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Context 1
... The evaluation parameters of transmission effect includes RSSI (Received Signal Strength Indicator), PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity index), and transmission time. The original picture with 200 × 150 pixels is shown in Fig. 4. The actual placement of the transmitting node and the receiving node are shown in Fig. 5. Fig. 6(a) shows the result after transmission using the jpg format. Fig. 6(b) shows the result of the transmission using Webp and then Base64 encoding. PSNR is used to evaluate image quality. The larger the PSNR, the smaller the image distortion. However, it is pointed out in the research report that PSNR is different from human perception [14]. ...
Context 2
... Signal Strength Indicator), PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity index), and transmission time. The original picture with 200 × 150 pixels is shown in Fig. 4. The actual placement of the transmitting node and the receiving node are shown in Fig. 5. Fig. 6(a) shows the result after transmission using the jpg format. Fig. 6(b) shows the result of the transmission using Webp and then Base64 encoding. PSNR is used to evaluate image quality. The larger the PSNR, the smaller the image distortion. However, it is pointed out in the research report that PSNR is different from human perception [14]. SSIM is used for measuring the similarity between two images and ...
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... To ensure compatibility and ease of transmission, the payload is converted into a Base64 format. Although Base64 encoding will increase the character length by, approximately, 1/3, we choose it as the transmission file format because it provides a textual representation of binary data, allowing for reliable and straightforward transfer through MQTT [14]. This encoding format simplifies the handling of non-textual data, such as images, in the messaging protocol. ...
The Internet of Things (IoT) has revolutionized how objects and devices interact, creating new possibilities for seamless connectivity and data exchange. This paper presents a unique and effective method for transferring images via the Message Queuing Telemetry Transport (MQTT) protocol in an encrypted manner. The image is split into multiple messages, with each carrying a segment of the image, and employ top-notch encryption techniques to ensure secure communication. Applying this process, the message payload is split into smaller segments, and consequently, it minimizes the network bandwidth impact while mitigating potential of packet loss or latency issues. Furthermore, by applying encryption techniques, we guarantee the confidentiality and integrity of the image data during transmission, safeguarding against unauthorized access or tampering. Our experiments in a real-world scenario involving remote indicator panels with LEDs verify the effectiveness of our approach. By using our proposed method, we successfully transmit images over MQTT, achieving secure and reliable data transfer while ensuring the integrity of the image content. Our results demonstrate the feasibility and effectiveness of the proposed approach for image transfer in IoT applications. The combination of message segmentation, MQTT protocol, and encryption techniques offers a practical solution for transmitting images in resource-constrained IoT networks while maintaining data security. This approach can be applied in different applications.
... A first attempt of transmitting images via LoRa is described in [6]: in this work, JPEG images are relayed by exploiting a Teensy 3.2 module connected to a LoRa module and provided with uCamII camera. A similar solution, though based on Arduino platforms, is also proposed in [7] and [8], while Wei et al. [9] compare the performances related to the transmission of JPEG files with the ones achievable exploiting the WebP format (i.e., the one employed in the solution presented in this paper). ...
... The resulting images from multi-pig detection and tracking are encoded by Base64 to represent binary image data in an ASCII string format [29]. An IoT alerting client sends the Base64-encoded images to an IoT server. ...
The present work proposes an encoder for image transmission via LoRa communication modules. These enable long-range, low-power transmission schemes and are ideal for monitoring in places with no mobile network connectivity. Nonetheless, this technology has a low transmission bitrate, which limits its use to high bandwidth applications. The state-of-the-art has numerous image encoders, but few achieve an adequate balance between image quality, compression, sequential decoding, and computational complexity. The proposed encoder uses the YCoCg color model and chromatic subsampling followed by wavelet subband decomposition, which extracts relevant subbands in the image to then reconstruct it sequentially. Each subband is quantized independently and then enters an adaptive entropic encoder. This encoder is compared to the JPEG2000 encoder using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) quality metrics. Results show that the proposal obtains a reconstructed image quality close to that of JPEG2000 with a higher compression rate. Moreover, it improves the transmission time of images through a LoRa link by 99.09%.
