JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads

Shankar, Karthick; Wang, Pengcheng; Xu, Ran; Mahgoub, Ashraf; Chaterji, Somali

doi:10.1109/cloud49709.2020.00088

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…The post-processing method is then applied to estimate the crack’s thickness and provide the complete crack pattern. Shankar and Wang [ 111 ] proposed a Fully Convolutional Neural Network (FCNN) model for anomaly detection, while Ishtiak and Ahmed [ 43 ] utilised a two-step image classification approach in their FCNN model. The first step involved feeding road surface images into the FCNN, with the model achieving 87% accuracy for all classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee

Bačić

Doborjeh

2023

Sensors

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Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee

Bačić

Doborjeh

2023

Sensors

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“…• Effective sharing of data processing and analytics load between sensors, edge devices [10], [11], and the cloud for maximizing throughput or latency, based on the client (farmer/farm manager) preferences [12], [13]. • Optimized cloud computation for beefier machine learning workloads using vision APIs, e.g., Azure Vision or Amazon Rekognition [14] or processing streaming workloads using on-premise database optimization or using optimized clustered cloud instances [15]. Examples of such optimization can be found in our recent work for on-premise database optimization [16] for streaming workloads or cloud optimization for beefier vision [17] or lighter-weight, but latency-sensitive, IoT workloads [4].…”

Section: Identified Gaps Motivating Latticementioning

confidence: 99%

“…Analytics workloads are often quite demanding, and do not fit, out-of-the-box, into the embedded devices, deployed in agriculture. Advanced processing for backend analytics may leverage edge platforms, e.g., Azure IoT Edge device [35], [21], and there may be sensitivity of farmers to upload personal data to the cloud. Interpretable analytics are important because the farmers will require insights into the results of the algorithm, at their level of understanding, to potentially take action.…”

Section: Data Analytics For Digital Agriculturementioning

confidence: 99%

Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

Chaterji

DeLay

Evans

et al. 2021

IEEE Open J. Comput. Soc.

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Digital agriculture, with the incorporation of Internet-of-Things (IoT)-based technologies, presents the ability to observe and control a system at multiple levels (individual, local, regional, and global) and generate tools that allow for improved decision making and higher productivity. Recent advances in IoT hardware, e.g., networks of heterogeneous embedded devices, and software, e.g., lightweight computer vision algorithms and cloud optimization solutions, make it possible to collect data and efficiently process data from diverse sources in a connected (smart) farm. By interconnecting these IoT devices, often across large swaths of farmland, it is possible to collect data from multiple farming and food systems, and at different time scales, including in near real-time (i.e., with delays of only a few tens of seconds). This data can then be used for actionable insights, e.g., precise applications of soil supplements and reduced environmental footprint.Through LATTICE, we present an integrated vision for IoT solutions, data processing, and actionable analytics, with economics and policy considerations for digital agriculture. Our paper starts off with the types of datasets in typical field operations, followed by the lifecycle for the data and storage and fast information-retrieval solutions. It then goes on to describe the most prevalent and promising aspects of machine learning and cloud computing for digital agriculture. IoT devices that are capable of sensing different kinds of information connect with each other in a wireless sensor network setting. These form a rich source for optimizing IoT data collection and subsequent analysis. We discuss what algorithms are proving to be most impactful in this space, e.g., approximate data analytics and on-device/in-network processing. We conclude by discussing analytics for alternative agriculture for generation of biofuels and policy challenges in the implementation of digital agriculture in the wild.

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“…We call this real IoT network a living testbed for use in downstream applications, such as approximate computer vision applications to monitor crop health, or livestock monitoring. 2) We also built an embedded testbed in our lab that consists of System-on-Chips (SoCs) from NVIDIA, used for benchmarking various lightweight IoT and object detection protocols [3], [4] for use in surveillance in our experimental farms, e.g., in livestock monitoring [2]. We leverage heterogeneous edge devices, as follows, in our testbed: NVIDIA Jetson Nano, which has 4GB memory, and a 128-core Maxwell GPU; NVIDIA Jetson TX2, which has 8GB memory, and a 256-core Pascal GPU; NVIDIA Jetson Xavier NX, which has a 8GB memory, and a 384-core Volta GPU with 48 Tensor cores; and NVIDIA Jetson AGX Xavier, which has 32GB memory and 512-core Volta GPU.…”

Section: Introductionmentioning

confidence: 99%

ORPHEUS: Living Labs for End-to-End Data Infrastructures for Digital Agriculture

Wang¹,

Yi²,

Ratkus³

et al. 2021

Preprint

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IoT networks are being used to collect, analyze, and utilize sensor data. There are still some key requirements to leverage IoT networks in digital agriculture, e.g., design and deployment of energy saving and ruggedized sensor nodes (SN), reliable and long-range wireless network connectivity, end-to-end data collection pipelines for batch and streaming data. Thus, we introduce our living lab ORPHEUS and its design and implementation trajectory to showcase our orchestrated testbed of IoT sensors, data connectivity, database orchestration, and visualization dashboard. We deploy light-weight energy saving SNs in the field to collect data, using LoRa (Long Range wireless) to transmit data from the SNs to the Gateway node, upload all the data to the database server, and finally visualize the data. For future exploration, we also built a testbed of embedded devices using four different variants of NVIDIA Jetson development modules (Nano, TX2, Xavier NX, AGX Xavier) to benchmark the potential upgrade choices for SNs in ORPHEUS. Based on our deployment in multiple farms in a 3-county region around Purdue University, and on the Purdue University campus, we present analyses from our living lab deployment and additional components of the next-generation IoT farm.

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JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads

Cited by 8 publications

References 15 publications

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

ORPHEUS: Living Labs for End-to-End Data Infrastructures for Digital Agriculture

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