1,721,064 research outputs found
Comunicazioni Radio e Monitoraggio - Opportunità e Sfide in Reti Consapevoli del Contesto
No full textThe widespread deployment of wireless communication networks in both indoor and outdoor spaces opens the door to a variety of new opportunities and, at the same time, poses several technical challenges. In this scenario, environment awareness represents both a new appealing side functionality offered by wireless systems, and a promising way to increase the quality of the services provided to the users.
As for the former, wireless devices are intrinsically capable of sensing the propagation environment. The estimation of the wireless channel parameters is continuously performed at the receiver end of the communication link to properly decode the radio signals collected by the antennas. This feature can be exploited to gain knowledge of the surrounding space by detecting the presence of obstacles through the analysis of the multi-path propagation effect. The potential of this application resides in the possibility to realize indoor monitoring systems leveraging
already deployed devices – such as Wi-Fi access points. The scientific community is extensively studying this opportunity, developing algorithms that target different sensing applications such as human-presence detection and activity recognition. The envisioned applications attracted the interest of the Institute of Electrical and Electronics Engineers (IEEE) that established a task force to develop a new version of the Wi-Fi standard, named IEEE 802.11bf. By 2024, the standard will enable the joint provisioning of the communication and the sensing services to the users.
Moving to the second aspect, information about the context where the wireless devices are operating enables reasoned management of the available network resources, guaranteeing an adequate quality of experience to the connected users. This is especially appealing when considering the emerging fifth generation – and beyond – systems, where the network will provide both connectivity and computation support for static and moving terminals, e.g., connected vehicles. The European Telecommunications Standards Institute (ETSI) standardization body is actively
working on this paradigm change – named multi-access edge computing (MEC) – through the standardization of new interoperable architectures. In this context, the network entities need to properly handle the handover of the computation service provisioning to avoid critical discontinuity issues. The exploitation of environmental information in such network management processes provides a clear benefit from both a network and a user perspective, allowing the operators to optimize the energy consumption while assuring a timely fulfillment of the customers' requests.
This thesis makes substantial contributions to the development of new-generation wireless networks, since in the future environmental awareness will be one of the key enablers. The interplay between wireless communications and sensing is discussed by detailing the design and the implementation – together with the assessment of the performance – of brand-new algorithms for environment aware networks. In-depth theoretical analysis is combined with advanced practical implementations and simulative evaluations. The strength of the detailed approaches is the combination of machine and deep learning processing techniques with mathematical models that provide solid foundations for the integration of the two services. Both communication-assisted sensing and sensing-assisted communication applications are presented through practical uses cases. Next-generation Wi-Fi and cellular networks are considered as examples of respectively the former and the latter case, showing the potential of the environment aware paradigm in both indoor and outdoor scenarios.The widespread deployment of wireless communication networks in both indoor and outdoor spaces opens the door to a variety of new opportunities and, at the same time, poses several technical challenges. In this scenario, environment awareness represents both a new appealing side functionality offered by wireless systems, and a promising way to increase the quality of the services provided to the users.
As for the former, wireless devices are intrinsically capable of sensing the propagation environment. The estimation of the wireless channel parameters is continuously performed at the receiver end of the communication link to properly decode the radio signals collected by the antennas. This feature can be exploited to gain knowledge of the surrounding space by detecting the presence of obstacles through the analysis of the multi-path propagation effect. The potential of this application resides in the possibility to realize indoor monitoring systems leveraging
already deployed devices – such as Wi-Fi access points. The scientific community is extensively studying this opportunity, developing algorithms that target different sensing applications such as human-presence detection and activity recognition. The envisioned applications attracted the interest of the Institute of Electrical and Electronics Engineers (IEEE) that established a task force to develop a new version of the Wi-Fi standard, named IEEE 802.11bf. By 2024, the standard will enable the joint provisioning of the communication and the sensing services to the users.
Moving to the second aspect, information about the context where the wireless devices are operating enables reasoned management of the available network resources, guaranteeing an adequate quality of experience to the connected users. This is especially appealing when considering the emerging fifth generation – and beyond – systems, where the network will provide both connectivity and computation support for static and moving terminals, e.g., connected vehicles. The European Telecommunications Standards Institute (ETSI) standardization body is actively
working on this paradigm change – named multi-access edge computing (MEC) – through the standardization of new interoperable architectures. In this context, the network entities need to properly handle the handover of the computation service provisioning to avoid critical discontinuity issues. The exploitation of environmental information in such network management processes provides a clear benefit from both a network and a user perspective, allowing the operators to optimize the energy consumption while assuring a timely fulfillment of the customers' requests.
This thesis makes substantial contributions to the development of new-generation wireless networks, since in the future environmental awareness will be one of the key enablers. The interplay between wireless communications and sensing is discussed by detailing the design and the implementation – together with the assessment of the performance – of brand-new algorithms for environment aware networks. In-depth theoretical analysis is combined with advanced practical implementations and simulative evaluations. The strength of the detailed approaches is the combination of machine and deep learning processing techniques with mathematical models that provide solid foundations for the integration of the two services. Both communication-assisted sensing and sensing-assisted communication applications are presented through practical uses cases. Next-generation Wi-Fi and cellular networks are considered as examples of respectively the former and the latter case, showing the potential of the environment aware paradigm in both indoor and outdoor scenarios
Runtime Integration of Machine Learning and Simulation for Business Processes
No full textRecent research in Computer Science has investigated the use of Deep Learning (DL) techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) model. The main idea of this combination is to maintain a white box simulation model but to complement it with information provided by DL models. State-of-the-art techniques in BPM combine Deep Learning and Discrete event simulation in a post-integration fashion: first an entire simulation is performed, and then a DL model is used to add waiting times and processing times to the events produced by the simulation model. In this paper, we aim at taking a step further by introducing RIMS (Runtime Integration of Machine Learning and Simulation). Instead of complementing the outcome of a complete simulation with the results of predictions "a posteriori", RIMS provides a tight integration of the predictions of the DL model at runtime during the simulation. This runtime-integration enables us to fully exploit the specific predictions thus enhancing the performance of the overall system both w.r.t. the single techniques (Business Process Simulation and DL) separately and the post-integration approach. The runtime-integration enables us to also incorporate the queue as an intercase feature in the DL model, thus further improving the performance in process scenarios where the queue plays an important role
Evaluating the Impact of Channel Feedback Quantization and Grouping in IEEE 802.11 MIMO Wi-Fi Networks
Full text linkRimsTool: a Hybrid Simulator for Business Processes
No full textBusiness Process Simulation represents a powerful instrument for business analysts when analyzing and comparing business processes. Most of the state-of-the-art business process simulators, however, rely on Discrete event simulation, which requires various unrealistic assumptions and simplifications to perform experiments. Predictive Process Monitoring, on the other hand, offers a viable way to complete ongoing traces or to generate entire traces from scratch, via predictions of the next activities and their attributes.
Predictive models, though, are usually based on black-box approaches that make it difficult to reason on what-if scenarios. RIMSTool is a hybrid business process simulator that aims at combining predictive models built from data and Discrete event simulation at runtime in a white-box manner. The proposed tool, thus, is able to exploit the strengths and avoid the limitations of both approaches
Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices
Full text linkRecently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) - a compressed representation of the CFR - from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package.To be presented at ACM WiNTECH, Madrid, Spain, October 6, 202
WHACK: Adversarial Beamforming in MU-MIMO Through Compressed Feedback Poisoning
Full text linkMulti-user MIMO is a key component of modern wireless networks. As such, investigating the related security weaknesses is a compelling necessity. A major issue unveiled by existing work is that adversaries can “poison” the channel information feedback reported to the beamformer to decrease the performance experienced by a legitimate user. Prior work, however, assumes that the feedback is reported in an uncompressed fashion, which is not the case in current wireless standards such as Wi-Fi or 5G. In this work, we first show that assuming uncompressed feedback leads to overestimating the attack effectiveness by up to 60%. Next, we formulate ACFP (Adversarial Compressed Feedback Problem), a novel non-convex constrained optimization problem to find the compressed feedback that maximizes a victim’s bit error rate (BER) while satisfying maximum power constraints. We propose WHACK (Wireless Harmful Adversarial Compressed feedbacK), a new algorithm to solve ACFP and find the malicious compressed feedback based on the convexity of the objective function and constraint using a nonlinear conjugate gradient method. WHACK has been prototyped and extensively evaluated with off-the-shelf Wi-Fi devices. Experimental results show that it maximizes the victim’s BER, while modifying less than 60% of the feedback. Our dataset and code are available
Multiperson Continuous Tracking and Identification From mm-Wave Micro-Doppler Signatures
Full text linkIn this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical towards achieving robustness and accuracy in multi-user settings. Our technique is tested both on a single-target public dataset, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase
IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices
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