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Web spectrum monitoring systems based on crowdsourcing have recently gained popularity. These systems are however limited to applications of interest for governamental organizationsor telecom providers, and only provide aggregated information about spectrum statistics. Theresult is that there is a lack of interest for layman users to participate, which limits its widespreaddeployment. We present Electrosense+ which addresses this challenge and creates a general-purpose and open platform for spectrum monitoring using low-cost, embedded, and software-defined spectrum IoT sensors. Electrosense+ allows users to remotely decode specific parts ofthe radio spectrum. It builds on the centralized architecture of its predecessor, Electrosense, forcontrolling and monitoring the spectrum IoT sensors, but implements a real-time and peer-to-peercommunication system for scalable spectrum data decoding. We propose different mechanismsto incentivize the participation of users for deploying new sensors and keep them operational inthe Electrosense network. As a reward for the user, we propose an incentive accounting systembased on virtual tokens to encourage the participants to host IoT sensors. We present the newElectrosense+ system architecture and evaluate its performance at decoding various wireless sig-nals, including FM radio, AM radio, ADS-B, AIS, LTE, and ACARS.
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 monito
This paper describes the principles and implementation results of reinforcement learning algorithms on IoT devices for radio collision mitigation in ISM unlicensed bands. Learning is here used to improve both the IoT network capability to support a l
This article addresses the market-changing phenomenon of the Internet of Things (IoT), which relies on the underlying paradigm of machine-to-machine (M2M) communications to integrate a plethora of various sensors, actuators, and smart meters across a
Time-sensitive wireless networks are an important enabling building block for many emerging industrial Internet of Things (IoT) applications. Quick prototyping and evaluation of time-sensitive wireless technologies are desirable for R&D efforts. Soft
In this paper, a novel spectrum association approach for cognitive radio networks (CRNs) is proposed. Based on a measure of both inference and confidence as well as on a measure of quality-of-service, the association between secondary users (SUs) in