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Sensors
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Privacy and Security in Sensor Networks
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Privacy and Security in Sensor Networks

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 1066

Special Issue Editors

Dr. Ahmed Sherif

E-Mail Website
Guest Editor
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USA
Interests: cryptography and network security; cybersecurity; AI in cybersecurity; hardware security; privacy and security-preserving schemes in autonomous vehicles; vehicular ad hoc networks; Internet of Things (IoT) applications; smart grid advanced metering infrastructure network
Special Issues, Collections and Topics in MDPI journals
Dr. Kasem Khalil

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Mississippi, Oxford, MS 38677, USA
Interests: hardware security; electronics; VLSI; machine learning; artificial intelligence; Internet of Things; security
Special Issues, Collections and Topics in MDPI journals
Dr. Nick Rahimi

E-Mail Website
Guest Editor
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USA
Interests: computer and network security; machine learning in cybersecurity; peer-to-peer networks; blockchain technology; distributed systems; decentralized systems

Special Issue Information

Dear Colleagues,

The expanding interconnectivity of sensors and the escalating complexity of systems have underscored significant weaknesses in privacy and security. This Special Issue intends to highlight advanced research and novel solutions that tackle the continuously changing difficulties in sensor networks. This issue will examine improvements in secure design techniques, threat detection mechanisms, and hardware-based cryptographic implementations, focusing on protecting sensor networks, IoT devices, embedded systems, and high-performance computing architectures. This Special Issue aims to thoroughly understand the present status and future trajectories of privacy and security frameworks by integrating interdisciplinary perspectives, thereby promoting collaboration among academia, industry, and government entities to develop robust and resilient systems.

Dr. Ahmed Sherif
Dr. Kasem Khalil
Dr. Nick Rahimi
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. Sensors 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 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

  • applied cryptography
  • IoT privacy and security
  • privacy-preservation schemes
  • AI-based privacy and security schemes
  • secure embedded systems
  • cryptographic hardware
  • physical unclonable functions (PUFs)
  • secure hardware architectures
  • hardware trojan detection
  • supply chain security
  • intrusion detection systems

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Published Papers (2 papers)

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Research

20 pages, 3592 KiB  
Article
Federated Security for Privacy Preservation of Healthcare Data in Edge-Cloud Environments
by Rasanga Jayaweera, Himanshu Agrawal and Nickson M. Karie
Sensors 2025, 25(16), 5108; https://doi.org/10.3390/s25165108 - 17 Aug 2025
Viewed by 409
Abstract
Digital transformation in healthcare has introduced data privacy challenges, as hospitals struggle to protect patient information while adopting digital technologies such as AI, IoT, and cloud more rapidly than ever before. The adoption of powerful third-party Machine Learning as a Service (MLaaS) solutions [...] Read more.
Digital transformation in healthcare has introduced data privacy challenges, as hospitals struggle to protect patient information while adopting digital technologies such as AI, IoT, and cloud more rapidly than ever before. The adoption of powerful third-party Machine Learning as a Service (MLaaS) solutions for disease prediction has become a common practice. However, these solutions offer significant privacy risks when sensitive healthcare data are shared externally to a third-party server. This raises compliance concerns under regulations like HIPAA, GDPR, and Australia’s Privacy Act. To address these challenges, this paper explores a decentralized, privacy-preserving approach to train the models among multiple healthcare stakeholders, integrating Federated Learning (FL) with Homomorphic Encryption (HE), ensuring model parameters remain protected throughout the learning process. This paper proposes a novel Homomorphic Encryption-based Adaptive Tuning for Federated Learning (HEAT-FL) framework to select encryption parameters based on model layer sensitivity. The proposed framework leverages the CKKS scheme to encrypt model parameters on the client side before sharing. This enables secure aggregation at the central server without requiring decryption, providing an additional layer of security through model-layer-wise parameter management. The proposed adaptive encryption approach significantly improves runtime efficiency while maintaining a balanced level of security. Compared to the existing frameworks (non-adaptive) using 256-bit security settings, the proposed framework offers a 56.5% reduction in encryption time for 10 clients and 54.6% for four clients per epoch. Full article
(This article belongs to the Special Issue Privacy and Security in Sensor Networks)
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16 pages, 1949 KiB  
Article
Secure Integration of Sensor Networks and Distributed Web Systems for Electronic Health Records and Custom CRM
by Marian Ileana, Pavel Petrov and Vassil Milev
Sensors 2025, 25(16), 5102; https://doi.org/10.3390/s25165102 - 17 Aug 2025
Viewed by 364
Abstract
In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data [...] Read more.
In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data with a custom customer relationship management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance. Full article
(This article belongs to the Special Issue Privacy and Security in Sensor Networks)
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