Internet of Things Technology and Service Computing

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.

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

Title: A Novel 6DChOA-DRL-Based Optimization Scheme for RIS-Assisted Energy Harvesting in Batteryless IoT Networks
Authors: Mehrdad Shoeibi 1; Anita Ershadi Oskouei 2; Masoud Kaveh 3
Affiliation: 1 The WPI Business School, Worcester Polytechnic Institute, Worcester, Massachusetts 01609-2280, United States 2 School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ 07030, United States 3 Department of Information and Communication Engineering, Aalto University, 02150 Espoo, Finland
Abstract: The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating a multitude of low-power devices into various industries. As IoT ex-pands, the demand for energy-efficient, batteryless devices becomes increasingly critical for sus-tainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RIS) offer a promising solution by dynamically manipulating the wireless propagation environment to en-hance 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 RIS to maximize the achievable rate of IoT devices while satisfying energy harvesting constraints. Our DRL framework leverages a novel six-dimensional Chimp optimization algorithm (6DChOA) to fine-tune the hy-perparameters, ensuring efficient and adaptive learning. The proposed 6DChOA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating inter-ference from direct and RIS-cascaded links. Simulation results demonstrate that our optimized RIS design significantly improves both energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves up to 101% gains in harvested power and 188% improvement in data rate at a transmit power of 20 dBm. Keywords: Internet of Things; energy harvesting; reconfigurable intelligent surfaces; advanced deep reinforcement learning