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IoT
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IoT and Distributed Computing
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IoT and Distributed Computing

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 4251

Special Issue Editors

Dr. Xianzhong Ding

E-Mail Website
Guest Editor
Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Interests: IoT; distributed computing; cyber-physical systems; reinforcement learning
Dr. Kang Yang

E-Mail Website
Guest Editor
Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
Interests: AI-driven wireless networking and sensing systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of Internet of Things (IoT) and distributed computing is reshaping modern smart systems by enabling large-scale data collection, real-time analytics, and efficient resource management. With the growing adoption of edge computing, cloud-integrated IoT architectures, and AI-driven decision-making, it is critical to address key challenges such as scalability, security, network reliability, and energy efficiency.

This Special Issue aims to bring together cutting-edge research and advancements in IoT and distributed computing, focusing on novel architectures, intelligent optimization techniques, and applications in smart grids, autonomous systems, industrial automation, and large-scale cyber-physical systems. We invite researchers and practitioners to contribute original research articles and reviews covering, but not limited to, the following topics:

  • Scalable IoT Architectures and Edge-Cloud Computing;
  • AI-Driven Resource Allocation in Distributed IoT Networks;
  • Cybersecurity and Privacy Challenges in IoT Systems;
  • High-Performance Computing Techniques for Large-Scale IoT;
  • Energy-Efficient Communication and Sensing Technologies;
  • IoT Applications in Smart Cities, Transportation, and Healthcare;
  • Reinforcement Learning-Based Optimization in IoT Environments;
  • Real-World Deployment and Case Studies of IoT Systems.

This collection will provide valuable insights into how distributed computing and intelligent decision-making can enhance IoT infrastructure, optimize network performance, and support emerging applications across different domains. We look forward to your contributions to advance this field.

Dr. Xianzhong Ding
Dr. Kang Yang
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. IoT is an international peer-reviewed open access quarterly 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 1400 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
  • distributed computing
  • edge computing
  • cyber-physical systems
  • AI-driven IoT
  • smart systems
  • high-performance computing
  • network security
  • energy-efficient IoT
  • reinforcement learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 601 KB  
Article
Multi-Flow Complex Event Optimization in the Edge: A Smart Street Scenario
by Halit Uyanık and Tolga Ovatman
IoT 2025, 6(4), 72; https://doi.org/10.3390/iot6040072 - 21 Nov 2025
Viewed by 207
Abstract
Internet of Things (IoT) devices can be used to provide safety, security, and other services that ensure that smart systems work as intended. However, the increasing complexity of the tasks is increasing the potential of performance loss when limited resources are not utilized [...] Read more.
Internet of Things (IoT) devices can be used to provide safety, security, and other services that ensure that smart systems work as intended. However, the increasing complexity of the tasks is increasing the potential of performance loss when limited resources are not utilized appropriately. Distributed complex event processing (CEP) applications can be used to execute multiple unique tasks on sensor data. Since these operations can require a variety of data from multiple sensors across separate task steps, non-optimal code and data management can lead to increased processing delays. In this study, a mathematical model for optimizing critical path performance across multiple independent CEP flows is proposed. The model optimally assigns both where codes are executed at, as well as where their respective data should be placed at. The proposed solution is implemented within an open source library with the inclusion of operator placement heuristics from the literature. Approaches are tested within a realistic smart-street scenario. Consumer delays, algorithm runtimes, and delivery ratios within different time windows are reported. The results indicate that the proposed approach can reduce the delivery times for the critical CEP paths better than the heuristic solutions, with the downside of increased optimization runtimes. Full article
(This article belongs to the Special Issue IoT and Distributed Computing)
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26 pages, 1043 KB  
Article
Centralized Two-Tiered Tree-Based Intrusion-Detection System (C2T-IDS)
by Hisham Abdul Karim Yassine, Mohammed El Saleh, Bilal Ezzeddine Nakhal and Abdallah El Chakik
IoT 2025, 6(4), 67; https://doi.org/10.3390/iot6040067 - 5 Nov 2025
Viewed by 636
Abstract
The exponential growth of Internet of Things (IoT) devices introduces significant security challenges due to their resource constraints and diverse attack surfaces. To address these issues, this paper proposes the Centralized Two-Tiered Tree-Based Intrusion Detection System (C2T-IDS), a lightweight framework designed for efficient [...] Read more.
The exponential growth of Internet of Things (IoT) devices introduces significant security challenges due to their resource constraints and diverse attack surfaces. To address these issues, this paper proposes the Centralized Two-Tiered Tree-Based Intrusion Detection System (C2T-IDS), a lightweight framework designed for efficient and scalable threat detection in IoT networks. The system employs a hybrid edge-centralized architecture, where the first tier, deployed on edge gateways, performs real-time binary classification to detect anomalous traffic using optimized tree-based models. The second tier, hosted on a centralized server, conducts detailed multi-class classification to diagnose specific attack types using advanced ensemble methods. Evaluated on the realistic CIC-IoT-2023 dataset, C2T-IDS achieves a Macro F1-Score of up to 0.94 in detection and 0.80 in diagnosis, outperforming direct multi-class classification by 5–15%. With inference times as low as 6 milliseconds on edge devices, the framework demonstrates a practical balance between accuracy, efficiency, and deployability, offering a robust solution for securing resource-constrained IoT environments. Full article
(This article belongs to the Special Issue IoT and Distributed Computing)
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24 pages, 1446 KB  
Article
MQTT Broker Architectural Enhancements for High-Performance P2P Messaging: TBMQ Scalability and Reliability in Distributed IoT Systems
by Dmytro Shvaika, Andrii Shvaika and Volodymyr Artemchuk
IoT 2025, 6(3), 34; https://doi.org/10.3390/iot6030034 - 23 Jun 2025
Cited by 1 | Viewed by 2667
Abstract
The Message Queuing Telemetry Transport (MQTT) protocol remains a key enabler for lightweight and low-latency messaging in Internet of Things (IoT) applications. However, traditional broker implementations often struggle with the demands of large-scale point-to-point (P2P) communication. This paper presents a performance and architectural [...] Read more.
The Message Queuing Telemetry Transport (MQTT) protocol remains a key enabler for lightweight and low-latency messaging in Internet of Things (IoT) applications. However, traditional broker implementations often struggle with the demands of large-scale point-to-point (P2P) communication. This paper presents a performance and architectural evaluation of TBMQ, an open source MQTT broker designed to support reliable P2P messaging at scale. The broker employs Redis Cluster for session persistence and Apache Kafka for message routing. Additional optimizations include asynchronous Redis access via Lettuce and Lua-based atomic operations. Stepwise load testing was performed using Kubernetes-based deployments on Amazon EKS, progressively increasing message rates to 1 million messages per second (msg/s). The results demonstrate that TBMQ achieves linear scalability and stable latency as the load increases. It reaches an average throughput of 8900 msg/s per CPU core, while maintaining end-to-end delivery latency within two-digit millisecond bounds. These findings confirm that TBMQ’s architecture provides an effective foundation for reliable, high-throughput messaging in distributed IoT systems. Full article
(This article belongs to the Special Issue IoT and Distributed Computing)
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