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Security and Smart Applications in IoT and Wireless Sensor and...
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Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Wireless Control Networks".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 13303

Special Issue Editors

Dr. Donald Elmazi

E-Mail Website
Guest Editor
Computer Engineering Department, Faculty of Computer Science, University Metropolitan Tirana, 1003 Tirana, Albania
Interests: next generations networks; IoT; intelligent algorithms
Dr. Elis Kulla

E-Mail Website
Guest Editor
Department of System Management, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka 811-0295, Japan
Interests: mobile ad-hoc networks; wireless sensor networks; ad-hoc vehicular networks; mesh networks; fuzzy logic; genetic algorithms; machine learning

Special Issue Information

Dear Colleagues,

In an era defined by rapid technological advancement, the Internet of Things (IoT) has emerged as a transformative force, reshaping the way we interact with the world around us. At the heart of this technological revolution lies the seamless integration of smart applications within IoT ecosystems, which are interconnected through Wireless Sensors and Actor Networks (WSAN). This convergence of cutting-edge technologies has not only accelerated the development of innovative solutions but has also redefined our understanding of connectivity, efficiency, and automation in various domains, from home automation to industrial processes, healthcare, and environmental monitoring.

The concept of IoT revolves around the interconnection of everyday objects, devices, and systems through the Internet, enabling them to collect, transmit, and exchange data in real time. Wireless sensors play a pivotal role in this network, as they are the eyes and ears of the IoT, collecting data from the physical world. These sensors are equipped with a multitude of capabilities, including data acquisition, processing, and communication, making them the building blocks of smart applications that enhance our daily lives. Actors in this context refer to the entities capable of taking actions based on the data collected and processed by sensors. These can be machines, robots, algorithms, or even humans. WSANs represent the synergy between sensors and actors, forming a dynamic, self-organizing system that can respond to changing conditions, thereby offering greater intelligence and autonomy to IoT applications. This dynamic interaction between sensors and actors is the key to enabling smart, context-aware decision making and automation in various domains.

In this era of connectivity, the potential applications of smart systems in IoT and WSANs are boundless. From smart homes that adjust their environment based on your preferences to industrial setups that optimize processes and maintenance, and from precision agriculture that ensures sustainable food production to healthcare systems that monitor patients' health in real time, the IoT and WSANs are revolutionizing the way we live, work, and play.

This exploration of smart applications in the IoT and WSANs delves into the principles, technologies, and the wide array of domains where they are making a significant impact. As we journey through this dynamic landscape, we will uncover the ways in which these interconnected systems are shaping our future and propelling us into an era of unprecedented convenience, efficiency, and sustainability.

The topics covered include, but are not limited to, the following:

  • Simulations and experiments;
  • Energy management;
  • Routing efficiency;
  • Optimization algorithms;
  • Applications;
  • Security and privacy;
  • Data aggregation techniques;
  • Cloud infrastructure;
  • Internet of Everything (IoE);
  • Intelligent algorithms.

This Special Issue aims to provide novel ideas related to these topics, providing insight into the technologies that are transforming our future.

Dr. Donald Elmazi
Dr. Elis Kulla
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. Journal of Sensor and Actuator Networks 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 2000 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

  • security and privacy
  • Internet of Everything (IoE)
  • intelligent algorithms
  • Internet of Things (IoT)
  • Actuator Networks

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Published Papers (6 papers)

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Research

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29 pages, 2096 KiB  
Article
Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach
by Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano and Kateryna Kuznetsova
J. Sens. Actuator Netw. 2024, 13(6), 78; https://doi.org/10.3390/jsan13060078 - 14 Nov 2024
Viewed by 479
Abstract
Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element [...] Read more.
Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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30 pages, 3145 KiB  
Article
Create a Realistic IoT Dataset Using Conditional Generative Adversarial Network
by Miada Almasre and Alanoud Subahi
J. Sens. Actuator Netw. 2024, 13(5), 62; https://doi.org/10.3390/jsan13050062 - 3 Oct 2024
Viewed by 709
Abstract
The increased use of Internet of Things (IoT) devices has led to greater threats to privacy and security. This has created a need for more effective cybersecurity applications. However, the effectiveness of these systems is often limited by the lack of comprehensive and [...] Read more.
The increased use of Internet of Things (IoT) devices has led to greater threats to privacy and security. This has created a need for more effective cybersecurity applications. However, the effectiveness of these systems is often limited by the lack of comprehensive and balanced datasets. This research contributes to IoT security by tackling the challenges in dataset generation and providing a valuable resource for IoT security research. Our method involves creating a testbed, building the ‘Joint Dataset’, and developing an innovative tool. The tool consists of two modules: an Exploratory Data Analysis (EDA) module, and a Generator module. The Generator module uses a Conditional Generative Adversarial Network (CGAN) to address data imbalance and generate high-quality synthetic data that accurately represent real-world network traffic. To showcase the effectiveness of the tool, the proportion of imbalance reduction in the generated dataset was computed and benchmarked to the BOT-IOT dataset. The results demonstrated the robustness of synthetic data generation in creating balanced datasets. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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18 pages, 1703 KiB  
Article
Hybrid Encryption Model for Secured Three-Phase Authentication Protocol in IoT
by Amr Munshi and Bandar Alshawi
J. Sens. Actuator Netw. 2024, 13(4), 41; https://doi.org/10.3390/jsan13040041 - 17 Jul 2024
Viewed by 1108
Abstract
The Internet of things (IoT) has recently received a great deal of attention, and there has been a large increase in the number of IoT devices owing to its significance in current communication networks. In addition, the validation of devices is an important [...] Read more.
The Internet of things (IoT) has recently received a great deal of attention, and there has been a large increase in the number of IoT devices owing to its significance in current communication networks. In addition, the validation of devices is an important concern and a major safety demand in IoT systems, as any faults in the authentication or identification procedure will lead to threatening attacks that cause the system to close. In this study, a new, three-phase authentication protocol in IoT is implemented. The initial phase concerns the user registration phase, in which encryption takes place with a hybrid Elliptic Curve Cryptography (ECC)–Advanced Encryption Standard (AES) model with an optimization strategy, whereby key generation is optimally accomplished via a Self-Improved Aquila Optimizer (SI-AO). The second and third phases include the login process and the authentication phase, in which information flow control-based authentication is conducted. Finally, decryption is achieved based on the hybrid ECC–AES model. The employed scheme’s improvement is established using various metrics. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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18 pages, 834 KiB  
Article
A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks
by Felipe Augusto Dutra Bueno, Alessandro Goedtel, Taufik Abrão and José Carlos Marinello
J. Sens. Actuator Netw. 2024, 13(3), 30; https://doi.org/10.3390/jsan13030030 - 30 Apr 2024
Viewed by 1452
Abstract
The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as [...] Read more.
The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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18 pages, 1189 KiB  
Article
Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control
by Salim Sabah Bulbul, Zaid Ameen Abduljabbar, Duaa Fadhel Najem, Vincent Omollo Nyangaresi, Junchao Ma and Abdulla J. Y. Aldarwish
J. Sens. Actuator Netw. 2024, 13(1), 12; https://doi.org/10.3390/jsan13010012 - 2 Feb 2024
Cited by 1 | Viewed by 1958
Abstract
Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, [...] Read more.
Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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Review

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26 pages, 2233 KiB  
Review
Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management
by Fernando Fuentes-Peñailillo, Karen Gutter, Ricardo Vega and Gilda Carrasco Silva
J. Sens. Actuator Netw. 2024, 13(4), 39; https://doi.org/10.3390/jsan13040039 - 8 Jul 2024
Cited by 10 | Viewed by 6136
Abstract
This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production [...] Read more.
This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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