Edge-Based Predictive Data Reduction for Smart Agriculture: A Lightweight Approach to Efficient IoT Communication

• URL: http://arxiv.org/abs/2511.19103v1
• Date: Mon, 24 Nov 2025 13:37:33 GMT
• Title: Edge-Based Predictive Data Reduction for Smart Agriculture: A Lightweight Approach to Efficient IoT Communication
• Authors: Dora Krekovic, Mario Kusek, Ivana Podnar Zarko, Danh Le-Phuoc,
• Abstract summary: The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing.<n>This is particularly problematic in resource-constrained and remote environments where bandwidth is limited.<n>We propose an analytical prediction algorithm designed for edge computing environments and validated through simulation.
• Score: 1.2249546377051437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of IoT devices has led to an enormous amount of sensor data that requires transmission to cloud servers for processing, resulting in excessive network congestion, increased latency and high energy consumption. This is particularly problematic in resource-constrained and remote environments where bandwidth is limited, and battery-dependent devices further emphasize the problem. Moreover, in domains such as agriculture, consecutive sensor readings often have minimal variation, making continuous data transmission inefficient and unnecessarily resource intensive. To overcome these challenges, we propose an analytical prediction algorithm designed for edge computing environments and validated through simulation. The proposed solution utilizes a predictive filter at the network edge that forecasts the next sensor data point and triggers data transmission only when the deviation from the predicted value exceeds a predefined tolerance. A complementary cloud-based model ensures data integrity and overall system consistency. This dual-model strategy effectively reduces communication overhead and demonstrates potential for improving energy efficiency by minimizing redundant transmissions. In addition to reducing communication load, our approach leverages both in situ and satellite observations from the same locations to enhance model robustness. It also supports cross-site generalization, enabling models trained in one region to be effectively deployed elsewhere without retraining. This makes our solution highly scalable, energy-aware, and well-suited for optimizing sensor data transmission in remote and bandwidth-constrained IoT environments.

Related papers

• Data-driven Modality Fusion: An AI-enabled Framework for Large-Scale Sensor Network Management [0.49998148477760973]
  This paper introduces a novel sensing paradigm called Data-driven Modality Fusion (DMF)<n>By leveraging correlations between timeseries data from different sensing modalities, the proposed DMF approach reduces the number of physical sensors required for monitoring.<n>The framework relocates computational complexity from the edge devices to the core, ensuring that resource-constrained IoT devices are not burdened with intensive processing tasks.
  arXiv  Detail & Related papers  (2025-02-07T14:00:04Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse [56.384390765357004]
  We propose an integrated federated split learning and hyperdimensional computing framework for emerging foundation models. This novel approach reduces communication costs, computation load, and privacy risks, making it suitable for resource-constrained edge devices in the Metaverse.
  arXiv  Detail & Related papers  (2024-08-26T17:03:14Z)
• ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networks [20.953587995374168]
  We propose a Deep Learning-based approach that combines Convolutional Neural Networks (CNNs) and customized Vision Transformers (ViTs) Our method capitalizes on the synergistic strengths of CNNs and ViTs to extract features from time-series multimodal data. Our results show that the proposed approach achieves high accuracy and outperforms state-of-the-art solutions, achieving more than $95%$ accurate predictions.
  arXiv  Detail & Related papers  (2024-06-27T01:38:09Z)
• Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning [19.715387333728152]
  Internet of Things (IoT) devices are often power-constrained and utilize wireless communication schemes with limited bandwidth. We formulate the communication planning problem of IoT sensors that track the state of the environment. We propose a multi-agent reinforcement learning (MARL) method to find the optimal communication policies for each sensor.
  arXiv  Detail & Related papers  (2023-08-19T22:59:09Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments [2.7776688429637466]
  FLARE is a lightweight dual-scheduler FL framework that conditionally transfers training data and deploys models between edge and sensor endpoints. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods. It can successfully detect concept drift reactively with at least a 16x reduction in latency.
  arXiv  Detail & Related papers  (2023-05-15T10:09:07Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)

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.