Gateway Controller with Deep Sensing: Learning to be Autonomic in
Intelligent Internet of Things
- URL: http://arxiv.org/abs/2009.08646v1
- Date: Fri, 18 Sep 2020 06:22:04 GMT
- Title: Gateway Controller with Deep Sensing: Learning to be Autonomic in
Intelligent Internet of Things
- Authors: Rahim Rahmani and Ramin Firouzi
- Abstract summary: The Internet of Things will revolutionize the Future Internet through ubiquitous sensing.
One of the challenges of having the hundreds of billions of devices that are estimated to be deployed would be rise of an enormous amount of data.
This paper presents an approach as a controller solution and designed specifically for autonomous management, connectivity and data interoperability in an IoT gateway.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The Internet of Things(IoT) will revolutionize the Future Internet through
ubiquitous sensing. One of the challenges of having the hundreds of billions of
devices that are estimated to be deployed would be rise of an enormous amount
of data, along with the devices ability to manage. This paper presents an
approach as a controller solution and designed specifically for autonomous
management, connectivity and data interoperability in an IoT gateway. The
approach supports distributed IoT nodes with both management and data
interoperability with other cloud-based solutions. The concept further allows
gateways to easily collect and process interoperability of data from IoT
devices. We demonstrated the feasibility of the approach and evaluate its
advantages regarding deep sensing and autonomous enabled gateway as an edge
computational intelligence.
Related papers
- Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey [82.84057882105931]
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV)
We present the fundamentals of GAI, MoE, and their interplay applications in IoV.
We discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation.
arXiv Detail & Related papers (2024-04-25T06:22:21Z) - 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) - A Novel IoT Trust Model Leveraging Fully Distributed Behavioral
Fingerprinting and Secure Delegation [3.10770247120758]
Internet of Things (IoT) solutions are experimenting a booming demand to make data collection and processing easier.
The higher the number of new capabilities and services provided in an autonomous way, the wider the attack surface that exposes users to data hacking and lost.
In this paper, we try to provide a contribution in this setting, tackling the non-trivial issues of equipping smart things with a strategy to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it.
arXiv Detail & Related papers (2023-10-02T07:45:49Z) - Towards Artificial General Intelligence (AGI) in the Internet of Things
(IoT): Opportunities and Challenges [55.82853124625841]
Artificial General Intelligence (AGI) possesses the capacity to comprehend, learn, and execute tasks with human cognitive abilities.
This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the Internet of Things.
The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education.
arXiv Detail & Related papers (2023-09-14T05:43:36Z) - 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) - IoT-based Route Recommendation for an Intelligent Waste Management
System [61.04795047897888]
This work proposes an intelligent approach to route recommendation in an IoT-enabled waste management system given spatial constraints.
Our solution is based on a multiple-level decision-making process in which bins' status and coordinates are taken into account.
arXiv Detail & Related papers (2022-01-01T12:36:22Z) - Computational Intelligence and Deep Learning for Next-Generation
Edge-Enabled Industrial IoT [51.68933585002123]
We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.
In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework.
In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
arXiv Detail & Related papers (2021-10-28T08:14:57Z) - An Automated Data Engineering Pipeline for Anomaly Detection of IoT
Sensor Data [0.0]
System of Chip technology, Internet of Things (IoT), cloud computing, and artificial intelligence has brought more possibilities of improving and solving the current problems.
Data analytics and the use of machine learning/deep learning makes it possible to learn the underlying patterns and make decisions based on what was learned from massive data generated from IoT sensors.
Process involves the use of IoT sensors, Raspberry Pis, Amazon Web Services (AWS) and multiple machine learning techniques with the intent to identify anomalous cases for the smart home security system.
arXiv Detail & Related papers (2021-09-28T15:57:29Z) - Federated Learning for Internet of Things: A Federated Learning
Framework for On-device Anomaly Data Detection [10.232121085973782]
We build a FedIoT platform that contains a synthesized dataset using N-BaIoT, FedDetect algorithm, and a system design for IoT devices.
In a network of realistic IoT devices (PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance.
arXiv Detail & Related papers (2021-06-15T08:53:42Z) - Pervasive AI for IoT Applications: Resource-efficient Distributed
Artificial Intelligence [45.076180487387575]
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services.
This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams.
The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
arXiv Detail & Related papers (2021-05-04T23:42:06Z) - Federated Machine Learning for Intelligent IoT via Reconfigurable
Intelligent Surface [35.64178319119883]
We develop an over-the-air based communication-efficient federated machine learning framework for intelligent IoT networks.
We exploit the waveform superposition property of a multi-access channel to reduce the model aggregation error.
arXiv Detail & Related papers (2020-04-13T09:48:04Z)
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.