22 papers · ranked by Valyu relevance
Salam Hamdan, Moussa Ayyash, Sufyan Almajali
The rapid growth of the Internet of Things (IoT) applications and their interference with our daily life tasks have led to a large number of IoT devices and enormous sizes of IoT-generated data. The resources of IoT devices are limited; therefore, the processing and storing IoT data in these devices are inefficient.…
Haftay Gebreslasie Abreha, Mohammad Hayajneh, Mohamed Adel Serhani, Matteo Anedda + 1 more
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share…
Aleksandr Ometov, Oliver Liombe Molua, Mikhail Komarov, Jari Nurmi + 3 more
'Leandros Maglaras' 'Helge Janicke' 'Mohamed Amine Ferrag'] The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage…
Nancy A Angel, Dakshanamoorthy Ravindran, P M Durai Raj Vincent, Kathiravan Srinivasan + 5 more
Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of…
Fang Liu, Guoming Tang, Youhuizi Li, Zhiping Cai + 2 more
'Tongqing Zhou'] Abstract—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the…
Won-Suk Kim, Philip Broadbridge
Edge computing can deliver network services with low latency and real-time processing by providing cloud services at the network edge. Edge computing has a number of advantages such as low latency, locality, and network traffic distribution, but the associated resource management has become a significant challenge…
Silvana Trindade, Luiz F. Bittencourt, Nelson L. S. da Fonseca
Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we…
Andrea Hamm, Alexander Willner, Ina Schieferdecker
Edge Computing is a new distributed Cloud Computing paradigm in which computing and storage capabilities are pushed to the topological edge of a network. However, various standards and implementations are promoted by different initiatives. Lead by a reference architecture model for Edge Computing, current initiatives…
Gongfan Chen, Abdullah Alsharef, Edward Jaselskis, Hai Dong
Image classification is increasingly being utilized on construction sites to automate project monitoring, driven by advancements in reality-capture technologies and artificial intelligence (AI). Deploying real-time applications remains a challenge due to the limited computing resources available on-site, particularly…
Hyun-Jong Cha, Ho-Kyung Yang, You-Jin Song
It is expected that the number of devices connecting to the Internet-of-Things (IoT) will increase geometrically in the future, with improvement of their functions. Such devices may create a huge amount of data to be processed in a limited time. Under the IoT environment, data management should play the role of an…
Runyu Jin, Qirui Yang
—The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge devices, edge computing, on the other hand, has the potential to better…
Vivek Basavegowda Ramu
Edge computing can be defined as an emerging technology that uses cloud computing to leverage edge data centers to process, store, and analyze data close to the source. Traditional cloud computing architectures are not designed for latency-critical applications such as AI (Artificial Intelligence) and IoT (Internet Of…
Balqees Talal Hasan, Ali Kadhum Idrees
DRAFTAbstract Over the past few years, The idea of edge computing has seen substantial expansion in both academic and industrial circles. This computing approach has garnered attention due to its integrating role in advancing various state-of-the-art technologies such as Internet of Things (IoT) , 5G, artificial…
Blesson Varghese, Eyal de Lara, Aaron Yi Ding, Cheol-Ho Hong + 7 more
'Flavio Bonomi' 'Schahram Dustdar' 'Paul Harvey' 'Peter Hewkin' 'Weisong Shi' 'Mark Thiele' 'Peter A. Willis'] The initial concepts of edge computing were formulated more than a decade ago [1]. Although a nascent research area, it is generally understood that edge computing enables the (pre)processing of data closer to…
Hongqiang Sun, Rui Xu, Jianguo Luo, Han Cheng + 4 more
The use of unmanned aerial vehicles (UAVs) attracts significant attention, especially in fire emergency rescue, where UAVs serve as indispensable tools. In fire rescue scenarios, the rapid increase in the amount of data collected and transmitted by sensors poses significant challenges to traditional methods of data…
Poornima Mahadevappa, Raja Kumar Murugesan
In recent years, edge computing has emerged as a promising technology due to its unique feature of real-time computing and parallel processing. They provide computing and storage capability closer to the data source and bypass the distant links to the cloud. The edge data analytics process the ubiquitous data on the…
Ben Blamey, Salman Toor, Martin Dahlö, Håkan Wieslander + 6 more
This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines…
Matthias Becker, Milind Chabbi, Stefanie Warnat-Herresthal, Kathrin Klee + 9 more
Next generation sequencing (NGS) is the driving force behind precision medicine and is revolutionizing most, if not all, areas of the life sciences. Particularly when targeting the major common diseases, an exponential growth of NGS data is foreseen for the next decades. This enormous increase of NGS data and the need…
Kamal Choudhary
SLMat is a serverless, browser-based toolkit that revolutionizes computational materials science by offering a scalable and efficient alternative to traditional server-based platforms like Google Colab. By eliminating the need for server management and providing persistent setups, SLMat enhances productivity and…
Jacob M. Luber, Braden T. Tierney, Evan M. Cofer, Chirag J. Patel + 1 more
Across biology we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective, and scalable framework that uses linear programming (LP) to optimally bid on and…
David Mayer, Seth Russell, Melissa P. Wilson, Michael G. Kahn + 1 more
One of the challenges of teaching applied data science courses is managing individual students’ local computing environment. This is especially challenging when teaching massively open online courses (MOOCs) where students come from across the globe and have a variety of access to and types of computing systems. There…
Cláudia Brito, Pedro Ferreira, João Paulo
Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing unprecedented biological in-sights and new therapeutic applications. However, analyzing such large amounts of sensitive data raises key concerns regarding data privacy, specifically when the information is outsourced to…