The Experts below are selected from a list of 15435 Experts worldwide ranked by ideXlab platform
Andreas F Molisch - One of the best experts on this subject based on the ideXlab platform.
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diversity backpressure scheduling and routing with mutual information accumulation in wireless ad hoc networks
IEEE Transactions on Information Theory, 2016Co-Authors: Hao Feng, Andreas F MolischAbstract:We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple Packet streams (commodities) when only the average channel state information is present, and that only locally. The proposed algorithms are the modifications of the diversity backpressure (DIVBAR) algorithm, under which the Packet whose commodity has the largest “backpressure metric” is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially Received Packet in a separate “partial Packet queue”, thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-renewal mutual information accumulation (RMIA), under which all the receiving nodes clear the Received partial information of a Packet once one or more receiving nodes firstly decode the Packet; and DIVBAR-full mutual information accumulation (FMIA), under which all the receiving nodes retain the partial information of a Packet until the Packet has reached its destination. We characterize the network capacity region with the RMIA transmission scheme and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimal among the policies with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR with respect to the achievable throughput. Moreover, we prove that DIVBAR-FMIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.
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diversity backpressure scheduling and routing with mutual information accumulation in wireless ad hoc networks
arXiv: Networking and Internet Architecture, 2013Co-Authors: Hao Feng, Andreas F MolischAbstract:We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple Packet streams (commodities) when only the average channel state information is present and that only locally. The proposed algorithms are modifications of the Diversity Backpressure (DIVBAR) algorithm, under which the Packet whose commodity has the largest "backpressure metric" is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially Received Packet in a separate "partial Packet queue", thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-RMIA, under which all the receiving nodes clear the Received partial information of a Packet once one or more receiving nodes firstly decode the Packet; and DIVBAR-MIA, under which all the receiving nodes retain the partial information of a Packet until the Packet has reached its destination. We characterize the network capacity region with RMIA and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimum among the polices with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR on the achievable throughput. Moreover, we prove that DIVBAR-MIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.
Elisa Bertino - One of the best experts on this subject based on the ideXlab platform.
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node or link fine grained analysis of Packet loss attacks in wireless sensor networks
ACM Transactions on Sensor Networks, 2016Co-Authors: Daniele Midi, Elisa BertinoAbstract:In wireless sensor networks, Packet losses are often an indicator of possible undergoing attacks. Therefore, security solutions aiming at providing comprehensive protection must include functions for Packet-loss detection. Determining the actual cause of these losses is crucial to a quick and automated reaction to the attack, be it a simple reporting of the attack or a more sophisticated action such as Packet rerouting and retransmission. Packet losses in wireless sensor networks can be caused by either attacks affecting the nodes or attacks focused on the wireless links. The efficacy of the response to such attacks is highly dependent on an accurate identification of the actual attack cause. Therefore, approaches to correctly identifying the cause of Packet losses are needed. The work presented in this article addresses this problem by designing and implementing a fine-grained analysis (FGA) tool that investigates Packet-loss events and reports their most likely cause. Our FGA tool profiles the wireless links between the nodes, as well as their neighborhood, by leveraging resident parameters, such as RSSI and LQI, available within every Received Packet. The design of the system is fully distributed and event-driven, and its low overhead makes it suitable for resource-constrained entities such as wireless motes. We have validated our approach through real-world experiments, showing that our FGA tool is effective in differentiating between the various attacks that may affect nodes and links.
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fine grained analysis of Packet loss symptoms in wireless sensor networks
International Conference on Embedded Networked Sensor Systems, 2013Co-Authors: Bilal Shebaro, Daniele Midi, Elisa BertinoAbstract:In a wireless sensor networks, Packet losses can result from attacks affecting the nodes or the wireless links connecting the nodes. Failure to identify the actual attack can undermine the efficacy of the attack responses. We thus need approaches to correctly identify the cause of Packet losses. In this poster paper, we address this problem by proposing and building a fine-grained analysis (FGA) tool that investigates the causes of Packet losses and reports the most likely cause of these losses. Our tool uses parameters, e.g. RSSI and LQI, transmitted with every Received Packet to profile the links between nodes and their corresponding neighborhood. Through real-world experiments, we have validated our approach and shown that our tool is able to differentiate between the various attacks that may affect the nodes and the links.
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fine grained analysis of Packet loss symptoms in wireless sensor networks
Annual Information Security Symposium, 2013Co-Authors: Bilal Shebaro, Daniele Midi, Elisa BertinoAbstract:Packet losses in a wireless sensor network represent an indicator of possible attacks to the network. Detecting and reacting to such losses is thus an important component of any comprehensive security solution. However, in order to quickly and automatically react to such a loss, it is important to determine the actual cause of the loss. In a wireless sensor networks, Packet losses can result from attacks affecting the nodes or the wireless links connecting the nodes. Failure to identify the actual attack can undermine the efficacy of the attack responses. We thus need approaches to correctly identify the cause of Packet losses. In this paper, we address this problem by proposing and building a fine-grained analysis (FGA) tool that investigates the causes of Packet losses and reports the most likely cause of these losses. Our tool uses parameters, e.g. RSSI and LQI, transmitted with every Received Packet to profile the links between nodes and their corresponding neighborhood. Through real-world experiments, we have validated our approach and shown that our tool is able to differentiate between the various attacks that may affect the nodes and the links.
Morin Cyrille - One of the best experts on this subject based on the ideXlab platform.
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Approches d'apprentissage profond pour la détection en couche physique de télécommunications multi-accès
HAL CCSD, 2021Co-Authors: Morin CyrilleAbstract:Current trends point towards an accelerated augmentation of devices with a desire to access the shared radio spectrum, both due to the continued democratisation and capability augmentation of user facing radio devices, such as cellphones, computers, and especially wearables, but also to the deployment of connected objects and sensors. Technology, protocols, and legislation improvements increase the available frequency bands by opening new channels in the $\si{\giga\hertz}$ range, but the density of devices is nevertheless expected to increase. Multiple access to a shared radio frequency resource leads to situations that are both complex to model, and to tackle with known algorithms, and it is true of detection tasks that arise in the physical layer of a wireless transmission. The class of deep learning algorithms is especially useful in this sort of situation without model, or with non tractable algorithms, as long as a large amount of labelled data is available to train the related neural networks. This thesis aims at adapting the deep learning tool to physical layer detection problems, in successive steps of a decoding chain. First with the problem of detecting the origin of a Received Packet, starting with hardware fingerprinting of a transmitting device, and extending it to a scenario with multiple active devices at the same time, detecting the set of active devices transmitting an explicit codeword. The next step after origin detection is bit detection, to decode transmitted messages. For that, deep learning is used to learn constellations allowing for an efficient bit detection in a multiple-access scenario, namely the two-user uplink NOMA. Data used to train the networks involved in this thesis are gathered both from simulated models, and from experimental implementations in the FIT/CorteXlab software defined radio test-bed.Les tendances actuelles pointent vers une accélération de l'augmentation du nombre d'objets cherchant à accéder au spectre radio, à la fois par la démocratisation des objets grand public, smartphones, ordinateurs, montres connectées,... et par le déploiement d'objets et capteurs connectés. Des avancées technologiques, protocolaires et législatives augmentent les bandes de fréquence disponibles en ouvrant l'accès à la zone des GHz, mais la densité des objets communicant sur le spectre tend quand même à augmenter. L'accès multiple à une ressource radio partagée mène à des situations qui sont à la fois complexes à modéliser et à aborder avec les algorithmes actuels, et c'est particulièrement vrai pour les taches de type détection présentes au niveau des couches physiques des communications sans fil. Les algorithmes d'apprentissage profond sont particulièrement utiles dans ce type de situation, sans modèle ou avec des algorithmes existant peu pratiques, pour peu qu'une grande quantité de données soit disponible pour entraîner les réseaux de neurones. Cette thèse vise à adapter l'outil de l'apprentissage profond aux problèmes de détection de la couche physique, à différentes étapes de la chaîne de décodage. D'abord par le problème de la détection d'origine d'un paquet reçu, commençant par l'identification de caractéristiques matérielles d'un l'objet émetteur, puis étendant ce scénario à un ensemble d'objets actifs simultanément. L'étape suivant la détection de l'origine d'un paquet est la détection des bits, pour décoder les messages transmis. Dans ce cadre, l'apprentissage profond est employé pour apprendre des constellations permettant une détection efficace des bits dans un scénario multi-accès non orthogonal à deux utilisateurs. Les données servant à l'apprentissage des réseaux de neurones impliqués dans cette thèse sont récoltées soit dans des modèles simulés, soit par des expériences implémentées dans l'équipement de radio logicielle FIT/CorteXlab
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Approches d'apprentissage profond pour la détection en couche physique de télécommunications multi-accès
HAL CCSD, 2021Co-Authors: Morin CyrilleAbstract:Current trends point towards an accelerated augmentation of devices with a desire to access the shared radio spectrum, both due to the continued democratisation and capability augmentation of user facing radio devices, such as cellphones, computers, and especially wearables, but also to the deployment of connected objects and sensors. Technology, protocols, and legislation improvements increase the available frequency bands by opening new channels in the GHz range, but the density of devices is nevertheless expected to increase. Multiple access to a shared radio frequency resource leads to situations that are both complex to model, and to tackle with known algorithms, and it is true of detection tasks that arise in the physical layer of a wireless transmission. The class of deep learning algorithms is especially useful in this sort of situation without model, or with non tractable algorithms, as long as a large amount of labelled data is available to train the related neural networks. This thesis aims at adapting the deep learning tool to physical layer detection problems, in successive steps of a decoding chain. First with the problem of detecting the origin of a Received Packet, starting with hardware fingerprinting of a transmitting device, and extending it to a scenario with multiple active devices at the same time, detecting the set of active devices transmitting an explicit codeword. The next step after origin detection is bit detection, to decode transmitted messages. For that, deep learning is used to learn constellations allowing for an efficient bit detection in a multiple-access scenario, namely the two-user uplink NOMA. Data used to train the networks involved in this thesis are gathered both from simulated models, and from experimental implementations in the FIT/CorteXlab software defined radio test-bed.Les tendances actuelles pointent vers une accélération de l'augmentation du nombre d'objets cherchant à accéder au spectre radio, à la fois par la démocratisation des objets grand public, smartphones, ordinateurs, montres connectées,... et par le déploiement d'objets et capteurs connectés. Des avancées technologiques, protocolaires et législatives augmentent les bandes de fréquence disponibles en ouvrant l'accès à la zone des GHz, mais la densité des objets communicant sur le spectre tend quand même à augmenter. L'accès multiple à une ressource radio partagée mène à des situations qui sont à la fois complexes à modéliser et à aborder avec les algorithmes actuels, et c'est particulièrement vrai pour les taches de type détection présentes au niveau des couches physiques des communications sans fil. Les algorithmes d'apprentissage profond sont particulièrement utiles dans ce type de situation, sans modèle ou avec des algorithmes existant peu pratiques, pour peu qu'une grande quantité de données soit disponible pour entraîner les réseaux de neurones. Cette thèse vise à adapter l'outil de l'apprentissage profond aux problèmes de détection de la couche physique, à différentes étapes de la chaîne de décodage. D'abord par le problème de la détection d'origine d'un paquet reçu, commençant par l'identification de caractéristiques matérielles d'un l'objet émetteur, puis étendant ce scénario à un ensemble d'objets actifs simultanément. L'étape suivant la détection de l'origine d'un paquet est la détection des bits, pour décoder les messages transmis. Dans ce cadre, l'apprentissage profond est employé pour apprendre des constellations permettant une détection efficace des bits dans un scénario multi-accès non orthogonal à deux utilisateurs. Les données servant à l'apprentissage des réseaux de neurones impliqués dans cette thèse sont récoltées soit dans des modèles simulés, soit par des expériences implémentées dans l'équipement de radio logicielle FIT/CorteXlab
Hao Feng - One of the best experts on this subject based on the ideXlab platform.
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diversity backpressure scheduling and routing with mutual information accumulation in wireless ad hoc networks
IEEE Transactions on Information Theory, 2016Co-Authors: Hao Feng, Andreas F MolischAbstract:We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple Packet streams (commodities) when only the average channel state information is present, and that only locally. The proposed algorithms are the modifications of the diversity backpressure (DIVBAR) algorithm, under which the Packet whose commodity has the largest “backpressure metric” is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially Received Packet in a separate “partial Packet queue”, thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-renewal mutual information accumulation (RMIA), under which all the receiving nodes clear the Received partial information of a Packet once one or more receiving nodes firstly decode the Packet; and DIVBAR-full mutual information accumulation (FMIA), under which all the receiving nodes retain the partial information of a Packet until the Packet has reached its destination. We characterize the network capacity region with the RMIA transmission scheme and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimal among the policies with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR with respect to the achievable throughput. Moreover, we prove that DIVBAR-FMIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.
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diversity backpressure scheduling and routing with mutual information accumulation in wireless ad hoc networks
arXiv: Networking and Internet Architecture, 2013Co-Authors: Hao Feng, Andreas F MolischAbstract:We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple Packet streams (commodities) when only the average channel state information is present and that only locally. The proposed algorithms are modifications of the Diversity Backpressure (DIVBAR) algorithm, under which the Packet whose commodity has the largest "backpressure metric" is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially Received Packet in a separate "partial Packet queue", thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-RMIA, under which all the receiving nodes clear the Received partial information of a Packet once one or more receiving nodes firstly decode the Packet; and DIVBAR-MIA, under which all the receiving nodes retain the partial information of a Packet until the Packet has reached its destination. We characterize the network capacity region with RMIA and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimum among the polices with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR on the achievable throughput. Moreover, we prove that DIVBAR-MIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.
Jamieson Kyle - One of the best experts on this subject based on the ideXlab platform.
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Cross-Layer Wireless Bit Rate Adaptation
'Association for Computing Machinery (ACM)', 2009Co-Authors: Vutukuru Mythili, Balakrishnan Hari, Jamieson KyleAbstract:This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each Received Packet (even when the Packet has no bit errors), to pick good bit rates. SoftRate's novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2X higher throughput than popular frame-level protocols such as SampleRate and RRAA. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4X higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single Packet-time and its robustness to collision losses.FoxconnNational Science Foundatio
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Cross-layer wireless bit rate adaptation
'Association for Computing Machinery (ACM)', 2009Co-Authors: Vutukuru Mythili, Balakrishnan Hari, Jamieson KyleAbstract:This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each Received Packet (even when the Packet has no bit errors), to pick good bit rates. SoftRate's novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2X higher throughput than popular frame-level protocols such as SampleRate and RRAA. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4X higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single Packet-time and its robustness to collision losses.National Science Foundation (U.S.) (Grant CNS-0721702)National Science Foundation (U.S.) (Grant CNS-0520032)Foxconn International Holdings Ltd
