Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks

• URL: http://arxiv.org/abs/2302.05300v1
• Date: Sat, 14 Jan 2023 03:28:26 GMT
• Title: Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks
• Authors: Hrishikesh Dutta, Amit Kumar Bhuyan, and Subir Biswas
• Abstract summary: The paper introduces the use of RL and Multi Armed Bandit (MAB), a specific type of RL, for Medium Access Control (MAC) It then introduces a novel learning based protocol synthesis framework that addresses specific difficulties and limitations in medium access for both random access and time slotted networks. The ability of independent protocol learning by the nodes makes the system robust and adaptive to the changes in network and traffic conditions.
• Score: 1.462434043267217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article explores the concepts of online protocol synthesis using Reinforcement Learning (RL). The study is performed in the context of sensor and IoT networks with ultra low complexity wireless transceivers. The paper introduces the use of RL and Multi Armed Bandit (MAB), a specific type of RL, for Medium Access Control (MAC) under different network and traffic conditions. It then introduces a novel learning based protocol synthesis framework that addresses specific difficulties and limitations in medium access for both random access and time slotted networks. The mechanism does not rely on carrier sensing, network time-synchronization, collision detection, and other low level complex operations, thus making it ideal for ultra simple transceiver hardware used in resource constrained sensor and IoT networks. Additionally, the ability of independent protocol learning by the nodes makes the system robust and adaptive to the changes in network and traffic conditions. It is shown that the nodes can be trained to learn to avoid collisions, and to achieve network throughputs that are comparable to ALOHA based access protocols in sensor and IoT networks with simplest transceiver hardware. It is also shown that using RL, it is feasible to synthesize access protocols that can sustain network throughput at high traffic loads, which is not feasible in the ALOHA-based systems. The ability of the system to provide throughput fairness under network and traffic heterogeneities are also experimentally demonstrated.

Related papers

• RACH Traffic Prediction in Massive Machine Type Communications [5.416701003120508]
  This paper presents a machine learning-based framework tailored for forecasting bursty traffic in ALOHA networks. We develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics.
  arXiv  Detail & Related papers  (2024-05-08T17:28:07Z)
• 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)
• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• UAV-Aided Decentralized Learning over Mesh Networks [23.612400109629544]
  Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. Local connectivity of real world mesh networks, due to the limited communication range of its wireless nodes, undermines the efficiency of decentralized learning protocols. We propose an optimized UAV trajectory, that is defined as a sequence of waypoints that the UAV visits sequentially in order to transfer intelligence across sparsely connected group of users.
  arXiv  Detail & Related papers  (2022-03-02T10:39:40Z)
• FENXI: Deep-learning Traffic Analytics at the Edge [69.34903175081284]
  We present FENXI, a system to run complex analytics by leveraging TPU. FENXI decouples operations and traffic analytics which operates at different granularities. Our analysis shows that FENXI can sustain forwarding line rate traffic processing requiring only limited resources.
  arXiv  Detail & Related papers  (2021-05-25T08:02:44Z)
• Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers [2.6397379133308214]
  This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for MAC layer wireless protocols in IoT networks with low-complexity wireless transceivers. In this framework, the access protocols are first formulated as Markov Decision Processes (MDP) and then solved using RL. The paper demonstrates the performance of the learning paradigm and its abilities to make nodes adapt their optimal transmission strategies on the fly in response to various network dynamics.
  arXiv  Detail & Related papers  (2021-04-29T17:57:43Z)
• Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection [3.3659034873495632]
  Internet of Things (IoT) devices are constructed to build a huge communications network. These devices are insecure in reality, it means that the communications network are exposed by the attacker. In this paper, we propose SS-VTCN, a semi-supervised network for IoT multiple anomaly detection.
  arXiv  Detail & Related papers  (2021-04-05T08:51:24Z)
• Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis [2.6397379133308214]
  This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP), and solved using reinforcement learning with every network node acting as a distributed learning agent.
  arXiv  Detail & Related papers  (2021-02-02T17:13:37Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

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.