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AI/ML-Powered Intelligent IoT Systems with Smart Sensors for Next-Generation Edge Computing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2811

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Special Issue Editors

Dr. Junaid Shuja

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Guest Editor

Department of Computer Science, Southeast Missouri State University, Dempster Hall 268, One University Plaza, MS 5950, Cape Girardeau, MO 63701, USA
Interests: edge computing; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Mohamed Ibrahem

E-Mail Website
Guest Editor

School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA
Interests: cybersecurity in internet of things (IoT) and cyber–physical systems (CPS); applied cryptography; privacy-preserving artificial intelligence (AI); machine learning for cyber security; traffic analysis attacks and countermeasures; smart healthcare systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in edge-native artificial intelligence (AI) and machine learning (ML) have empowered IoT systems with intelligence, low latency, efficiency, and collaborative resource orchestration. The rapid adoption of AI/ML and next-generation edge computing is transforming the way Internet of Things (IoT) systems are deployed and managed. IoT networks continue to expand in scale and complexity in dynamic and resource-constrained environments. Therefore, there is demand for intelligent, autonomous, and adaptive frameworks for IoT resource management. Current innovation and research are on IoT systems that move beyond reactive operation toward predictive, self-optimizing, and resilient behavior. IoT should support real-time anomaly detection, intelligent traffic management, proactive fault diagnosis, and efficient resource utilization. When coupled with edge computing, these techniques bring intelligence closer to data sources, reducing latency, improving scalability, and enhancing privacy and reliability. Looking ahead, AI/ML-powered edge-enabled IoT systems are expected to play a crucial role in supporting emerging applications such as smart cities, healthcare, and transportation. This Special Issue seeks to bring together novel research contributions that advance intelligent IoT systems empowered by AI/ML and next-generation edge computing.

Topics of interest for publication include, but are not limited to, the following:

Scalable edge intelligence architectures for autonomous IoT;
Serverless architectures for event-driven sensor frameworks;
AI/ML-driven resource allocation and orchestration;
Explainable and trustworthy AI for healthcare IoT;
Digital twins and predictive intelligence for edge-enabled IoT applications.

Dr. Junaid Shuja
Dr. Mohamed Ibrahem
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

IoT
edge computing
AI
ML
edge cloud continuum

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Published Papers (2 papers)

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Research

36 pages, 12041 KB

Open AccessArticle

HydroNeuro: A Data-Efficient IoT Sensing and Edge-AI Framework for Real-Time Hydraulic Anomaly Detection

by Nasreddine Somaali, Mohamed Hayouni, Lokman Sboui and Fethi Choubani

Sensors 2026, 26(10), 3010; https://doi.org/10.3390/s26103010 - 10 May 2026

Viewed by 1709

Abstract

Reliable monitoring of hydraulic networks is essential for efficient and sustainable water management in agriculture. To address the growing need for intelligent, low-latency anomaly detection in such systems, we propose HydroNeuro, a domain-aware embedded framework that integrates hydraulic domain knowledge with data-driven neural inference for the real-time detection of leaks and obstructions. Rather than embedding physical equations directly into the learning objective, we leverage established hydraulic principles, including Bernoulli’s equation and the Darcy–Weisbach formulation, to structure the experimental design, interpret pressure–flow relationships, and ensure physical consistency of the learned representations. These principles confirm that pressure deviations induced by leaks or obstructions are causally explainable and measurable. We employ a fractional factorial design (FFD) to optimize valve activation combinations and sensor configurations during dataset acquisition, thereby reducing redundant experiments, water circulation, and energy consumption while limiting mechanical stress on system components. We deploy a lightweight neural network on an ESP32 microcontroller using TensorFlow Lite for Microcontrollers to enable energy-efficient, low-latency edge inference under severe hardware constraints. Our experimental validation on a laboratory-scale hydraulic testbed demonstrates anomaly detection accuracy exceeding 96%, with strong robustness under sensor noise and hydraulic perturbations. Compared to a multiple linear regression baseline, the proposed neural model reduces the prediction error from an RMSE of 0.58 to 0.12. By coupling physically consistent experimental modeling with embedded neural inference, HydroNeuro provides a scalable and practically deployable solution for autonomous hydraulic monitoring in precision irrigation and smart water distribution systems. Full article

(This article belongs to the Special Issue AI/ML-Powered Intelligent IoT Systems with Smart Sensors for Next-Generation Edge Computing)

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21 pages, 6540 KB

Open AccessArticle

HAPQ: A Hardware-Aware Pruning and Quantization Pipeline for Event-Based SNN Detection

by Zhengyinan Li and Jing Wu

Sensors 2026, 26(9), 2910; https://doi.org/10.3390/s26092910 - 6 May 2026

Viewed by 747

Abstract

Autonomous driving perception demands low latency, high temporal resolution, and stringent hardware efficiency. While event-based spiking neural networks (SNNs) offer bio-inspired sparse computation, their deployment on edge field-programmable gate arrays (FPGAs) is obstructed by irregular execution patterns and temporal state storage overhead. To address this, we propose HAPQ, a unified hardware-aware pruning and quantization pipeline for compact event-based object detection. Starting from an end-to-end adaptive sampling SNN detector (EAS-SNN), HAPQ conducts hardware-aware configuration search within discrete digital signal processor (DSP) and block RAM (BRAM) budgets, applies single-instruction-multiple-data (SIMD)-aligned structured pruning for computational regularity, and jointly quantizes synaptic weights and membrane potentials via a shift-friendly fixed-point recurrence. Evaluation on the Prophesee Gen1 dataset and an FPGA accelerator shows that HAPQ improves detection accuracy from 0.284 to 0.425 in mean average precision (

m A P_{50 : 95}

) and achieves 0.722

m A P_{50}

. Hardware implementation reveals a reduction in lookup table (LUT) usage to 1680, complete DSP elimination, and a maximum operating frequency of 920.81 MHz at 0.630 W. These results confirm that effective temporal SNN deployment requires joint optimization of model architecture, state precision, and hardware-aligned workload organization. Full article