Glassist: Using Augmented Reality on Google Glass as an Aid to Classroom Management

Silva, Manoela; Freitas, Daniel; Neto, Edvar Vilar; Lins, Caio; Teichrieb, Verônica; Teixeira, João Marcelo X. N.

doi:10.1109/svr.2014.41

Cited by 26 publications

(11 citation statements)

References 5 publications

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“…In real life, the applications running on the mobile devices of the students can be variable. For example, it can be an augmented reality application on Google Glass, an infotainment application, or a health application using a foot‐mounted inertial sensor . In our simulations, the mobile devices utilize three different applications and the edge and cloud servers provide corresponding services.…”

Section: Edgecloudsim In Operationmentioning

confidence: 99%

EdgeCloudSim: An environment for performance evaluation of edge computing systems

Sonmez

Özgövde

Ersoy

2018

Trans Emerging Tel Tech

289

171

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Edge computing is a fast growing field of research that covers a spectrum of technologies bringing the cloud computing services closer to the end user. Growing interest in this area yields many edge computing approaches that need to be evaluated and optimized. Experimenting on the real cloud environments is not always feasible due to the operational cost and the scalability. Despite increasing research activity, this field lacks a simulation tool that supports the modeling of both computational and networking resources to handle the edge computing scenarios. Existing network simulators can model the network behavior at different levels of granularity. The cloud computing simulators support the modeling and simulation of the computational infrastructures and services efficiently. Starting from the available simulators, a significant programming effort is required to obtain a simulation tool meeting the actual needs. On the other hand, designing a new edge computing tool has many challenges such as the scalability, extensibility, and modeling the mobility, network, and virtualized resources. To decrease the barriers, a new simulator tool called EdgeCloudSim streamlined for the edge computing scenarios is proposed in this work. EdgeCloudSim builds upon CloudSim to address the specific demands of edge computing research and support the necessary functionalities. To demonstrate the capabilities of EdgeCloudSim, an experiment setup based on different edge architectures is simulated. In addition, the effect of the edge server capacity and the mobility on the overall system performance are investigated.

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Section: Edgecloudsim In Operationmentioning

confidence: 99%

EdgeCloudSim: An environment for performance evaluation of edge computing systems

Sonmez

Özgövde

Ersoy

2018

Trans Emerging Tel Tech

289

171

View full text Add to dashboard Cite

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“…On the other hand, orchestrator of the FCTO scheme distributes the incoming tasks among the edge servers. In order to present a realistic simulation for different real-life scenarios, we used three different applications during the experiments: an augmented reality (AR) application, an infotainment (I) application, and a health monitoring (HM) application [ 42 , 43 , 44 ]. Among them, the HM application is latency-sensitive, and the infotainment application is delay-tolerant.…”

Section: Performance Evaluationmentioning

confidence: 99%

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Hossain

Sultana

Hossain

et al. 2021

Sensors

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Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

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“…According to [12,58], to simulate more realistically, we use four different application types in our study. First, an augmented reality application on Google Glass is presented in [59]. Second, the infotainment application is discussed in [47].…”

Section: Performance Evaluationmentioning

confidence: 99%

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Khanh

Nguyen

Huh

2021

Wireless Communications and Mobile Computing

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Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high-quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy-based MEO is used to handle the multicriteria decision-making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy-based MEO, the fuzzy-based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy-based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy-based (FuB) algorithm, for a fuzzy-based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute-intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.

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Glassist: Using Augmented Reality on Google Glass as an Aid to Classroom Management

Cited by 26 publications

References 5 publications

EdgeCloudSim: An environment for performance evaluation of edge computing systems

EdgeCloudSim: An environment for performance evaluation of edge computing systems

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

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