Analysis of the Degree of Congestion of WiFi, Bluetooth and ZigBee Communication Media

Burciu, Loredana-Maria; Fotescu, Radu-Petru; Constantinescu, Rodica-Claudia; Svasta, Paul; Moise, Mădălin Vasile

doi:10.1109/estc55720.2022.9939381

Cited by 4 publications

(1 citation statement)

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“…Traditional wireless communication technologies like WiFi and Bluetooth have been widely used in these fields. However, these technologies often encounter problems related to spectrum congestion, electromagnetic interference, costs, energy efficiency and limitations in the ability to connect a large number of devices simultaneously [1].…”

Section: Introduction 1optical Camera Communicationsmentioning

confidence: 99%

Cost-Effective Optical Wireless Sensor Networks: Enhancing Detection of Sub-Pixel Transmitters in Camera-Based Communications

Rodríguez-Yánez,

Guerra,

Rabadán

et al. 2024

Sensors

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In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera’s utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies.

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Section: Introduction 1optical Camera Communicationsmentioning

confidence: 99%