Panorama: A Framework to Support Collaborative Context Monitoring on Co-located Mobile Devices

Alanezi, Khaled; Zhou, Xinyang; Chen, Lijun; Mishra, Shivakant

doi:10.1007/978-3-319-29003-4_9

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Microcast [11] also utilizes collaborations between smartphones to split up video streaming task assuming that co-located users are watching the same content. Panorama [12] is another system that is based on collaborative computing. However, in addition to nearby mobile devices, Panorama considers available edge and cloud resources to further minimize the task allocated to battery powered devices.…”

Section: Opportunistic Computingmentioning

confidence: 99%

“…DNN Modules). This service builds a model to predict the execution time for the software component given the input task size [20,12], which is beneficial for time performance optimization decisions when deciding the distributed execution plan. In addition, any identifying information to execute the task along with performance metrics of execution accuracy are also stored in the Assets database to be used as part of the optimization process that is carried The latter uses the information gathered from the edge server and the mobile device to devise the best execution plan.…”

Section: Architecturementioning

confidence: 99%

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An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

Alanezi

Mishra

2021

Future Generation Computer Systems

Self Cite

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In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energyefficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment.

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Section: Opportunistic Computingmentioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

Alanezi

Mishra

2021

Future Generation Computer Systems

Self Cite

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“…CoMon [19] is a platform for cooperative context monitoring between collocated devices that uses a benefit-aware negotiation mechanism in order to improve the outcome of the cooperation in terms of energy usage. A similar approach is taken by Panorama [20]. In addition to collocated devices, Panorama also involves clouds and cloudlets in the cooperation process in order to increase the performance of continuous context monitoring.…”

Section: A Collaborative and Distributed Sensingmentioning

confidence: 99%

SenseLE: Exploiting Spatial Locality in Decentralized Sensing Environments

Bozdog

Makkes

Uta

et al. 2017

2017 IEEE International Symposium on Parallel and Distributed Processing With Applications and 2017 IEEE International Conferen

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Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%.

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“…Edge computing was recently introduced in an attempt to reduce the high latencies of faraway clouds and also to improve scalability [5]. The usefulness of offloading mobile computation to edge devices was proved by systems addressing different types of real-time problems, like improving the quality of video streaming [11] or monitoring the context of the user [12]. As opposed to cloud computing, where the computation is executed in high performance dat-acenters, edge computing allows for computation to be done on a large variety of networked devices, like WiFi Access Points [13], cloudlets [12] or ad hoc networks of smartphones [14] [15].…”

Section: Related Workmentioning

confidence: 99%

Kea: A Computation Offloading System for Smartphone Sensor Data

Das

Bozdog²,

Makkes³

et al. 2017

2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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Panorama: A Framework to Support Collaborative Context Monitoring on Co-located Mobile Devices

Cited by 6 publications

References 17 publications

An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

SenseLE: Exploiting Spatial Locality in Decentralized Sensing Environments

Kea: A Computation Offloading System for Smartphone Sensor Data

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