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13 pages, 8848 KiB

Open AccessArticle

Optical Camera Communications in Healthcare: A Wearable LED Transmitter Evaluation during Indoor Physical Exercise

by Eleni Niarchou, Vicente Matus, Jose Rabadan, Victor Guerra and Rafael Perez-Jimenez

Sensors 2024, 24(9), 2766; https://doi.org/10.3390/s24092766 - 26 Apr 2024

Viewed by 199

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce [...] Read more.

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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21 pages, 551 KiB

Open AccessArticle

An Analytical Model of IaaS Architecture for Determining Resource Utilization

by Slawomir Hanczewski, Maciej Stasiak and Michal Weissenberg

Sensors 2024, 24(9), 2758; https://doi.org/10.3390/s24092758 - 26 Apr 2024

Viewed by 190

Abstract

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users’ virtual machines (VMs) are run on the service provider’s physical infrastructure, making [...] Read more.

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users’ virtual machines (VMs) are run on the service provider’s physical infrastructure, making it possible to become independent of the need to purchase one’s own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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30 pages, 1044 KiB

Open AccessArticle

Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection

by Adrian Komadina, Ivan Kovačević, Bruno Štengl and Stjepan Groš

Sensors 2024, 24(8), 2636; https://doi.org/10.3390/s24082636 - 20 Apr 2024

Viewed by 241

Abstract

Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good [...] Read more.

Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good detection results. This study focuses on analyzing firewall logs from a large industrial control network and presents a novel method for generating anomalies that simulate real attacker actions within the network without the need for a dedicated testbed or installed security controls. To demonstrate that the proposed method is feasible and that the constructed logs behave as one would expect real-world logs to behave, different supervised and unsupervised learning models were compared using different feature subsets, feature construction methods, scaling methods, and aggregation levels. The experimental results show that unsupervised learning methods have difficulty in detecting the injected anomalies, suggesting that they can be seamlessly integrated into existing firewall logs. Conversely, the use of supervised learning methods showed significantly better performance compared to unsupervised approaches and a better suitability for use in real systems. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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25 pages, 3321 KiB

Open AccessArticle

Decentralized IoT Data Authentication with Signature Aggregation

by Jay Bojič Burgos and Matevž Pustišek

Sensors 2024, 24(3), 1037; https://doi.org/10.3390/s24031037 - 05 Feb 2024

Viewed by 749

Abstract

The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge [...] Read more.

The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge aggregating servers and Ethereum Layer 2 rollups, offering a scalable and secure IoT data authentication solution that reduces the need for continuous, direct interaction between IoT devices and the blockchain. We utilize and compare the Nova and Risc0 proving systems for authenticating batches of IoT data by verifying signatures, ensuring data integrity and privacy. Notably, the Nova prover significantly outperforms Risc0 in proving and verification times; for instance, with 10 signatures, Nova takes 3.62 s compared to Risc0’s 369 s, with this performance gap widening as the number of signatures in a batch increases. Our framework further enhances data verifiability and trust by recording essential information on L2 rollups, creating an immutable and transparent record of authentication. The use of Layer 2 rollups atop a permissionless blockchain like Ethereum effectively reduces on-chain storage costs by approximately 48 to 57 times compared to direct Ethereum use, addressing cost bottlenecks efficiently. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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23 pages, 1521 KiB

Open AccessArticle

Comprehensive Analysis and Evaluation of Anomalous User Activity in Web Server Logs

by Lenka Benova and Ladislav Hudec

Sensors 2024, 24(3), 746; https://doi.org/10.3390/s24030746 - 24 Jan 2024

Viewed by 794

Abstract

In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by [...] Read more.

In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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28 pages, 1447 KiB

Open AccessArticle

Analytical Model of the Connection Handoff in 5G Mobile Networks with Call Admission Control Mechanisms

by Mariusz Głąbowski, Maciej Sobieraj and Maciej Stasiak

Sensors 2024, 24(2), 697; https://doi.org/10.3390/s24020697 - 22 Jan 2024

Viewed by 568

Abstract

Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this [...] Read more.

Handoff mechanisms are very important in fifth-generation (5G) mobile networks because of the cellular architecture employed to maximize spectrum utilization. Together with call admission control (CAC) mechanisms, they enable better optimization of bandwidth use. The primary objective of the research presented in this article is to analyze traffic levels, aiming to optimize traffic management and handling. This article considers the two most popular CAC mechanisms: the resource reservation mechanism and the threshold mechanism. It presents an analytical approach to occupancy distribution and blocking probability calculation in 5G mobile networks, incorporating connection handoff and CAC mechanisms for managing multiple traffic streams generated by multi-service sources. Due to the fact that the developed analytical model is an approximate model, its accuracy was also examined. For this purpose, the results of analytical calculations of the blocking probability in a group of 5G cells are compared with the simulation data. This paper is an extended version of our paper published in 17th ConTEL 2023. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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16 pages, 1494 KiB

Open AccessArticle

Novel Results on SNR Estimation for Bandlimited Optical Intensity Channels

by Wilfried Gappmair

Sensors 2024, 24(1), 23; https://doi.org/10.3390/s24010023 - 19 Dec 2023

Cited by 1 | Viewed by 534

Abstract

In a previous work of the author about non-data-aided estimation of the signal-to-noise ratio (SNR) for bandlimited optical intensity channels, a couple of limitations have been identified in terms of error performance and computational complexity. In the current paper, these deficiencies are avoided [...] Read more.

In a previous work of the author about non-data-aided estimation of the signal-to-noise ratio (SNR) for bandlimited optical intensity channels, a couple of limitations have been identified in terms of error performance and computational complexity. In the current paper, these deficiencies are avoided by the introduction of a second receiver filter with specific properties that is operated in parallel to the receiver filter normally used in this respect. Although not initially intended, the concept is also applied to data-aided SNR estimation by deriving a maximum likelihood algorithm and the Cramer–Rao lower bound (CRLB) as the theoretical limit of the error performance. In the next step, the dual-filter framework is used in the context of SNR estimation without knowledge about data symbols. The most significant benefit of this method is that the number of payload data employed for the estimation procedure might be selected arbitrarily long without impacting the spectral efficiency of the link. Since the computation of the true CRLB was out of scope due to complexity reasons, an asymptotic variant for very low SNR values is analyzed, which ends up in a closed-form solution. Furthermore, an algorithm based on first- and second-order moments of the samples at the dual-filter output is investigated, which turned out to be very attractive in terms of error performance and computational complexity. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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34 pages, 9721 KiB

Open AccessArticle

Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN)

by Khawaja Tahir Mehmood, Shahid Atiq and Muhammad Majid Hussain

Sensors 2023, 23(23), 9324; https://doi.org/10.3390/s23239324 - 22 Nov 2023

Viewed by 925

Abstract

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling [...] Read more.

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Telecommunications and Sensing)

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