An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms

• URL: http://arxiv.org/abs/2509.10507v1
• Date: Mon, 01 Sep 2025 22:54:01 GMT
• Title: An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms
• Authors: Vadim Allayev, Mahbubur Rahman,
• Abstract summary: Internet of Intelligent Things (IoIT) combines the utility of Internet of Things (IoT) devices with the innovation of embedded AI algorithms.<n>Many impediments to IoIT are linked to the energy-efficient deployment of machine learning (ML)/deep learning (DL) models in embedded devices.<n>We propose a heterogeneous, decentralized sensing and monitoring IoIT peer-to-peer mesh network system model.
• Score: 0.17188280334580194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Internet of Intelligent Things (IoIT), an emerging field, combines the utility of Internet of Things (IoT) devices with the innovation of embedded AI algorithms. However, it does not come without challenges, and struggles regarding available computing resources, energy supply, and storage limitations. In particular, many impediments to IoIT are linked to the energy-efficient deployment of machine learning (ML)/deep learning (DL) models in embedded devices. Research has been conducted to design energy-efficient IoIT platforms, but these papers often focus on centralized systems, in which some central entity processes all the data and coordinates actions. This can be problematic, e.g., serve as bottleneck or lead to security concerns. In a decentralized system, nodes/devices would self-organize and make their own decisions. Therefore, to address such issues, we propose a heterogeneous, decentralized sensing and monitoring IoIT peer-to-peer mesh network system model. Nodes in the network will coordinate towards several optimization goals: reliability, energy efficiency, and latency. The system employs federated learning to train nodes in a distributed manner, metaheuristics to optimize task allocation and routing paths, and multi-objective optimization to balance conflicting performance goals.

Related papers

• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things [11.185300073739098]
  Traditional Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks.<n>This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments.
  arXiv  Detail & Related papers  (2025-04-09T18:52:15Z)
• 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)
• Decentralized Learning Made Easy with DecentralizePy [3.1848820580333737]
  Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance. We propose DecentralizePy, a distributed framework for decentralized ML, which allows for the emulation of large-scale learning networks in arbitrary topologies. We demonstrate the capabilities of DecentralizePy by deploying techniques such as sparsification and secure aggregation on top of several topologies.
  arXiv  Detail & Related papers  (2023-04-17T14:42:33Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• 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)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• Gateway Controller with Deep Sensing: Learning to be Autonomic in Intelligent Internet of Things [0.0]
  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.
  arXiv  Detail & Related papers  (2020-09-18T06:22:04Z)
• Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks [8.456633924613456]
  Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing.
  arXiv  Detail & Related papers  (2019-12-27T15:16:04Z)

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.