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2020 | Book

IoT Digital Forensics Readiness in the Edge: A Roadmap for Acquiring Digital Evidences from Intelligent Smart Applications Read chapter Read first chapter

Edge Computing – EDGE 2020

4th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings

Editors: Ajay Katangur, Shih-Chun Lin, Dr. Jinpeng Wei, Shuhui Yang, Liang-Jie Zhang

Publisher: Springer International Publishing

Book Series : Lecture Notes in Computer Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book constitutes the proceedings of the International Conference on Edge Computing, EDGE 2020, held virtually as part of SCF 2020, in Honolulu, HI, USA, in September 2020.

The 7 full and 2 short papers presented in this volume were carefully reviewed and selected from 13 submissions.

The conference proceeding EDGE 2020 presents the latest fundamental advances in the state of the art and practice of edge computing, identify emerging research topics, and define the future of edge computing.

Table of Contents

Frontmatter
IoT Digital Forensics Readiness in the Edge: A Roadmap for Acquiring Digital Evidences from Intelligent Smart Applications
Abstract
Entering the era of the Internet of Things, the traditional Computer Forensics is no longer as trivial as decades ago with a rather limited pool of possible computer components. It has been demonstrated recently how the complexity and advancement of IoT are being used by malicious actors attack digital and physical infrastructures and systems. The investigative methodology, therefore, faces multiple challenges related to the fact that billions of interconnected devices generate tiny pieces of data that easily comprehend the Big Data paradigm. As a result, Computer Forensics is no longer a simple methodology of the straightforward process. In this paper, we study the complexity and readiness of community-accepted devices in a smart application towards assistance in criminal investigations. In particular, we present a clear methodology and involved tools related to Smart Applications. Relevant artefacts are discussed and analysed using the prism of the Digital Forensics Process. This research contributes towards increased awareness of the IoT Forensics in the Edge, corresponding challenges and opportunities.
Andrii Shalaginov, Asif Iqbal, Johannes Olegård
A Microservice-Based Industrial Control System Architecture Using Cloud and MEC
Abstract
Cloud computing has been adapted for various application areas. Several research projects are underway to migrate Industrial Control Systems (ICSs) to the public cloud. Some functions of ICSs require real-time processing that is difficult to migrate to the public cloud because network latency of the internet is unpredictable. Fog computing is a new computing paradigm that could address this latency issue. In particular, Multi-access Edge Computing (MEC) is a fog computing environment integrated with the 5G network, and therefore the real-time processing requirement of ICSs could be satisfied by using MEC. In this paper, we propose a microservice-based ICS architecture using the cloud and fog computing. In the architecture, each function of an ICS is implemented as a microservice and its execution locations are determined by an algorithm minimizing the total usage fee for cloud and fog computing while satisfying the real-time processing requirement. The proposed architecture and placement algorithm are evaluated by simulation under the scenario of a virtual power plant that manages distributed energy resources. The simulation result shows the proposed placement algorithm suppresses VM usage fee while satisfying the requirement of a real-time control function.
Yu Kaneko, Yuhei Yokoyama, Nobuyuki Monma, Yoshiki Terashima, Keiichi Teramoto, Takuya Kishimoto, Takeshi Saito
Edge Architecture for Dynamic Data Stream Analysis and Manipulation
Abstract
The exponential growth in IoT and connected devices featuring limited computational capabilities requires the delegation of computation tasks to cloud compute platforms. Edge compute tasks largely involve sending data from an edge compute device to a central location where data is processed and returned to the edge device as a response. Since most edge network infrastructure is restricted in its ability to dynamically delegate computation while retaining context, these events are commonly limited to a predefined task that the edge function is modeled to process and respond to. Edge functions traditionally handle isolated events or periodic updates, making them ill-suited for continuous tasks on streaming data. We propose a decentralized, massively scalable architecture of modular edge compute components which dynamically defines computation channels in the network, with emphasis on the ability to efficiently process data streams from a large amount of producers and support a large amount of consumers in real time. We test this architecture on real-world tasks, involving chaining of edge functions, context retention, and machine learning models on the edge, demonstrating its viability .
Orpaz Goldstein, Anant Shah, Derek Shiell, Mehrdad Arshad Rad, William Pressly, Majid Sarrafzadeh
fogcached: DRAM-NVM Hybrid Memory-Based KVS Server for Edge Computing
Abstract
With the widespread use of sensors in smart devices and robots, there is a growing expectation for edge computing that processes data not on distant cloud servers but also on or near interactive devices to store their data with low latency access. To satisfy these requirements, we consider a new edge computing system that consists of a hybrid main memory with a KVS (Key-Value-Store) server utilizing the DRAM and nonvolatile main memory (NVM). It provides large-capacity cache memory in a server, supporting high-speed processing and quick response for sensor nodes. However, since existing KVS servers are not designed for NVM, there are less satisfactory implementations that achieve low response time and high throughputs. We propose a novel hybrid KVS server that is designed and implemented on the Memcached distributed memory-caching system, which dynamically moves cached data between two types of memory devices according to access frequency in order to achieve a low latency compared to the existent approaches. We developed a Dual-LRU (Least Recently Used) structure for it. Evaluation was performed using a real machine equipped with NVM. The result showed the proposed method successfully reduced the response time and improves access throughputs.
Kouki Ozawa, Takahiro Hirofuchi, Ryousei Takano, Midori Sugaya
Small, Accurate, and Fast Re-ID on the Edge: The SAFR Approach
Abstract
We propose a Small, Accurate, and Fast Re-ID (SAFR) design for flexible vehicle re-id under a variety of compute environments such as cloud, mobile, edge, or embedded devices by only changing the re-id model backbone. Through best-fit design choices, feature extraction, training tricks, global attention, and local attention, we create a re-id model design that optimizes multi-dimensionally along model size, speed, & accuracy for deployment under various memory and compute constraints. We present several variations of our flexible SAFR model: SAFR-Large for cloud-type environments with large compute resources, SAFR-Small for mobile devices with some compute constraints, and SAFR-Micro for edge devices with severe memory & compute constraints.
SAFR-Large delivers state-of-the-art results with mAP 81.34 on the VeRi-776 vehicle re-id dataset (15% better than related work). SAFR-Small trades a 5.2% drop in performance (mAP 77.14 on VeRi-776) for over 60% model compression and 150% speedup. SAFR-Micro, at only 6 MB and 130 MFLOPS, trades 6.8% drop in accuracy (mAP 75.80 on VeRi-776) for 95% compression and 33x speedup compared to SAFR-Large .
Abhijit Suprem, Calton Pu, Joao Eduardo Ferreira
Study on the Digital Imaging Process to Improve the Resolution of Historical Artifacts Photos
Abstract
In this research, computational simulations and statistical analysis were performed with several modified mathematical functions to improve the resolution of digital images. Proposed alternative functions as new low pass filters were proved to save operation time and process. In the imaging process, when the domain of the proposed function over the frequency domain is narrow, it showed that the resolution of the final image was low due to the insufficient amount of frequency data from K-space. The main purpose of this research was to find a better low pass filter that would both improve the quality of the resolution of an image using mathematical, statistical and computational analysis. Result shows work time was decreased by a substantial amount of time to produce the final image. When the width of the function over the K space domain was narrow, low quality of image was produced due to the insufficient amount of frequency data from K-space. Higher quality of image was obtained using the proposed LPF with a certain width of domain. Also non-traditional function and its behavior of image statistics were studied in this analytic analysis.
Siwoo Kim, Andrew Kyung
Godec: An Open-Source Data Processing Framework for Deploying ML Data Flows in Edge-Computing Environments
Abstract
We present Godec, a C++-based framework that allows easy transition of complex machine learning (ML) data flows to edge-computing environments where common data processing frameworks do not apply. Godec allows for free mixing of technologies such as Kaldi, TensorFlow and custom modules, all wrapped into a single OS process, making it easy to deploy inference engines on constrained environments like Android, iOS or embedded Linux. Godec achieves this by connecting the components into an arbitrary graph specified by a simple JSON file during startup. Despite being multithreaded, results between runs are guaranteed identical, allowing for immediate transition from offline experiments to deployment. The source code is released under the MIT license https://​github.​com/​raytheonbbn/​Godec, with the authors’ hoping that the community will find it a useful tool to create their own components for it, in turn enabling others to mix and merge disparate technologies into applications of their own.
Ralf Meermeier, Le Zhang, Francis Keith, William Hartmann, Stavros Tsakalidis, Andrew Tabarez
Edge Storage Solution Based on Computational Object Storage
Abstract
Emerging computing/storage architecture provides new opportunities and requirements for multimedia data storage, especially at the edge (close to where the data is captured). Computational storage, defined as an architecture that conducts data processing at the storage layer so as to offload host processing or reduce data movement, allows raw data to be analyzed as the data are stored. As a consequence, the data to be stored may intrinsically carry richer metadata. Meanwhile object storage is a data storage architecture that organizes data into flexible-sized data containers, named objects. Combining object and computational storage, this paper described an edge data storage platform built on a representative computational object storage with content indexed object keys. The platform provides both computing and storage scalability for Edge applications while concurrently managing the richer metadata generated in a structured way to promote future information retrieval. Using video data as a sample use case, the concept of object key design is illustrated.
Lijuan Zhong
Preserving Patients’ Privacy in Medical IoT Using Blockchain
Abstract
Medical IoT is a collection of devices and applications that are connected to healthcare systems via the Internet. Wearable devices and body sensors are used to track individuals’ medical conditions. The collected data is processed, analyzed, and stored in the cloud platforms to provide healthcare services. The data does not only include personal information like users’ identity and location but also consists of sensitive information such as mental status, drug addiction, sexual orientation, and genetics. Therefore, preserving an individual’s privacy remains a huge challenge for IoT service providers. The existing techniques significantly reduce the originality of data which affects the application’s efficiency. Therefore, in this paper, we propose the idea of using blockchains and smart contract to preserve privacy while obtaining data usability.
Bandar Alamri, Ibrahim Tariq Javed, Tiziana Margaria
Survey of Edge Computing Based on a Generalized Framework and Some Recommendation
Abstract
The fast adoption and success of IoT and 5G related technology, accompanied by the ever-increasing critical demand for better QoS, revolutionized the paradigm shift from centralized cloud computing to some combination of distributed edge computing and traditional cloud computing. There are substantial researches and reviews on edge computing, and several industry-specific frameworks were proposed, but general purpose frameworks that could enable speedy utilization of millions of innovated business/IT services worldwide across the entire spectrum of the current computing paradigm is not yet properly addressed. We first proposed a generalized and service-oriented edge computing framework, based on a relatively complete survey of recent publications, then we conducted an in-depth analysis of selected works from both academia and industry aimed to access the maturity, and the gaps in this arena. Finally, we summarize the challenges and opportunities in edge computing, and we hope that this paper can inspire significant future improvements.
Yiwen Sun, Bihai Zhang, Min Luo
Correction to: Edge Computing – EDGE 2020
Ajay Katangur, Shih-Chun Lin, Jinpeng Wei, Shuhui Yang, Liang-Jie Zhang
Backmatter
Metadata
Title
Edge Computing – EDGE 2020
Editors
Ajay Katangur
Shih-Chun Lin
Dr. Jinpeng Wei
Shuhui Yang
Liang-Jie Zhang
Copyright Year
2020
Electronic ISBN
978-3-030-59824-2
Print ISBN
978-3-030-59823-5
DOI
https://doi.org/10.1007/978-3-030-59824-2

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