arXiv subjects

Jinting Liu

Publications and source records attributed to Jinting Liu.

2 recordsLinked to original sources

arXiv2026

Monaas: Mobile Node as a Service for TSCH-based Industrial IoT Networks

The Time-Slotted Channel Hopping (TSCH) mode of IEEE802.15.4 standard provides ultra high end-to-end reliability and low-power consumption for application in field of Industrial Internet of Things (IIoT). With the evolving of Industrial 4.0, dynamic and bursty tasks with varied Quality of Service (QoS); effective management and utilization of growing number of mobile equipments become two major challenges for network solutions. The existing TSCH-based networks lack of a system framework design to handle these challenges. In this paper, we propose a novel, service-oriented, and hierarchical IoT network architecture named Mobile Node as a Service (Monaas). Monaas aims to systematically manage and schedule mobile nodes as on-demand, elastic resources through a new architectural design and protocol mechanisms. Its core features include a hierarchical architecture to balance global coordination with local autonomy, task-driven scheduling for proactive resource allocation, and an on-demand mobile resource integration mechanism. The feasibility and potential of the Monaas link layer mechanisms are validated through implementation and performance evaluation on an nRF52840 hardware testbed, demonstrating its potential advantages in specific scenarios. On a physical nRF52840 testbed, Monaas consistently achieved a Task Completion Rate (TCR) above 98% for high-priority tasks under bursty traffic and link degradation, whereas all representative baselines (Static TSCH, 6TiSCH Minimal, OST, FTS-SDN) remained below 40%.Moreover, its on-demand mobile resource integration activated services in 1.2 s, at least 65% faster than SDN (3.5 s) and OST/6TiSCH (> 5.8 s).

cs.NI↗

arXiv2025

PAPER: Privacy-Preserving Convolutional Neural Networks using Low-Degree Polynomial Approximations and Structural Optimizations on Leveled FHE

Recent work using Fully Homomorphic Encryption (FHE) has made non-interactive privacy-preserving inference of deep Convolutional Neural Networks (CNNs) possible. However, the performance of these methods remains limited by their heavy reliance on bootstrapping, a costly FHE operation applied across multiple layers that severely slows inference. Moreover, they depend on high-degree polynomial approximations of non-linear activations, which increase multiplicative depth and reduce accuracy by 2-5% compared with plaintext ReLU models. In this work, we close the accuracy gap between FHE-based non-interactive CNNs and their plaintext counterparts while achieving faster inference than existing methods. We propose a quadratic polynomial approximation of ReLU that achieves the theoretical minimum multiplicative depth for non-linear activations, together with a penalty-based training strategy. We further introduce structural optimizations that reduce the required FHE levels in CNNs by a factor of five compared with prior work, allowing us to run deep CNN models under leveled FHE without bootstrapping. To further accelerate inference and recover accuracy typically lost with polynomial approximations, we introduce parameter clustering together with a joint strategy involving data layout and ensemble techniques. Experiments with VGG and ResNet models on CIFAR and Tiny-ImageNet datasets show that our approach achieves up to 4x faster private inference than prior work, with accuracy comparable to plaintext ReLU models.

cs.CR↗