An Intelligent Scheme for Uncertainty Management of Data Synopses Management in Pervasive Computing Applications

• URL: http://arxiv.org/abs/2007.12648v1
• Date: Fri, 24 Jul 2020 16:58:51 GMT
• Title: An Intelligent Scheme for Uncertainty Management of Data Synopses Management in Pervasive Computing Applications
• Authors: Kostas Kolomvatsos
• Abstract summary: Internet of Things (IoT) devices collect ambient data transferring them towards the Edge Computing (EC) and Cloud for further processing. EC nodes could become the hosts of distributed datasets where various processing activities take place. A critical issue for concluding this cooperative approach is the exchange of data synopses to have EC nodes informed about the data present in their peers. We propose n uncertainty driven model for the exchange of data synopses.
• Score: 9.289846887298852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pervasive computing applications deal with the incorporation of intelligent components around end users to facilitate their activities. Such applications can be provided upon the vast infrastructures of Internet of Things (IoT) and Edge Computing (EC). IoT devices collect ambient data transferring them towards the EC and Cloud for further processing. EC nodes could become the hosts of distributed datasets where various processing activities take place. The future of EC involves numerous nodes interacting with the IoT devices and themselves in a cooperative manner to realize the desired processing. A critical issue for concluding this cooperative approach is the exchange of data synopses to have EC nodes informed about the data present in their peers. Such knowledge will be useful for decision making related to the execution of processing activities. In this paper, we propose n uncertainty driven model for the exchange of data synopses. We argue that EC nodes should delay the exchange of synopses especially when no significant differences with historical values are present. Our mechanism adopts a Fuzzy Logic (FL) system to decide when there is a significant difference with the previous reported synopses to decide the exchange of the new one. Our scheme is capable of alleviating the network from numerous messages retrieved even for low fluctuations in synopses. We analytically describe our model and evaluate it through a large set of experiments. Our experimental evaluation targets to detect the efficiency of the approach based on the elimination of unnecessary messages while keeping immediately informed peer nodes for significant statistical changes in the distributed datasets.

Related papers

• Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks [3.4179091429029382]
  We study the choices of exchanging raw data, synthetic data, or (partial) model updates among devices. Across various scenarios that we considered, time-limited knowledge transfer efficiency can differ by up to 9.08%.
  arXiv  Detail & Related papers  (2024-06-16T03:46:23Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning [53.18060442931179]
  We propose the age of semantics (AoS) for measuring semantics freshness of status updates in a cooperative relay communication system. We derive an online deep actor-critic (DAC) learning scheme under the on-policy temporal difference learning framework. We then put forward a novel offline DAC scheme, which estimates the optimal control policy from a previously collected dataset.
  arXiv  Detail & Related papers  (2022-09-19T11:55:28Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• A Proactive Management Scheme for Data Synopses at the Edge [20.711789781518753]
  The Internet of Things (IoT) with numerous processing nodes present at the Edge Computing ecosystem opens up new pathways to support intelligent applications. Such applications can be provided upon humongous volumes of data collected by IoT devices being transferred to the edge nodes through the network. Various processing activities can be performed on the discussed data and multiple collaborative opportunities between EC nodes can facilitate the execution of the desired tasks. In this paper, we recommend the exchange of data synopses than real data between EC nodes to provide them with the necessary knowledge about peer nodes owning similar data.
  arXiv  Detail & Related papers  (2021-07-22T10:22:37Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Data Synopses Management based on a Deep Learning Model [14.180331276028662]
  We argue on the delivery of data synopses to EC nodes making them capable to take offloading decisions fully aligned with data present at peers. Our approach involves a Deep Learning model for learning the distribution of calculated synopses and estimate future trends.
  arXiv  Detail & Related papers  (2020-08-01T12:04:21Z)
• On the Use of Interpretable Machine Learning for the Management of Data Quality [13.075880857448059]
  We propose the use of interpretable machine learning to deliver the features that are important to be based for any data processing activity. Our aim is to secure data quality, at least, for those features that are detected as significant in the collected datasets.
  arXiv  Detail & Related papers  (2020-07-29T08:49:32Z)
• The Internet of Things as a Deep Neural Network [16.78220721167574]
  This article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN) We show how to learn the model parameters of the DNN and study the trade-off between the communication rate and the inference accuracy. Our findings have the potentiality to enable many new IoT data analysis applications generating large amount of measurements.
  arXiv  Detail & Related papers  (2020-03-23T20:36:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.