A Distributed Data Allocation Scheme for Autonomous Nodes

Kolomvatsos, Kostas; Oikonomou, Panagiotis; Koziri, Maria; Loukopoulos, Thanasis

doi:10.1109/smartworld.2018.00282

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…The EGX Edge Computing Platform 67 is proposed by NVIDIA to deliver accelerated AI computing to the edge with an easy-to-deploy Cloud native software stack, a range of validated servers and devices, and a vast ecosystem of partners who offer EGX through their products and services. The EGX hardware portfolio starts with the power-efficient NVIDIA Jetson Family, which includes the small but mighty Jetson Nano and Xavier NX providing between 0.5 to 21 trillion operations per second (TOPS) for tasks such as image recognition and sensor fusion.…”

Section: Hardware Requirementsmentioning

confidence: 99%

“…Data and tasks allocation/placement at the EC/EM is a significant research field. A number of research activities try to detect the optimal location where data and tasks are distributed (e.g., [67]). The aim is to eliminate the time for delivering the final analytics while keeping maximized the 'matching' between requests and the available data.…”

Section: Joint Data and Tasks Allocationmentioning

confidence: 99%

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On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

et al. 2021

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Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain.

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Section: Hardware Requirementsmentioning

confidence: 99%

Section: Joint Data and Tasks Allocationmentioning

confidence: 99%

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

et al. 2021

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“…The data allocation problem is critical if we want to achieve a fast solution to administrate data streams. Data allocation methods should be combined with outliers detection models to secure the minimum acceptable data quality before any processing takes place [31]. Data replication mainly deals with the minimization of the latency in the provision of responses and the support of fault tolerant systems.…”

Section: Related Workmentioning

confidence: 99%

Landmark based Outliers Detection in Pervasive Applications

Kolomvatsos

Anagnostopoulos

2021

2021 12th International Conference on Information and Communication Systems (ICICS)

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The combination of the Internet of Things and the Edge Computing gives many opportunities to support innovative applications close to end users. Numerous devices present in both infrastructures can collect data upon which various processing activities can be performed. However, the quality of the outcomes may be jeopardized by the presence of outliers. In this paper, we argue on a novel model for outliers detection by elaborating on a 'soft' approach. Our mechanism is built upon the concepts of candidate and confirmed outliers. Any data object that deviates from the population is confirmed as an outlier only after the study of its sequence of magnitude values as new data are incorporated into our decision making model. We adopt the combination of a sliding with a landmark window model when a candidate outlier is detected to expand the sequence of data objects taken into consideration. The proposed model is fast and efficient as exposed by our experimental evaluation while a comparative assessment reveals its pros and cons.

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“…DDAS4AN [43] is an approach defining an scheme for selecting the data that should be stored locally in the IoT nodes. With this scheme, nodes collect data from their environment in the form of multidimensional streams and decide, in real time, which data should be stored locally for further processing.…”

Section: A Fully Distributed Data Storagementioning

confidence: 99%

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

2019

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Sensor/Actuator devices are currently being massively adopted, often as nodes of larger sensor networks. These sensor networks are typically dedicated to context acquisition (e.g., get temperature) as well as providing acting services (e.g., open the blinds). However, regarding their own data storage, data is usually sent to Fog/Cloud servers. Fog/Cloud storage solutions provide several advantages over sensor network storage solutions, but also some drawbacks. For instance, in Cloud environments, privacy and legal issues may appear, while in Fog, additional costly hardware must be purchased and maintained, at least a server with redundant storage or many servers when distributed data storage is required. Nowadays, sensor nodes count in thousands around us, and they have significantly increased their storage and computational capabilities over the past few years. Therefore, traditional Fog/Cloud storage solutions could be combined or even replaced by Mist/Edge storage solutions for many use cases. A principal contribution of this paper is a novel data distribution and replication storage solution for wireless sensor networks, the first to consider sensor node heterogeneity to find the optimal storage replication according to node capabilities. The solution has been carefully planned and implemented to run even in very low-end microcontrollers, that lives in many of our surrounding smart devices. Other contributions include data comparing Mist/Edge and Amazon S3 regular storage, showing that there remains plenty of room for research into Mist/Edge storage, as well as into the industry itself.INDEX TERMS Mist computing, edge computing, distributed storage, sensor networks.

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A Distributed Data Allocation Scheme for Autonomous Nodes

Cited by 6 publications

References 23 publications

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

On the Use of Intelligent Models towards Meeting the Challenges of the Edge Mesh

Landmark based Outliers Detection in Pervasive Applications

Mist and Edge Storage: Fair Storage Distribution in Sensor Networks

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