With the rapid development of mobile Internet, home broadband has been integrated into people's daily lives, and the market has become increasingly saturated. User experience and broadband quality have become the key factors determining market competitiveness, and consequently, most operators currently are increasing attention to network quality issues and how to improve user experience. This paper proposes an efficient machine learning model to accurately evaluate home user network experiences. The dataset used encompasses network indicator data from 500 anonymized users, and presents a set of formidable challenges including a non-standard sampling rate and time range, an uneven distribution of observations, multiple recorded observations for identical timestamps, a constrained sample size, a subjective definition of Internet experience, and a lack of essential information regarding the data collection setup. Our novel time series characteristic-based method extracts thousands of descriptive statistics from the time series sequences which reveal that, even in the face of the dataset's inherent complexities, our proposed method excels, achieving an impressive 67% validation accuracy. This represents a substantial 3% enhancement over the performance of conventional models on this dataset. Furthermore, we explore the potential of a Recurrent Neural Network (RNN) model, which also yields promising results with a validation accuracy of 58%. It is important to underscore that the performance of the RNN model could be substantially enhanced with a larger dataset. [...]
{"title":"Enhancing user experience in home networks with machine learning-based classification","authors":"Rushat Rai, Thomas Basikolo","doi":"10.52953/fzdr8743","DOIUrl":"https://doi.org/10.52953/fzdr8743","url":null,"abstract":"With the rapid development of mobile Internet, home broadband has been integrated into people's daily lives, and the market has become increasingly saturated. User experience and broadband quality have become the key factors determining market competitiveness, and consequently, most operators currently are increasing attention to network quality issues and how to improve user experience. This paper proposes an efficient machine learning model to accurately evaluate home user network experiences. The dataset used encompasses network indicator data from 500 anonymized users, and presents a set of formidable challenges including a non-standard sampling rate and time range, an uneven distribution of observations, multiple recorded observations for identical timestamps, a constrained sample size, a subjective definition of Internet experience, and a lack of essential information regarding the data collection setup. Our novel time series characteristic-based method extracts thousands of descriptive statistics from the time series sequences which reveal that, even in the face of the dataset's inherent complexities, our proposed method excels, achieving an impressive 67% validation accuracy. This represents a substantial 3% enhancement over the performance of conventional models on this dataset. Furthermore, we explore the potential of a Recurrent Neural Network (RNN) model, which also yields promising results with a validation accuracy of 58%. It is important to underscore that the performance of the RNN model could be substantially enhanced with a larger dataset. [...]","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin Hoarau, Pierre Ugo Tournoux, Tahiry Razafindralambo
The Border Gateway Protocol (BGP) is crucial for the communication routes of the Internet. Anomalies in BGP can pose a threat to the stability of the Internet. These anomalies, caused by a variety of factors, can be challenging to detect due to the massive and complex nature of BGP data traces. Various machine learning techniques have been employed to overcome this issue. The traditional approach involves the extraction of ad hoc features, which, although effective, results in a significant loss of information and may be biased towards a certain type of anomaly. A recent supervised machine learning pipeline learns representations from BGP graphs derived from BGP data traces. Although this solution achieves good anomaly detection results, the representations learned are specific to the types of anomalies within the training data. To overcome this limitation, in this paper, we propose to learn the representations of normal BGP behaviour in an unsupervised manner using a Graph Auto-Encoder (GAE). This approach ensures that the representations are not limited to the specific set of anomalies included in the training set. These representations associated with a Multi-Layer Perceptron (MLP)-based detector allowed to achieve an accuracy rate of 99% in detecting large-scale events, outperforming previous literature results.
{"title":"Unsupervised representation learning for BGP anomaly detection using graph auto-encoders","authors":"Kevin Hoarau, Pierre Ugo Tournoux, Tahiry Razafindralambo","doi":"10.52953/ctfy7896","DOIUrl":"https://doi.org/10.52953/ctfy7896","url":null,"abstract":"The Border Gateway Protocol (BGP) is crucial for the communication routes of the Internet. Anomalies in BGP can pose a threat to the stability of the Internet. These anomalies, caused by a variety of factors, can be challenging to detect due to the massive and complex nature of BGP data traces. Various machine learning techniques have been employed to overcome this issue. The traditional approach involves the extraction of ad hoc features, which, although effective, results in a significant loss of information and may be biased towards a certain type of anomaly. A recent supervised machine learning pipeline learns representations from BGP graphs derived from BGP data traces. Although this solution achieves good anomaly detection results, the representations learned are specific to the types of anomalies within the training data. To overcome this limitation, in this paper, we propose to learn the representations of normal BGP behaviour in an unsupervised manner using a Graph Auto-Encoder (GAE). This approach ensures that the representations are not limited to the specific set of anomalies included in the training set. These representations associated with a Multi-Layer Perceptron (MLP)-based detector allowed to achieve an accuracy rate of 99% in detecting large-scale events, outperforming previous literature results.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"8 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luke Puppo, Weng-Keen Wong, Bechir Hamdaoui, Abdurrahman Elmaghbub, Lucy Lin
New capabilities in wireless network security have been enabled by deep learning that leverages and exploits signal patterns and characteristics in Radio Frequency (RF) data captured by radio receivers to identify and authenticate radio transmitters. Open-set detection is an area of deep learning that aims to identify RF data samples captured from new devices during deployment (aka inference) that were not part of the training set; i.e. devices that were unseen during training. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. In this paper, we introduce a novel open-set detection approach for RF data-driven device identification that extracts its neural network features from patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) model. Experimental results obtained using real datasets collected from 15 IoT devices, each enabled with LoRa, wireless-Wi-Fi, and wired-Wi-Fi communication protocols, show that our new approach greatly improves the area under the precision-recall curve, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.
{"title":"On the extraction of RF fingerprints from LSTM hidden-state values for robust open-set detection","authors":"Luke Puppo, Weng-Keen Wong, Bechir Hamdaoui, Abdurrahman Elmaghbub, Lucy Lin","doi":"10.52953/mogl1293","DOIUrl":"https://doi.org/10.52953/mogl1293","url":null,"abstract":"New capabilities in wireless network security have been enabled by deep learning that leverages and exploits signal patterns and characteristics in Radio Frequency (RF) data captured by radio receivers to identify and authenticate radio transmitters. Open-set detection is an area of deep learning that aims to identify RF data samples captured from new devices during deployment (aka inference) that were not part of the training set; i.e. devices that were unseen during training. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. In this paper, we introduce a novel open-set detection approach for RF data-driven device identification that extracts its neural network features from patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) model. Experimental results obtained using real datasets collected from 15 IoT devices, each enabled with LoRa, wireless-Wi-Fi, and wired-Wi-Fi communication protocols, show that our new approach greatly improves the area under the precision-recall curve, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"36 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140244800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dmitry Artemasov, Alexander Blagodarnyi, Alexander Sherstobitov, Vladimir Lyashev
In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.
{"title":"On autoregressive and neural methods for massive-MIMO channel de-noising","authors":"Dmitry Artemasov, Alexander Blagodarnyi, Alexander Sherstobitov, Vladimir Lyashev","doi":"10.52953/mckv3131","DOIUrl":"https://doi.org/10.52953/mckv3131","url":null,"abstract":"In modern wireless communication systems, the Multiple-Input Multiple-Output (MIMO) technology allows to greatly increase power efficiency, the serving area, and the overall cell throughput through the use of the antenna array beamforming. Nevertheless, the MIMO systems require accurate channel state knowledge to apply correct precoding. In 5G Time Division Duplex (TDD) systems, the Channel State Information (CSI) is obtained via Sounding Reference Signals (SRS) transmitted by the User Equipment (UE). UEs have limited power capabilities and thus cannot achieve high Uplink (UL) Signal-to-Noise Ratio (SNR) on gNodeB (gNB) in large bandwidth. There are multiple techniques that can be applied to improve the accuracy of Channel Estimation (CE) in noisy conditions. In this paper, we describe a classical method, namely the Vector Autoregression (VAR) with adaptive model order estimation, as well as a modern Deep Neural Network (DNN) approach for the massive-MIMO channel estimation de-noising problem. The developed methods and signal pre and postprocessing steps are described, followed by their performance evaluation in a set of realistic simulations. The designed algorithms provide results outperforming the baseline spatio-temporal windowing approaches by approximately equal to 2dB effective Downlink (DL) Signal-to-Interference-plus-Noise Ratio (SINR) metric in single and multi-user MIMO scenarios. Extensive simulation results demonstrate the robustness of the developed methods to the dynamic channel conditions.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"125 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wi-Fi networks enable user-friendly network connectivity in various environments, ranging from home to enterprise networks. However, vulnerabilities in Wi-Fi implementations may allow nearby adversaries to gain an initial foothold into a network, e.g., in order to attempt further network penetration. In this paper we propose a methodology for the detection of attacks originating from Wi-Fi networks, along with a Wi-Fi Network Intrusion Detection (Wi-Fi-NID) tool, developed to automate the detection of such attacks at 802.11 networks. In particular, Wi-Fi-NID has the ability to detect and trace possible illegal network scanning attacks, which originate from attacks at the Wi-Fi access layer. We extend our initial implementation to increase the efficiency of detection, based on mathematical and statistical function techniques. A penetration testing methodology is defined, in order to discover the environmental security characteristics, related with the current configuration of the devices connected to the 802.11 network. The methodology covers known Wi-Fi attacks such as de-authentication attacks, capturing and cracking WPA-WPA/2 handshake, captive portal and WPA attacks, mostly based on various open source software tools, custom tools, as well as on specialized hardware.
{"title":"Automated Wi-Fi intrusion detection tool on 802.11 networks","authors":"Dimitris Koutras, Panos Dimitrellos, Panayiotis Kotzanikolaou, Christos Douligeris","doi":"10.52953/lhxo3338","DOIUrl":"https://doi.org/10.52953/lhxo3338","url":null,"abstract":"Wi-Fi networks enable user-friendly network connectivity in various environments, ranging from home to enterprise networks. However, vulnerabilities in Wi-Fi implementations may allow nearby adversaries to gain an initial foothold into a network, e.g., in order to attempt further network penetration. In this paper we propose a methodology for the detection of attacks originating from Wi-Fi networks, along with a Wi-Fi Network Intrusion Detection (Wi-Fi-NID) tool, developed to automate the detection of such attacks at 802.11 networks. In particular, Wi-Fi-NID has the ability to detect and trace possible illegal network scanning attacks, which originate from attacks at the Wi-Fi access layer. We extend our initial implementation to increase the efficiency of detection, based on mathematical and statistical function techniques. A penetration testing methodology is defined, in order to discover the environmental security characteristics, related with the current configuration of the devices connected to the 802.11 network. The methodology covers known Wi-Fi attacks such as de-authentication attacks, capturing and cracking WPA-WPA/2 handshake, captive portal and WPA attacks, mostly based on various open source software tools, custom tools, as well as on specialized hardware.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"46 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The primary objective of this paper is to introduce a comprehensive framework designed to automate the assessment of environmental vulnerability status of communication protocols and networked services, within operational contexts. The proposed algorithm leverages the Common Vulnerability Scoring System version 3 (CVSS 3) metrics in conjunction with network security data. The initial step involves the establishment of a network security ontology, which serves to model the environmental attributes associated with the current security posture of communication protocol channels available within an infrastructure. The process commences with the identification and enumeration of all active communication services through a combination of diverse information gathering tools. Subsequently, active network services undergo assessment using a blend of passive scanning and active security analysis tools, which produce the environmental security score. This score can be integrated into vulnerability scoring systems such as CVSS, facilitating the fine-tuning of base CVSS scores, as well as vulnerability mitigation in real-world environments. To validate the proposed framework, we conducted testing across various networks and communication protocols within a controlled environment, thereby offering tangible illustrations for widely-utilized communication protocols.
{"title":"A framework for automating environmental vulnerability analysis of network services","authors":"Dimitris Koutras, Panayiotis Kotzanikolaou, Evangelos Paklatzis, Christos Grigoriadis, Christos Douligeris","doi":"10.52953/tbfn5500","DOIUrl":"https://doi.org/10.52953/tbfn5500","url":null,"abstract":"The primary objective of this paper is to introduce a comprehensive framework designed to automate the assessment of environmental vulnerability status of communication protocols and networked services, within operational contexts. The proposed algorithm leverages the Common Vulnerability Scoring System version 3 (CVSS 3) metrics in conjunction with network security data. The initial step involves the establishment of a network security ontology, which serves to model the environmental attributes associated with the current security posture of communication protocol channels available within an infrastructure. The process commences with the identification and enumeration of all active communication services through a combination of diverse information gathering tools. Subsequently, active network services undergo assessment using a blend of passive scanning and active security analysis tools, which produce the environmental security score. This score can be integrated into vulnerability scoring systems such as CVSS, facilitating the fine-tuning of base CVSS scores, as well as vulnerability mitigation in real-world environments. To validate the proposed framework, we conducted testing across various networks and communication protocols within a controlled environment, thereby offering tangible illustrations for widely-utilized communication protocols.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"32 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The widespread adoption of Internet of Things (IoT) applications in different technical fields has resulted in a significant increase in connected devices while amplifying concerns regarding security and privacy. The presence of security vulnerabilities in various layers of IoT design has emerged as an important issue. Trusted computing, particularly leveraging the Trusted Platform Module (TPM), is seen as a promising approach to counter these vulnerabilities. This paper investigates thoroughly the utilization of TPM technology to enhance node authentication with a focus on energy efficiency. Researchers closely examine each layer to carefully outline an adversary model that is tailored to the IoT ecosystem. The node authentication scheme that is proposed leverages TPM, which has advantages both in terms of processing time and energy. The outcome of this study can be applied to Flying AdHoc Network (FANET) nodes that operate in areas with high levels of traffic, where there are strict safety and reliability standards. Experiments conducted present the essential significance of TPM in ensuring secure node authentication across various application environments. The adoption of TPM technology is validated through rigorous performance assessments, revealing significant improvements in both energy efficiency and security.
{"title":"Optimizing IoT security via TPM integration: An energy efficiency case study for node authentication","authors":"Anestis Papakotoulas, Theodoros Mylonas, Kakia Panagidi, Stathes Hadjiefthymiades","doi":"10.52953/gytk2455","DOIUrl":"https://doi.org/10.52953/gytk2455","url":null,"abstract":"The widespread adoption of Internet of Things (IoT) applications in different technical fields has resulted in a significant increase in connected devices while amplifying concerns regarding security and privacy. The presence of security vulnerabilities in various layers of IoT design has emerged as an important issue. Trusted computing, particularly leveraging the Trusted Platform Module (TPM), is seen as a promising approach to counter these vulnerabilities. This paper investigates thoroughly the utilization of TPM technology to enhance node authentication with a focus on energy efficiency. Researchers closely examine each layer to carefully outline an adversary model that is tailored to the IoT ecosystem. The node authentication scheme that is proposed leverages TPM, which has advantages both in terms of processing time and energy. The outcome of this study can be applied to Flying AdHoc Network (FANET) nodes that operate in areas with high levels of traffic, where there are strict safety and reliability standards. Experiments conducted present the essential significance of TPM in ensuring secure node authentication across various application environments. The adoption of TPM technology is validated through rigorous performance assessments, revealing significant improvements in both energy efficiency and security.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"25 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sokratis Nikolaidis, Stylianos I. Venieris, I. Venieris
Cascade systems, consisting of a lightweight model processing all samples and a heavier, high accuracy model refining challenging samples, have become a widely-adopted distributed inference approach to achieving high accuracy and maintaining a low computational burden for mobile and IoT devices. As intelligent indoor environments, like smart homes, continue to expand, a new scenario emerges, the multi-device cascade. In this setting, multiple diverse devices simultaneously utilize a shared heavy model hosted on a server, often situated within or close to the consumer environment. This work introduces MultiTASC++, a continuously adaptive multi-tenancy-aware scheduler that dynamically controls the forwarding decision functions of devices to optimize system throughput while maintaining high accuracy and low latency. Through extensive experimentation in diverse device environments and with varying server-side models, we demonstrate the scheduler's efficacy in consistently maintaining a targeted satisfaction rate while providing the highest available accuracy across different device tiers and workloads of up to 100 devices. This demonstrates its scalability and efficiency in addressing the unique challenges of collaborative DNN inference in dynamic and diverse IoT environments.
{"title":"MultiTASC++: A continuously adaptive scheduler for edge-based multi-device cascade inference","authors":"Sokratis Nikolaidis, Stylianos I. Venieris, I. Venieris","doi":"10.52953/tbyb6219","DOIUrl":"https://doi.org/10.52953/tbyb6219","url":null,"abstract":"Cascade systems, consisting of a lightweight model processing all samples and a heavier, high accuracy model refining challenging samples, have become a widely-adopted distributed inference approach to achieving high accuracy and maintaining a low computational burden for mobile and IoT devices. As intelligent indoor environments, like smart homes, continue to expand, a new scenario emerges, the multi-device cascade. In this setting, multiple diverse devices simultaneously utilize a shared heavy model hosted on a server, often situated within or close to the consumer environment. This work introduces MultiTASC++, a continuously adaptive multi-tenancy-aware scheduler that dynamically controls the forwarding decision functions of devices to optimize system throughput while maintaining high accuracy and low latency. Through extensive experimentation in diverse device environments and with varying server-side models, we demonstrate the scheduler's efficacy in consistently maintaining a targeted satisfaction rate while providing the highest available accuracy across different device tiers and workloads of up to 100 devices. This demonstrates its scalability and efficiency in addressing the unique challenges of collaborative DNN inference in dynamic and diverse IoT environments.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"38 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In conjunction with the traffic overload of next-generation wireless communication and computer networks, their resource-constrained nature calls for effective methods to deal with the fundamental resource allocation problem. In this context, the Minimum Collisions Assignment (MCA) problem in an interdependent networked system refers to the assignment of a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. Given the interdependent networked system's organization in the form of a graph, there already exists a randomized algorithm that converges with high probability to an assignment of resources having zero collisions when the number of resources is larger than the maximum degree of the underlying graph. In this article, differing from the prevailing literature, we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We introduce two distributed, randomized algorithms that converge in a logarithmic number of rounds to an assignment of resources over the network for which every node has at most a certain number of collisions. The proposed algorithms apply to settings where the available resources at each node are equal to three and two, respectively, while they are executed in a fully-distributed manner without requiring information exchange between the networked nodes.
{"title":"Minimum collisions assignment in interdependent networked systems via defective colorings","authors":"Maria Diamanti, Nikolaos Fryganiotis, Symeon Papavassiliou, Christos Pelekis, Eirini Eleni Tsiropoulou","doi":"10.52953/jndy5511","DOIUrl":"https://doi.org/10.52953/jndy5511","url":null,"abstract":"In conjunction with the traffic overload of next-generation wireless communication and computer networks, their resource-constrained nature calls for effective methods to deal with the fundamental resource allocation problem. In this context, the Minimum Collisions Assignment (MCA) problem in an interdependent networked system refers to the assignment of a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. Given the interdependent networked system's organization in the form of a graph, there already exists a randomized algorithm that converges with high probability to an assignment of resources having zero collisions when the number of resources is larger than the maximum degree of the underlying graph. In this article, differing from the prevailing literature, we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We introduce two distributed, randomized algorithms that converge in a logarithmic number of rounds to an assignment of resources over the network for which every node has at most a certain number of collisions. The proposed algorithms apply to settings where the available resources at each node are equal to three and two, respectively, while they are executed in a fully-distributed manner without requiring information exchange between the networked nodes.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"34 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonio Caruso, Stefano Chessa, Soledad Escolar, Fernando Rincón, Juan Carlos López
Energy neutrality in an energy harvesting Internet of Things (IoT) device ensures continuous operation of the device by trading performance with energy consumption, and a way to achieve this is by adopting a task-based model. In this model, the device embeds several alternative tasks with different ratio energy-cost/quality and a scheduler that, depending on the current energy production and battery level, runs at any time the best task to maximize the performance while guaranteeing energy neutrality. In this context, this work proposes a novel scheduling algorithm that takes into account also the stability of the device, by minimizing the leaps of quality between two consecutive tasks in the scheduling. We show by simulation and by experiments on a low-power IoT platform that the proposed algorithm greatly improves the stability of the device with respect to the state-of-the-art algorithms, with a marginal worsening of the overall quality of the tasks executed.
{"title":"A dynamic programming schedule trading off quality and stability in task allocation for energy-neutral Internet of Things devices harvesting solar energy","authors":"Antonio Caruso, Stefano Chessa, Soledad Escolar, Fernando Rincón, Juan Carlos López","doi":"10.52953/gqza5788","DOIUrl":"https://doi.org/10.52953/gqza5788","url":null,"abstract":"Energy neutrality in an energy harvesting Internet of Things (IoT) device ensures continuous operation of the device by trading performance with energy consumption, and a way to achieve this is by adopting a task-based model. In this model, the device embeds several alternative tasks with different ratio energy-cost/quality and a scheduler that, depending on the current energy production and battery level, runs at any time the best task to maximize the performance while guaranteeing energy neutrality. In this context, this work proposes a novel scheduling algorithm that takes into account also the stability of the device, by minimizing the leaps of quality between two consecutive tasks in the scheduling. We show by simulation and by experiments on a low-power IoT platform that the proposed algorithm greatly improves the stability of the device with respect to the state-of-the-art algorithms, with a marginal worsening of the overall quality of the tasks executed.","PeriodicalId":274720,"journal":{"name":"ITU Journal on Future and Evolving Technologies","volume":"10 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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