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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Ismail Rakip Karas
Karabuk University
Computer Science & IT

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Privacy-preserving messaging for medical device networks Rachmad Andri Atmoko; Salnan Ratih Asriningtias; Akas Bagus Setiawan; Devasis Pradhan; Ismail Rakip Karas
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27794

Abstract

Internet of medical things (IoMT) deployments rely on lightweight messaging, but the message queuing telemetry transport (MQTT) protocol still exposes sensitive metadata through plaintext topic names and stable client identifiers. In healthcare settings, this visibility can reveal patient identity, location, and monitored condition even when payloads are encrypted. This paper presents a gateway-based privacy architecture that replaces semantic MQTT topics and client identifier (ClientIDs) with rotating pseudonyms managed by a topic and ID privacy manager (TPM). The design uses hash-based message authentication code using secure hash algorithm 256-bit (HMAC-SHA256) for pseudonym generation, advanced encryption standard-galois/counter mode (AES-GCM) for payload protection, and a short overlap phase that preserves message delivery during rotation without modifying the broker. Experiments from 5 to 10,000 patients show a consistent 5.0x increase in topic diversity, 2.50 μs per message cryptographic overhead, 0.063 ms maximum latency overhead, and zero packet loss. These results indicate that practical IoMT deployments can improve metadata privacy while still meeting real-time clinical communication requirements.
Optimized edge-aware frequency-guided filtering for robust image denoising Iman Elawady; Ahmet Reşit Kavsaoğlu; İsmail Rakıp Karaş
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27338

Abstract

The problem of denoising intrusion is still of great concern in computational imaging because of the trade-off between noise reduction and image structure and details recovery. This paper proposes an optimized edge-aware fast adaptive guided filter (E-FAGF) combining wavelet-domain decomposition, edge-awareness, and lightweight deep learning for efficient and effective denoising. The biorthogonal wavelet transform is employed to decompose noisy images into low- and high-frequency sub bands and an improved edge-attention map for selective high-frequency denoising. Regularization parameters are estimated pixel-wise by a compact convolutional neural network (CNN), allowing spatial-varying filtering to be done with multi-scale processing. The resultant E-FAGF consistently outperforms the state of the art on this dataset: on BSD500 for speckle and Gaussian noise (peak signal-to-noise ratio (PSNR) of 39.63 dB and 33.97 dB, respectively), and competitive performance for Poisson noise (30.84 dB) a large margin compared to the reference bilateral and non-local means. Our method maintains high structural similarity (up to 0.97 in structural similarity index measure (SSIM)), runs at 0.015 seconds per 512×512 image on graphics processing unit (GPU), and can be applied without dataset specific training. These results suggest the possibility of E-FAGF to achieve a balance between classical efficiency and learning-based adaptability, thereby forming a new scenario to combine fast and reliable image restoration for actual scenarios.
Co-Authors Ahmet Reşit Kavsaoğlu Devasis Pradhan Iman Elawady Rachmad Andri Atmoko Salnan Ratih Asriningtias, Salnan Ratih Setiawan, Akas Bagus