Leveraging Wireless Sensor Networks for Real-Time Monitoring and Control of Industrial Environments

• URL: http://arxiv.org/abs/2510.13820v1
• Date: Fri, 26 Sep 2025 04:07:44 GMT
• Title: Leveraging Wireless Sensor Networks for Real-Time Monitoring and Control of Industrial Environments
• Authors: Muhammad Junaid Asif, Shazia Saqib, Rana Fayyaz Ahmad, Hamza Khan,
• Abstract summary: We proposed a system based on NRF transceivers to establish a strong Wireless Sensor Network (WSN)<n>Key parameters, crucial for industrial setup such as temperature, humidity, soil moisture and fire detection, are monitored and displayed on an LCD screen.<n>Other than monitoring, there is an additional feature to remotely control these parameters by controlling the speed of DC motors through online commands.
• Score: 0.9099663022952497
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This research proposes an extensive technique for monitoring and controlling the industrial parameters using Internet of Things (IoT) technology based on wireless communication. We proposed a system based on NRF transceivers to establish a strong Wireless Sensor Network (WSN), enabling transfer of real-time data from multiple sensors to a central setup that is driven by ARDUINO microcontrollers. Different key parameters, crucial for industrial setup such as temperature, humidity, soil moisture and fire detection, are monitored and displayed on an LCD screen, enabling factory administration to oversee the industrial operations remotely over the internet. Our proposed system bypasses the need for physical presence for monitoring by addressing the shortcomings of conventional wired communication systems. Other than monitoring, there is an additional feature to remotely control these parameters by controlling the speed of DC motors through online commands. Given the rising incidence of industrial fires over the worldwide between 2020 and 2024 due to an array of hazards, this system with dual functionality boosts the overall operational efficiency and safety. This overall integration of IoT and Wireless Sensor Network (WSN) reduces the potential risks linked with physical monitoring, providing rapid responses in emergency scenarios, including the activation of firefighting equipment. The results show that innovations in wireless communication perform an integral part in industrial process automation and safety, paving the way to more intelligent and responsive operating environments. Overall, this study highlights the potential for change of IoT-enabled systems to revolutionize monitoring and control in a variety of industrial applications, resulting in increased productivity and safety.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications [58.796149594878585]
  This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks.<n>In the considered system, a central reader node actively queries a subset of neuromorphic sensor nodes (neuro-SNs) at each time frame.<n>The neuromorphic sensors are event-driven, producing spikes in correspondence to relevant changes in the monitored system.
  arXiv  Detail & Related papers  (2025-10-16T13:56:54Z)
• ICSLure: A Very High Interaction Honeynet for PLC-based Industrial Control Systems [0.0]
  Honeypots provide a means to detect and analyze such threats by emulating target systems and capturing attacker behavior.<n>Traditional ICS honeypots, often limited to software-based simulations of a single PLC, lack the realism required to engage adversaries.<n>In this work, we introduce a modular honeynet framework named ICSLure which integrates physical PLCs with sophisticated industrial protocols.
  arXiv  Detail & Related papers  (2025-09-04T10:18:30Z)
• Backscattering-Based Security in Wireless Power Transfer Applied to Battery-Free BLE Sensors [44.99833362998488]
  Integration of security and energy efficiency in Internet of Things systems remains a critical challenge.<n>This paper explores the scalability and protocol-agnostic nature of a backscattering-based security mechanism by integrating it into Bluetooth Low Energy battery-free Wireless Sensor Network.
  arXiv  Detail & Related papers  (2025-07-17T12:15:09Z)
• 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)
• A Comparative Study on Self-Organization in Wireless Sensor Networks [0.0]
  Wireless sensor networks (WSNs) enable efficient resource utilization and open doors to numerous applications.<n>WSNs are susceptible to environmental factors in their deployment areas and may suffer damage.<n>To address these challenges and adapt to resource constraints, WSN mechanisms must exhibit self-organizing capabilities.
  arXiv  Detail & Related papers  (2024-11-24T02:38:07Z)
• 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)
• 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)
• A Low-Cost Multi-Agent System for Physical Security in Smart Buildings [0.08999666725996971]
  Integrated Physical Security System (IP2S) is a multi-agent system capable of coordinating diverse Internet of Things (IoT) sensors and actuators. The proposed system was tested in a live case study that combined fire and intrusion detection in an industrial shop floor environment with four different sectors.
  arXiv  Detail & Related papers  (2022-09-01T22:20:39Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Assessing Wireless Sensing Potential with Large Intelligent Surfaces [42.23329726068689]
  This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an Industry 4.0 scenario. By treating an LIS as a radio image of the environment relying on the received signal power, we develop techniques to sense the environment. We derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution.
  arXiv  Detail & Related papers  (2020-11-16T15:50:22Z)
• A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting [39.85717881039926]
  This paper addresses the potential of communication-sensing integration of Large Intelligent Surfaces (LIS) in an Industry 4.0 scenario. By treating an LIS as a radio image of the environment, we develop sensing techniques that leverage the usage of computer vision combined with machine learning. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.
  arXiv  Detail & Related papers  (2020-06-11T16:15:50Z)

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.