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Advances in Security of Mobile and Wireless Communications
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Advances in Security of Mobile and Wireless Communications

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 6852

Special Issue Editor

Dr. Hyoungshick Kim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, College of Computing, Sungkyunkwan University, Seoul 06351, Republic of Korea
Interests: usable security; blockchain; security vulnerability analysis; data-driven security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancements in mobile and wireless communications have transformed industries such as transportation and healthcare. This has expanded the capabilities of our world, but it has also brought about new cybersecurity challenges. The surge in connected devices produces large amounts of data, making them attractive to potential attackers and creating new vulnerabilities. Mobile malware, wireless sensor network intrusion, and data breaches are serious threats that need to be addressed. As technology continues to evolve, the sophistication of attacks is also increasing, making traditional security measures inadequate.

This Special Issue explores cutting-edge mobile and wireless communications security developments, including machine learning (ML)-based security solutions; malware, intrusion, and anomaly detection; risk assessment; and security protocols. The Special Issue aims to report high-quality research on recent advances in mobile and wireless communications security. Topics of interest include, but are not limited to, those covered by the keyword list below.

Dr. Hyoungshick Kim
Guest Editor

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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • mobile security
  • wireless security
  • mobile malware
  • mobile/wireless sensor network intrusion
  • intrusion and anomaly detection in mobile and wireless sensor networks
  • risk assessment in mobile and wireless sensor networks
  • security protocols for mobile and wireless sensor networks

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Published Papers (3 papers)

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Research

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15 pages, 4855 KiB  
Communication
A Hierarchical Dispatcher for Scheduling Multiple Deep Neural Networks (DNNs) on Edge Devices
by Hyung Kook Jun, Taeho Kim, Sang Cheol Kim and Young Ik Eom
Sensors 2025, 25(7), 2243; https://doi.org/10.3390/s25072243 - 2 Apr 2025
Viewed by 258
Abstract
This paper presents a hierarchical dispatcher architecture designed to efficiently schedule the execution of multiple deep neural networks (DNNs) on edge devices with heterogeneous processing units (PUs). The proposed architecture is applicable to systems where PUs are either integrated on a single edge [...] Read more.
This paper presents a hierarchical dispatcher architecture designed to efficiently schedule the execution of multiple deep neural networks (DNNs) on edge devices with heterogeneous processing units (PUs). The proposed architecture is applicable to systems where PUs are either integrated on a single edge device or distributed across multiple devices. We separate the dispatcher and scheduling policy. The dispatcher in our framework acts as a mechanism for allocating, executing, and managing subgraphs of DNNs across various PUs, and the scheduling policy generates optimized scheduling sequences. We formalize a hierarchical structure consisting of high-level and low-level dispatchers, which together provide scalable and flexible scheduling support for diverse DNN workloads. The high-level dispatcher oversees the partitioning and distribution of subgraphs, while the low-level dispatcher handles the execution and coordination of subgraphs on allocated PUs. This separation of responsibilities allows the architecture to efficiently manage workloads in both homogeneous and heterogeneous environments. Through case studies on edge devices, we demonstrate the practicality of the proposed architecture. By integrating appropriate scheduling policies, our approach achieves an average performance improvement of 51.6%, providing a scalable and adaptable solution for deploying deep learning models on heterogeneous edge systems. Full article
(This article belongs to the Special Issue Advances in Security of Mobile and Wireless Communications)
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12 pages, 2946 KiB  
Article
Optimizing Real-Time Object Detection in a Multi-Neural Processing Unit System
by Sehyeon Oh, Yongin Kwon and Jemin Lee
Sensors 2025, 25(5), 1376; https://doi.org/10.3390/s25051376 - 24 Feb 2025
Viewed by 1178
Abstract
Real-time object detection demands high throughput and low latency, necessitating the use of hardware accelerators. NPU is specialized hardware designed to accelerate the calculation of deep learning models, providing better energy efficiency and parallel processing performance than existing CPUs or GPUs. In particular, [...] Read more.
Real-time object detection demands high throughput and low latency, necessitating the use of hardware accelerators. NPU is specialized hardware designed to accelerate the calculation of deep learning models, providing better energy efficiency and parallel processing performance than existing CPUs or GPUs. In particular, it plays an important role in reducing latency and improving processing speed in applications that require real-time processing. In this paper, we construct a real-time object detection system based on YOLOv3, utilizing Neubla’s Antara NPU, and propose two approaches for performance optimization. First, we ensure the continuity of NPU inference by allowing the CPU to process data in advance through double buffering. Second, in a multi-NPU environment, we distribute tasks among NPUs through queue-based processing and analyze the performance limits using Amdahl’s law. Experimental results demonstrate that compared to a CPU-only environment, applying the NPU in single buffering improved throughput by 2.13 times, double buffering by 3.35 times, and in a multi-NPU environment by 4.81 times. Latency decreased by 1.6 times in single and double buffering, and by 1.18 times in the multi-NPU environment. The accuracy remained consistent, with 31.4 mAP on the CPU and 31.8 mAP on the NPU. Full article
(This article belongs to the Special Issue Advances in Security of Mobile and Wireless Communications)
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Review

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45 pages, 3153 KiB  
Review
Securing Cloud-Based Internet of Things: Challenges and Mitigations
by Nivedita Singh, Rajkumar Buyya and Hyoungshick Kim
Sensors 2025, 25(1), 79; https://doi.org/10.3390/s25010079 - 26 Dec 2024
Cited by 8 | Viewed by 4588
Abstract
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various [...] Read more.
The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. Moreover, IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers. As IoT ecosystems expand, the convergence of traditional and cloud-based systems presents unique security threats in the absence of uniform regulations. Cloud-based IoT systems, enabled by Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) models, offer flexibility and scalability but also pose additional security risks. The intricate interaction between these systems and traditional IoT devices demands comprehensive strategies to protect data integrity and user privacy. This paper highlights the pressing security concerns associated with the widespread adoption of IoT devices and services. We propose viable solutions to bridge the existing security gaps while anticipating and preparing for future challenges. This paper provides a detailed survey of the key security challenges that IoT services are currently facing. We also suggest proactive strategies to mitigate these risks, thereby strengthening the overall security of IoT devices and services. Full article
(This article belongs to the Special Issue Advances in Security of Mobile and Wireless Communications)
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