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Future Internet
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Internet of Things Technology and Service Computing
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Internet of Things Technology and Service Computing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3524

Special Issue Editors

Dr. Yang Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: service-oriented computing; Internet of Things; service security and privacy
Prof. Dr. Michael Sheng

E-Mail Website
Guest Editor
School of Computing, Faculty of Science & Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: web of things; internet of things; big data analytics; web science; service-oriented computing; pervasive computing; sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is a network of connected devices such as sensors, electronic equipment, cameras, and many more. Due to the advancements in the field of AI and Machine Learning, the scope of IoT has impressively increased. The Special Issue of Internet of Things Technology and Service Computing aims to establish a collection focusing on IoT-based services and applications, featuring intelligent IoT modeling, IoT protocols, Machine Learning-based IoT data management, IoT service recommendation and composition, and AI-based IoT service systems. All topics relevant to IoT and IoT-based services are of interest. This SI will organize peer-reviewed papers in seven research scope categories:

1. IoT Architectures and Protocols

  • Design and evaluation of IoT architectures;
  • Standardization efforts and protocols for IoT communication (e.g., MQTT, CoAP, etc.);
  • Interoperability challenges and solutions.

2. IoT Networking Technologies:

  • Semantic publish/subscribe networks;
  • Low-power wide-area networks (LPWANs);
  • 6G and beyond for IoT connectivity.

3. IoT Data Management and Analytics:

  • Data collection, storage, and processing in IoT environments;
  • AI analytics for IoT-generated data;
  • Real-time analytics and stream processing.

4. Edge Computing in IoT:

  • Edge computing architectures and frameworks;
  • Edge analytics and decision making;
  • Resource management and task offloading.

5. Security and Privacy in IoT:

  • Authentication and access control mechanisms;
  • Encryption techniques for securing IoT data;
  • Privacy-preserving data aggregation and sharing.

6. Generative AI in IoT Services

  • LLM and Multimodal Models enhanced IoT service discovery, selection, and composition;
  • LLM-driven IoT service recommendation: algorithms, models, and performance;
  • IoT Service Reasoning with Generative AI Approaches;
  • Generative AI as a IoT Service (GaaIS);
  • Reinforcement Learning and Transformer Models for IoT Services;
  • Innovative Applications of Generative AI in IoT Services.

7. IoT Service Composition and Application

  • Automatic IoT service composition;
  • IoT Business process integration and management;
  • IoT Service coordination and cooperation;
  • IoT Service-based data integration;
  • IoT Data-driven service composition;
  • IoT Knowledge-driven service composition;
  • IoT Service orchestration and choreography for the future Internet.

Dr. Yang Zhang
Prof. Dr. Michael Sheng
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • LLM-driven IoT service
  • multimodal models enhanced IoT service
  • AI-based IoT service reasoning
  • generative AI as a IoT service
  • IoT security and privacy

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Published Papers (3 papers)

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30 pages, 2090 KiB  
Article
Ubunye: An MEC Orchestration Service Based on QoE, QoS, and Service Classification Using Machine Learning
by Kilbert Amorim Maciel, David Martins Leite, Guilherme Alves de Araújo, Flavia C. Delicato and Atslands R. Rocha
Future Internet 2025, 17(2), 66; https://doi.org/10.3390/fi17020066 - 5 Feb 2025
Viewed by 626
Abstract
The increasing adoption of Internet of Things devices has led to a significant demand for cloud services, where latency and bandwidth play a crucial role in shaping users’ perception of network service quality. However, the use of cloud services with the desired quality [...] Read more.
The increasing adoption of Internet of Things devices has led to a significant demand for cloud services, where latency and bandwidth play a crucial role in shaping users’ perception of network service quality. However, the use of cloud services with the desired quality is not always available to all users. Furthermore, uneven network coverage in urban and rural areas has created “digital deserts”, which are characterized by a lack of connectivity resources, complicating access to cloud services. In this scenario, edge computing emerges as a promising alternative for service provision. Edge computing leverages data processing at or near the source where it is generated rather than sending it to the cloud for processing. It can lead to several advantages, such as reduced latency and lower bandwidth usage. This paper addresses the need to ensure consistent quality of experience (QoE) and quality of service (QoS) in dynamic network environments, particularly in remote regions with limited infrastructure. We propose an orchestration service called Ubunye, which operates at the network edge and selects the most appropriate edge node to fulfill a given application request while satisfying its quality requirements. Ubunye considers factors such as latency and available bandwidth when selecting a node to execute the requested service. It implements a service classification system based on machine learning (ML) techniques. The ideal edge node is chosen through a multi-faceted evaluation, which includes current CPU load, memory availability, and other relevant parameters. Experiment results show that Ubunye effectively orchestrates resources at the network edge, enhancing QoE and QoS for services that demand low latency and high bandwidth. Additionally, it showcases the ability to classify services and allocate resources under challenging network conditions. Full article
(This article belongs to the Special Issue Internet of Things Technology and Service Computing)
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24 pages, 5558 KiB  
Article
A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks
by Mehrdad Shoeibi, Anita Ershadi Oskouei and Masoud Kaveh
Future Internet 2024, 16(12), 460; https://doi.org/10.3390/fi16120460 - 6 Dec 2024
Cited by 3 | Viewed by 916
Abstract
The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal [...] Read more.
The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing the dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including the limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network’s achievable rate while satisfying IoT devices’ energy harvesting constraints. Our DRL framework leverages a novel six-dimensional chimp optimization algorithm (6DChOA) to fine-tune the hyper-parameters, ensuring efficient and adaptive learning. The proposed 6DChOA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating interference from direct and RIS-cascaded links. The simulation results demonstrate that our optimized RIS design significantly improves energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts. Full article
(This article belongs to the Special Issue Internet of Things Technology and Service Computing)
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27 pages, 14786 KiB  
Article
New Model for Defining and Implementing Performance Tests
by Marek Bolanowski, Michał Ćmil and Adrian Starzec
Future Internet 2024, 16(10), 366; https://doi.org/10.3390/fi16100366 - 10 Oct 2024
Viewed by 1462
Abstract
The article proposes a new model for defining and implementing performance tests used in the process of designing and operating IT systems. By defining the objectives, types, topological patterns, and methods of implementation, a coherent description of the test preparation and execution is [...] Read more.
The article proposes a new model for defining and implementing performance tests used in the process of designing and operating IT systems. By defining the objectives, types, topological patterns, and methods of implementation, a coherent description of the test preparation and execution is achieved, facilitating the interpretation of results and enabling straightforward replication of test scenarios. The model was used to develop and implement performance tests in a laboratory environment and in a production system. The proposed division of the testing process into layers correlated with the test preparation steps allows to separate quasi-independent areas, which can be handled by isolated teams of engineers. Such an approach allows to accelerate the process of implementation of performance tests and may affect the optimization of the cost of their implementation. Full article
(This article belongs to the Special Issue Internet of Things Technology and Service Computing)
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