient resource utilization for the demanded workload. As compared with the exiting approach, we achieved better performance by 0.11% for accurate prediction. The proposed architecture is intended to provide the resources dynamically and efficiently satisfying the demands of the user. To achieve this objective of efficient resource provisioning, algorithms are developed for workload prediction which helps in deciding optimum resource provisioning. Our system uses proactive approach resource management and deployment of the adaptive cloud system. In traditional systems, resources are managed based on demand, availability and the strategy of scheduling which results in delayed response time at large. We configured the ARIMA model to predict the future workload for provisioning the resources dynamically and remove the problem of over-provisioning and under-provisioning in a cloud environment.

With the combination of deep learning and mobile edge computing, the accuracy of the computer vision task in mobile augmented reality (AR) applications can be significantly improved along with the enhancement on the end-to-end latency and energy efficiency. However, no architecture-based delay model for convolutional neural networks (CNNs) has been proposed in edge computing. In this paper, we first develop a new delay model to characterize the relation between the processing delay and the input image size of general CNN models. Then, we formulate a non-convex optimization problem to maximize the learning accuracy under the communication and computation resource constraints. By problem transformation, the optimal resource allocation policy is derived in closed-form and low-complexity search algorithm is also developed. Finally, test results validate the applicability of the delay model and demonstrate the learning accuracy improvement of the proposed algorithm.