Sensors

2024

Jump to: 2023, 2022, 2021, 2020

17 pages, 1665 KiB

Open AccessArticle

Fine-Grained Boundary Conditions in Field-Based Routing

by Jihoon Sung and Yeunwoong Kyung

Sensors 2024, 24(3), 813; https://doi.org/10.3390/s24030813 - 26 Jan 2024

Viewed by 810

Abstract

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to [...] Read more.

In the realm of industrial wireless mesh networks, an efficient routing protocol is highly demanded to play a crucial role in ensuring that packets are efficiently directed along shorter and congestion-free routes toward gateways. Field-based routing has emerged as a promising solution to tackle these network challenges. This routing approach draws inspiration from physics and employs a differential equation to model its behavior in finding efficient routes. Given the fundamental significance of boundary conditions in physics, where they play an essential role in shaping the solutions to the equation, exploring the impact of boundary conditions on field-based routing behavior within network domains becomes highly significant. However, despite their influence, the impact of boundary conditions has remained unexplored in existing studies on field-based routing. In this context, our work explores the boundary condition problem and introduces new advanced fine-grained boundary conditions for field-based routing. We demonstrate the superior performance of our proposed scheme, highlighting the substantial role of boundary conditions in network behavior. Our work holds significant value in that it explores the boundary condition problem, an aspect largely overlooked in previous research, and provides a viable solution, underscoring its crucial importance in routing enhancement. Full article

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2023

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21 pages, 1869 KiB

Open AccessArticle

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

by Artjom Lind, Shan Wu and Amnir Hadachi

Sensors 2023, 23(7), 3603; https://doi.org/10.3390/s23073603 - 30 Mar 2023

Cited by 2 | Viewed by 1451

Abstract

Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the [...] Read more.

Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter’s inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells’ coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network. Full article

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2022

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23 pages, 917 KiB

Open AccessArticle

Arithmetic Framework to Optimize Packet Forwarding among End Devices in Generic Edge Computing Environments

by Pedro Juan Roig, Salvador Alcaraz, Katja Gilly, Cristina Bernad and Carlos Juiz

Sensors 2022, 22(2), 421; https://doi.org/10.3390/s22020421 - 6 Jan 2022

Cited by 4 | Viewed by 1613

Abstract

Multi-access edge computing implementations are ever increasing in both the number of deployments and the areas of application. In this context, the easiness in the operations of packet forwarding between two end devices being part of a particular edge computing infrastructure may allow [...] Read more.

Multi-access edge computing implementations are ever increasing in both the number of deployments and the areas of application. In this context, the easiness in the operations of packet forwarding between two end devices being part of a particular edge computing infrastructure may allow for a more efficient performance. In this paper, an arithmetic framework based in a layered approach has been proposed in order to optimize the packet forwarding actions, such as routing and switching, in generic edge computing environments by taking advantage of the properties of integer division and modular arithmetic, thus simplifying the search of the proper next hop to reach the desired destination into simple arithmetic operations, as opposed to having to look into the routing or switching tables. In this sense, the different type of communications within a generic edge computing environment are first studied, and afterwards, three diverse case scenarios have been described according to the arithmetic framework proposed, where all of them have been further verified by using arithmetic means with the help of applying theorems, as well as algebraic means, with the help of searching for behavioral equivalences. Full article

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2021

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25 pages, 1980 KiB

Open AccessArticle

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

by Krzysztof K. Cwalina, Piotr Rajchowski, Alicja Olejniczak, Olga Błaszkiewicz and Robert Burczyk

Sensors 2021, 21(22), 7716; https://doi.org/10.3390/s21227716 - 19 Nov 2021

Viewed by 1883

Abstract

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for [...] Read more.

Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost. Full article

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17 pages, 4149 KiB

Open AccessArticle

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

by Elias Hatem, Sergio Fortes, Elizabeth Colin, Sara Abou-Chakra, Jean-Marc Laheurte and Bachar El-Hassan

Sensors 2021, 21(16), 5346; https://doi.org/10.3390/s21165346 - 8 Aug 2021

Cited by 8 | Viewed by 3387

Abstract

Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization [...] Read more.

Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity. Full article

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20 pages, 2106 KiB

Open AccessArticle

Algebraic Connectivity Control in Distributed Networks by Using Multiple Communication Channels

by Karlo Griparić

Sensors 2021, 21(15), 5014; https://doi.org/10.3390/s21155014 - 23 Jul 2021

Cited by 2 | Viewed by 2305

Abstract

The effectiveness of collaboration in distributed networks, such as sensor networks and multi-agent systems, relies on nodes’ ability to exchange information. The availability of various communication protocols with different technical properties opens the possibility to guarantee connectivity during a system’s operation in any [...] Read more.

The effectiveness of collaboration in distributed networks, such as sensor networks and multi-agent systems, relies on nodes’ ability to exchange information. The availability of various communication protocols with different technical properties opens the possibility to guarantee connectivity during a system’s operation in any condition. A communication network can be represented by a graph on which connectivity can be expressed by a well-known algebraic connectivity value or Fiedler value. It is one of the most important tools used in many applications where connectivity preservation is required. In this paper, a trust-based consensus algorithm for algebraic connectivity estimation has been implemented. To guarantee the accomplishment of the global objective and the system’s performance, our contributions include: (i) a novel decentralized framework for combining multiple communication channels in a resulting channel and (ii) a decentralized algebraic connectivity control law that dynamically changes the number of agents in the system during operation. The proposed algebraic connectivity control strategy has been evaluated in simulations and in a real multi-robot system using two channels with different properties and initial topologies. Full article

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18 pages, 924 KiB

Open AccessArticle

Computing Resource Allocation Scheme for DAG-Based IOTA Nodes

by Houssein Hellani, Layth Sliman, Abed Ellatif Samhat and Ernesto Exposito

Sensors 2021, 21(14), 4703; https://doi.org/10.3390/s21144703 - 9 Jul 2021

Cited by 10 | Viewed by 3120

Abstract

IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are [...] Read more.

IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections. Full article

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34 pages, 957 KiB

Open AccessArticle

Automatic Actionable Information Processing and Trust Management towards Safer Internet of Things

by Marek Janiszewski, Anna Felkner, Piotr Lewandowski, Marcin Rytel and Hubert Romanowski

Sensors 2021, 21(13), 4359; https://doi.org/10.3390/s21134359 - 25 Jun 2021

Cited by 8 | Viewed by 2974

Abstract

The security of the Internet of Things (IoT) is a very important aspect of everyday life for people and industries, as well as hospitals, military, households and cities. Unfortunately, this topic is still too little researched and developed, which results in exposing users [...] Read more.

The security of the Internet of Things (IoT) is a very important aspect of everyday life for people and industries, as well as hospitals, military, households and cities. Unfortunately, this topic is still too little researched and developed, which results in exposing users of Internet of Things to possible threats. One of the areas which should be addressed is the creation of a database of information about vulnerabilities and exploits in the Internet of Things; therefore, the goal of our activities under the VARIoT (Vulnerability and Attack Repository for IoT) project is to develop such a database and make it publicly available. The article presents the results of our research aimed at building this database, i.e., how the information about vulnerabilities is obtained, standardized, aggregated and correlated as well as the way of enhancing and selecting IoT related data. We have obtained and proved that existing databases provide various scopes of information and because of that a single and most comprehensive source of information does not exist. In addition, various sources present information about a vulnerability at different times—some of them are faster than others, and the differences in publication dates are significant. The results of our research show that aggregation of information from various sources can be very beneficial and has potential to enhance actionable value of information. We have also shown that introducing more sophisticated concepts, such as trust management and metainformation extraction based on artificial intelligence, could ensure a higher level of completeness of information as well as evaluate the usefulness and reliability of data. Full article

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14 pages, 504 KiB

Open AccessArticle

Transmission Quality Classification with Use of Fusion of Neural Network and Genetic Algorithm in Pay&Require Multi-Agent Managed Network

by Dariusz Żelasko, Wojciech Książek and Paweł Pławiak

Sensors 2021, 21(12), 4090; https://doi.org/10.3390/s21124090 - 14 Jun 2021

Cited by 5 | Viewed by 2021

Abstract

Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through [...] Read more.

Modern computer systems practically cannot function without a computer network. New concepts of data transmission are emerging, e.g., programmable networks. However, the development of computer networks entails the need for development in one more aspect, i.e., the quality of the data transmission through the network. The data transmission quality can be described using parameters, i.e., delay, bandwidth, packet loss ratio and jitter. On the basis of the obtained values, specialists are able to state how measured parameters impact on the overall quality of the provided service. Unfortunately, for a non-expert user, understanding of these parameters can be too complex. Hence, the problem of translation of the parameters describing the transmission quality appears understandable to the user. This article presents the concept of using Machine Learning (ML) to solve the above-mentioned problem, i.e., a dynamic classification of the measured parameters describing the transmission quality in a certain scale. Thanks to this approach, describing the quality will become less complex and more understandable for the user. To date, some studies have been conducted. Therefore, it was decided to use different approaches, i.e., fusion of a neural network (NN) and a genetic algorithm (GA). GA’s were choosen for the selection of weights replacing the classic gradient descent algorithm. For learning purposes, 100 samples were obtained, each of which was described by four features and the label, which describes the quality. In the reasearch carried out so far, single classifiers and ensemble learning have been used. The current result compared to the previous ones is better. A relatively high quality of the classification was obtained when we have used 10-fold stratified cross-validation, i.e., SEN = 95% (overall accuracy). The incorrect classification was 5/100, which is a better result compared to previous studies. Full article

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23 pages, 3153 KiB

Open AccessArticle

Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking

by Mateusz Krzysztoń and Ewa Niewiadomska-Szynkiewicz

Sensors 2021, 21(11), 3625; https://doi.org/10.3390/s21113625 - 23 May 2021

Cited by 4 | Viewed by 2599

Abstract

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier [...] Read more.

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier than air when accidentally released into the atmosphere. The paper addresses issues concerning sensing networks’ design, focussing on a computing scheme for online motion trajectory calculation and data exchange. A three-stage approach that incorporates three algorithms for sensing devices’ displacement calculation in a collaborative network according to the current task, namely exploration and gas cloud detection, boundary detection and estimation, and tracking the evolving cloud, is presented. A network connectivity-maintaining virtual force mobility model is used to calculate subsequent sensor positions, and multi-hop communication is used for data exchange. The main focus is on the efficient tracking of the cloud boundary. The proposed sensing scheme is sensitive to crucial mobility model parameters. The paper presents five procedures for calculating the optimal values of these parameters. In contrast to widely used techniques, the presented approach to gas cloud monitoring does not calculate sensors’ displacements based on exact values of gas concentration and concentration gradients. The sensor readings are reduced to two values: the gas concentration below or greater than the safe value. The utility and efficiency of the presented method were justified through extensive simulations, giving encouraging results. The test cases were carried out on several scenarios with regular and irregular shapes of clouds generated using a widely used box model that describes the heavy gas dispersion in the atmospheric air. The simulation results demonstrate that using only a rough measurement indicating that the threshold concentration value was exceeded can detect and efficiently track a gas cloud boundary. This makes the sensing system less sensitive to the quality of the gas concentration measurement. Thus, it can be easily used to detect real phenomena. Significant results are recommendations on selecting procedures for computing mobility model parameters while tracking clouds with different shapes and determining optimal values of these parameters in convex and nonconvex cloud boundaries. Full article

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24 pages, 3558 KiB

Open AccessArticle

Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

by Marek Amanowicz and Damian Jankowski

Sensors 2021, 21(9), 2972; https://doi.org/10.3390/s21092972 - 23 Apr 2021

Cited by 8 | Viewed by 3350

Abstract

The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based [...] Read more.

The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions. Full article

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19 pages, 1151 KiB

Open AccessArticle

A Multivariate Time-Series Based Approach for Quality Modeling in Wireless Networks

by Leonardo Aguayo, Sergio Fortes, Carlos Baena, Eduardo Baena and Raquel Barco

Sensors 2021, 21(6), 2017; https://doi.org/10.3390/s21062017 - 12 Mar 2021

Cited by 1 | Viewed by 2058

Abstract

This work presents a method for estimating key quality indicators (KQIs) from measurements gathered at the nodes of a wireless network. The procedure employs multivariate adaptive filtering and a clustering algorithm to produce a KQI time-series suitable for post-processing by the network management [...] Read more.

This work presents a method for estimating key quality indicators (KQIs) from measurements gathered at the nodes of a wireless network. The procedure employs multivariate adaptive filtering and a clustering algorithm to produce a KQI time-series suitable for post-processing by the network management system. The framework design, aimed to be applied to 5G and 6G systems, can cope with a nonstationary environment, allow fast and online training, and provide flexibility for its implementation. The concept’s feasibility was evaluated using measurements collected from a live heterogeneous network, and initial results were compared to other linear regression techniques. Suggestions for modifications in the algorithms are also described, as well as directions for future research. Full article

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18 pages, 1627 KiB

Open AccessArticle

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

by Sergio Fortes, Carlos Baena, Javier Villegas, Eduardo Baena, Muhammad Zeeshan Asghar and Raquel Barco

Sensors 2021, 21(4), 1501; https://doi.org/10.3390/s21041501 - 22 Feb 2021

Cited by 14 | Viewed by 3167

Abstract

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the [...] Read more.

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost. Full article

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24 pages, 1103 KiB

Open AccessArticle

Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

by Oswaldo Sebastian Peñaherrera-Pulla, Carlos Baena, Sergio Fortes, Eduardo Baena and Raquel Barco

Sensors 2021, 21(4), 1387; https://doi.org/10.3390/s21041387 - 17 Feb 2021

Cited by 35 | Viewed by 6390

Abstract

Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and [...] Read more.

Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and online gaming but using reduced hardware requirements. In contrast, this approach stresses the communication networks between the client and the cloud. In this context, it is necessary to know how to configure the network in order to provide service with the best quality. To that end, the present work defines a novel framework for Cloud Gaming performance evaluation. This system is implemented in a real testbed and evaluates the Cloud Gaming approach for different transport networks (Ethernet, WiFi, and LTE (Long Term Evolution)) and scenarios, automating the acquisition of the gaming metrics. From this, the impact on the overall gaming experience is analyzed identifying the main parameters involved in its performance. Hence, the future lines for Cloud Gaming QoE-based (Quality of Experience) optimization are established, this way being of configuration, a trendy paradigm in the new-generation networks, such as 4G and 5G (Fourth and Fifth Generation of Mobile Networks). Full article

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32 pages, 596 KiB

Open AccessArticle

Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

by Davi Ribeiro Militani, Hermes Pimenta de Moraes, Renata Lopes Rosa, Lunchakorn Wuttisittikulkij, Miguel Arjona Ramírez and Demóstenes Zegarra Rodríguez

Sensors 2021, 21(2), 504; https://doi.org/10.3390/s21020504 - 12 Jan 2021

Cited by 13 | Viewed by 3458

Abstract

The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning [...] Read more.

The routing algorithm is one of the main factors that directly impact on network performance. However, conventional routing algorithms do not consider the network data history, for instances, overloaded paths or equipment faults. It is expected that routing algorithms based on machine learning present advantages using that network data. Nevertheless, in a routing algorithm based on reinforcement learning (RL) technique, additional control message headers could be required. In this context, this research presents an enhanced routing protocol based on RL, named e-RLRP, in which the overhead is reduced. Specifically, a dynamic adjustment in the Hello message interval is implemented to compensate the overhead generated by the use of RL. Different network scenarios with variable number of nodes, routes, traffic flows and degree of mobility are implemented, in which network parameters, such as packet loss, delay, throughput and overhead are obtained. Additionally, a Voice-over-IP (VoIP) communication scenario is implemented, in which the E-model algorithm is used to predict the communication quality. For performance comparison, the OLSR, BATMAN and RLRP protocols are used. Experimental results show that the e-RLRP reduces network overhead compared to RLRP, and overcomes in most cases all of these protocols, considering both network parameters and VoIP quality. Full article

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2020

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24 pages, 2261 KiB

Open AccessArticle

Transform-Based Multiresolution Decomposition for Degradation Detection in Cellular Networks

by Sergio Fortes, Pablo Muñoz, Inmaculada Serrano and Raquel Barco

Sensors 2020, 20(19), 5645; https://doi.org/10.3390/s20195645 - 2 Oct 2020

Cited by 3 | Viewed by 2560

Abstract

Anomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required [...] Read more.

Anomaly detection in the performance of the huge number of elements that are part of cellular networks (base stations, core entities, and user equipment) is one of the most time consuming and key activities for supporting failure management procedures and ensuring the required performance of the telecommunication services. This activity originally relied on direct human inspection of cellular metrics (counters, key performance indicators, etc.). Currently, degradation detection procedures have experienced an evolution towards the use of automatic mechanisms of statistical analysis and machine learning. However, pre-existent solutions typically rely on the manual definition of the values to be considered abnormal or on large sets of labeled data, highly reducing their performance in the presence of long-term trends in the metrics or previously unknown patterns of degradation. In this field, the present work proposes a novel application of transform-based analysis, using wavelet transform, for the detection and study of network degradations. The proposed system is tested using cell-level metrics obtained from a real-world LTE cellular network, showing its capabilities to detect and characterize anomalies of different patterns and in the presence of varied temporal trends. This is performed without the need for manually establishing normality thresholds and taking advantage of wavelet transform capabilities to separate the metrics in multiple time-frequency components. Our results show how direct statistical analysis of these components allows for a successful detection of anomalies beyond the capabilities of detection of previous methods. Full article

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