Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Mobile Sensing and IoT Security
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Mobile Sensing and IoT Security

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

Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 859

Special Issue Editor

Dr. Lidia Fotia

E-Mail Website
Guest Editor
Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, I-84084 Fisciano, Italy
Interests: social networks; intelligent agents; information security and privacy; recommender systems; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advances in mobile technologies, mobile devices are now equipped with many sensors, powerful processors, big memories, and wireless communication modules. However, concerns are being raised in regard to security, privacy, and trust in the context of mobile crowdsourcing. It is important to protect security and privacy in the IoT era for the following reasons: data heterogeneity, human mobility, device diversity, and dynamic topology. This Special Issue aims to gather the latest research outcomes and developments in security, privacy, and trust in mobile and IoT contexts. Topics of interest include but are not limited to:

  • Advanced security models for IoT networks and the mobile internet;
  • Privacy protection algorithms or mechanisms for data sharing over IoT networks and the mobile internet;
  • Novel and lightweight authentication methods for different roles in IoT networks and the mobile internet;
  • Trust management models considering diverse trust levels in IoT networks and the mobile internet;
  • Evaluation of blockchain-based security and privacy systems for IoT networks and the mobile internet;
  • Machine learning models, especially distributed machine learning models, to enhance the security and privacy of IoT networks and the mobile internet;
  • Other security or privacy research directions are also welcome.

Dr. Lidia Fotia
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.

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

  • IoT
  • mobile
  • privacy
  • trust
  • reliability
  • security
  • blockchain

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 3705 KiB  
Article
An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing
by Nsikak Owoh, Jackie Riley, Moses Ashawa, Salaheddin Hosseinzadeh, Anand Philip and Jude Osamor
Sensors 2024, 24(7), 2353; https://doi.org/10.3390/s24072353 - 7 Apr 2024
Viewed by 560
Abstract
Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject [...] Read more.
Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model’s performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications. Full article
(This article belongs to the Special Issue Mobile Sensing and IoT Security)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop