Electrosense: Open and Big Spectrum Data
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Abstract (arXiv)
While the radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions in various applications including cognitive radios, electrosmog monitoring, and law enforcement. We introduce Electrosense, an initiative that seeks a more efficient, safe and reliable monitoring of the electromagnetic space by improving the accessibility of spectrum data for the general public. A collaborative spectrum monitoring network is designed that monitors the spectrum at large scale with low-cost spectrum sensing nodes. The large set of data is stored and processed in a big data architecture and provided back to the community with an open spectrum data as a service model, that allows users to build diverse and novel applications with different requirements. We illustrate useful usage scenarios of the Electrosense data.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2017.
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Citing papers per year: 2022: 3; 2023: 5; 2024: 1; 2025: 1.
10 papers.
- 2023Fedstellar: A Platform for Decentralized Federated Learningcited by 14
- 2024Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Callscited by 8
- 2022Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensorscited by 5
- 2022RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTcited by 3
- 2025Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directionscited by 2
- 2022Self-supervised Learning for Clustering of Wireless Spectrum Activitycited by 1
- 2023Deep Feature Learning for Wireless Spectrum Datacited by 1
- 2023CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigationcited by 1
- 2023Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networkscited by 0
- 2023RansomAI: AI-powered Ransomware for Stealthy Encryptioncited by 0
- 2025Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions11 Mar 2025
- 2024Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls15 May 2024
- 2023CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation11 Aug 2023
- 2023Deep Feature Learning for Wireless Spectrum Data7 Aug 2023
- 2023RansomAI: AI-powered Ransomware for Stealthy Encryption27 Jun 2023
- 2023Fedstellar: A Platform for Decentralized Federated Learning16 Jun 2023
- 2023Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks23 Mar 2023
- 2022RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT30 Dec 2022
- 2022Self-supervised Learning for Clustering of Wireless Spectrum Activity22 Sep 2022
- 2022Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors31 Jan 2022