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IoT/AIoT-Enabled Wireless Sensor Networks: Issues and Challenges

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 1040

Special Issue Editors


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Guest Editor
LIMOS, UMR 6158 CNRS, University Clermont Auvergne, 63173 Aubière, CEDEX, France
Interests: wireless sensor network; WSN; Internet of Things and Web of Things
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
UR TSCF, INRAE, Université Clermont Auvergne, 63178 Aubière, France
Interests: IoT; ontologies; sensor networks; data integration; e-agriculture; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent significant advances in IoT/AIoT technology ecosystem such as artificial intelligence (deep learning, generative AI, Tiny ML), nano-electronic material and technology (graphene, graphene/CMOS and 2D/3D integrated circuits), cloud data analysis (AI agent), wireless communication networks (5G, 5.5G, 6G, Wi-Fi 7), personal area network (Nearlink, BLE), satellite communication network (Eutelsat Oneweb, Starlink), MEMS/NEMS sensors and standard firmware (operating system, communication protocol) will allow to implement dedicated real-time ubiquitous IoT/AIoT platforms considering cost, reliability and performance to meet the requirements of diverse applications: Industry 4.0, autonomous vehicle, metaverse, digital twin, wearable sensors (SmartCare, smartwatch), etc. According to IoT Analytics, there were 18.8 billion IoT connected devices at the end of 2024 (a growth of 13% over 2023).

With the continued rapid advancements of the IoT/AIoT ecosystem delivering new IoT/AIoT capabilities for many new and complex applications that were previously inaccessible, we believe the number of AIoT/IoT devices will continue to grow significantly in the coming year. Thus, more and more smart AIoT/IoT platforms will become available and will transform our daily lives, especially those of the elderly (SmartCare, Smart Home) and the Industry 4.0 ecosystem. Finally, AIoT/IoT technology is considered as one of the key technologies for smart agriculture to address climate change and preserve biodiversity.

Dr. Kunmean Hou
Dr. Jean-Pierre Chanet
Guest Editors

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Keywords

  • low power and long range wireless communication network
  • satellite communication
  • nano-electronic new material and technologies such as 2D/3D field effect transistor for implementing low cost low power MCU IoT/AIoT node
  • firmware for IoT/AIoT
  • operating system
  • wireless communication protocol
  • static and mobile sink node
  • safety, reliability and security
  • AI agent for IoT/AIoT management
  • SmartCare, smart home, smart farming
  • MEMS/NEMS sensors
  • energy harvesting technologies

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Published Papers (1 paper)

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Research

31 pages, 2826 KB  
Article
HEOCP: Hybrid Energy-Optimized Clustering Protocol for WSNs Using Analytical Modeling and Deep Learning Integration
by Yen-Wu Ti, Rei-Heng Cheng, Songlin Wei and Chih-Min Yu
Sensors 2026, 26(4), 1188; https://doi.org/10.3390/s26041188 - 12 Feb 2026
Viewed by 546
Abstract
Wireless Sensor Networks (WSNs) play a pivotal role in Internet of Things (IoT) applications; however, their lifetime is fundamentally constrained by the limited energy of sensor nodes. This paper introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) that combines analytical modeling of radio energy [...] Read more.
Wireless Sensor Networks (WSNs) play a pivotal role in Internet of Things (IoT) applications; however, their lifetime is fundamentally constrained by the limited energy of sensor nodes. This paper introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) that combines analytical modeling of radio energy consumption with deep learning–assisted cluster-head (CH) selection. First, an analytical framework is developed to determine the distance-constrained CH eligibility region and the optimal number of clusters, thereby minimizing redundant transmissions and balancing energy consumption. Then, a genetic algorithm (GA) is used to determine the best cluster head configuration. These configurations are then trained by a ResNet-50 deep network and averaged to reduce noise, allowing for real-time cluster head prediction without repeatedly performing expensive heuristic optimization, resulting in more steady performance. Extensive simulations under various network scales demonstrate that HEOCP extends network lifetime by up to 60% compared with conventional LEACH and GA-based approaches, effectively delaying the first-node death and improving overall energy efficiency. Furthermore, the hybrid GA–ResNet framework exhibits high scalability and computational efficiency, making it suitable for large-scale IoT deployments. The results confirm that integrating analytical energy modeling with deep learning provides a powerful and sustainable paradigm for intelligent energy management in future IoT-enabled WSNs. Full article
(This article belongs to the Special Issue IoT/AIoT-Enabled Wireless Sensor Networks: Issues and Challenges)
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