1,721,168 research outputs found
Radio Map Interpolation using Graph Signal Processing
Get PDFInterpolating a radio map is a problem of great relevance in many scenarios such as network planning, network optimization and localization. In this work such a problem is tackled by leveraging recent results from the emerging field of signal processing on graphs. A technique for interpolating graph structured data is adapted to the problem at hand by using different graph creation strategies, including ones that explicitly consider NLOS propagation conditions. Extensive experiments in a realistic large-scale urban scenario demonstrate that the proposed technique outperforms other traditional methods such as IDW, RBF and model-based interpolation
A Framework for Storage-Accuracy Optimization of IoT Forensic Analysis
No full textThe proliferation of Internet of Things (IoT) devices, coupled with the recent popularity of machine-learning and artificial intelligence has given birth to a new research field named IoT forensics. Such a new field considers network traffic from IoT devices as possible source of evidence for forensic investigations. However, the massive amount of IoT devices and traffic produced makes storage challenging, especially when this is performed on limited-resource edge devices such as e.g., WiFi access points.
This paper proposes a framework to optimize the storage-accuracy trade-offs of IoT forensic analysis tasks.
The goal of the framework is to find the optimal working point in terms of number of features to extract from network traffic and the number of bits used for quantizing each feature, in order to maximize the IoT forensic task accuracy under storage constraints. After presenting the framework, we validate it over two different IoT forensics tasks: IoT device identification and activity recognition from encrypted traffic of IoT cameras. Results show that with low effort it is possible to find the optimal settings to operate to maximize the analysis accuracy under given storage limitations
MAC Address De-Randomization using Multi-Channel Sniffers and Two-Stage Clustering
Get PDFMAC randomization is a widely used technique implemented on most modern smartphones to protect user's privacy against tracking based on Probe Request frames capture. However, there exist weaknesses in such a methodology which may still expose distinctive information, allowing to track the device generating the Probe Requests. Such techniques, known as MAC de-randomization algorithms, generally exploit Information Elements (IEs) contained in the Probe Requests and use clustering methodologies to group together frames belonging to the same device. While effective on heterogeneous device types, such techniques are not able to differentiate among devices of identical type and running the same Operating System (OS). In this paper, we propose a MAC de-randomization technique able to overcome such a weakness. First, we propose a new dataset of Probe Requests captured from devices sharing the same characteristics. Secondly, we observe that the time-frequency pattern of Probe Request emission is unique among devices and can therefore be used as a discriminative feature. We embed such a feature in a two-stage clustering methodology and show through experiments its effectiveness compared to state-of-the-art techniques based solely on IEs fingerprinting. The original dataset used in this work is made publicly available for reproducible research
Unsatisfied today, satisfied tomorrow: A simulation framework for performance evaluation of crowdsourcing-based network monitoring
Get PDFNetwork operators need to continuously upgrade their infrastructures in order to keep their customer satisfaction levels high. Crowdsourcing-based approaches are generally adopted, where customers are directly asked to answer surveys about their experience. Since the number of collaborative users is generally low, network operators rely on Machine Learning models to predict the satisfaction levels/QoE of the users rather than directly measuring it through surveys. Finally, combining the true/predicted users satisfaction labels with information on each user mobility (e.g, which network sites each user has visited and for how long), an operator may reveal critical areas in the network and drive/prioritize investments properly. In this work, we propose an empirical framework tailored to assess the quality of the detection of under-performing cells starting from subjective user experience grades. The framework allows to simulate diverse networking scenarios, where a network characterized by a small set of under-performing cells is visited by heterogeneous users moving through it according to realistic mobility models. The framework simulates both the processes of satisfaction surveys delivery and users satisfaction prediction, considering different delivery strategies and evaluating prediction algorithms characterized by different prediction performance. We use the simulation framework to test empirically the performance of under-performing sites detection in general scenarios characterized by different users density and mobility models to obtain insights which are generalizable and that provide interesting guidelines for network operators
Energy-accuracy trade-offs for hybrid localization using RSS and inertial measurements in wireless sensor networks
No full textThis paper presents a framework for optimizing the trade-off between energy consumption and localization accuracy in hybrid localization systems combining Received Signal Strength (RSS) measurements with inertial ones. The proposed framework aims at finding the optimal operation point that minimizes the radio energy consumption for a desired target accuracy, or equivalently, the one that maximizes the localization accuracy for a given energy budget. To this end, the proposed approach considers the joint optimization of the localization frequency and number of RSS measurements used at each localization round and leverages practical models to predict the energy consumption and the localization accuracy for combined RSS-inertial localization systems. Simulations and real-field experiments are used to demonstrate that, for a given target accuracy, the proposed strategy entails a lower energy consumption than state-of-the-art methods available in the literature
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