Collecting Channel State Information in Wi-Fi Access Points for IoT Forensics

The Internet of Things (IoT) has boomed in recent years, with an ever-growing number of connected devices and a corresponding exponential increase in network traffic. As a result, IoT devices have become potential witnesses of the surrounding environment and people living in it, creating a vast new source of forensic evidence. To address this need, a new field called IoT Forensics has emerged. In this paper, we present CSI Sniffer, a tool that integrates the collection and management of Channel State Information (CSI) in WiFi Access Points. CSI is a physical layer indicator that enables human sensing, including occupancy monitoring and activity recognition. After a description of the tool architecture and implementation, we demonstrate its capabilities through two application scenarios that use binary classification techniques to classify user behavior based on CSI features extracted from IoT traffic. Our results show that the proposed tool can enhance the capabilities of forensic investigations by providing additional sources of evidence. Wi-Fi Access Points integrated with CSI Sniffer can be used by ISP or network managers to facilitate the collection of information from IoT devices and the surrounding environment. We conclude the work by analyzing the storage requirements of CSI sample collection and discussing the impact of lossy compression techniques on classification performance

Machine-Learning Based Prediction of Next HTTP Request Arrival Time in Adaptive Video Streaming

Continuously monitoring the network activity to proactively recognise possible problems and prevent users QoE degradation is a major concern for network operators, for both mobile radio and home networks. Considering video streaming applications, which generate the majority of overall Internet traffic, monitoring the chunk requests from the video client to the video server is of particular interest, as they not only indicate that a download burst is imminent, but their type (e.g., request of an audio or video chunk) and frequency also allow to estimate which and how much data will be downloaded to the client. In this work, we propose a machine-learning based video streaming traffic monitoring architecture able to i) predict when next uplink request will be issued by the video client and ii) classify the type of next uplink request. We evaluate the system performance on a dataset of more than 900 HTTP adaptive streaming sessions and 15,000 request-response exchanges, where both the predictor of the next request arrival and the request type classifier are fed with lightweight features extracted from encrypted traffic in an online fashion, both in the uplink and downlink directions of the traffic. Results show that i) the system is able to classify the type of a HAS uplink requests with an accuracy greater than 95 % and ii) pipe-lining request type classification and prediction of next request arrival time improves the final prediction performance

Walk this way! An IoT-based urban routing system for smart cities

Future smart cities are expected to change radically the way people live, interact and move in urban environments. This will be possible thanks to the massive amount of data that will be generated by ubiquitously deployed sensor devices through the Internet of Things paradigm. Indeed, solutions able to improve the quality of urban mobility for citizens are of particular interests. As a matter of fact, they are a key objective for many municipal administrations as well as one of the priority themes of the European Commission. In this context, this work proposes an advanced smart urban routing service named SURF, which is specifically thought for pedestrians and cyclists willing to move inside a city. The system allows to retrieve the best route between a source location and a destination according to user-defined objective function (e.g., selecting the route with the best air quality or with the lowest average temperature). This is possible through the interaction with a federation of IoT testbeds, deployed worldwide. This paper comments on the implementation and the evaluation of the proposed system, focusing on both the backend (data retrieval and spatio/temporal data interpolation and forecasting operations) and the front-end (graphical user interface). We assess the performance of several spatial interpolation and temporal prediction models, to understand their relationship with the particular sensor measurements (air pollution, temperature, sound pressure level, etc.). We show through experiments that for what concerns spatial interpolation, Universal Kriging is generally able to perform well across all sensor measurements and can be selected as a generic interpolation strategy. As for temporal prediction, experiments highlight a tradeoff between model accuracy and look-ahead capability. We note that short and mid-term prediction methods show satisfactory performance across all sensor measurements. Finally, subjective and objective experiments demonstrate the positive impact of IoT-based solutions for smart routing on urban citizens

MQTT-ST: A Spanning Tree Protocol for Distributed MQTT Brokers

MQTT, one of the most popular protocols for the IoT, works according to a publish/subscribe pattern in which multiple clients connect to a single broker, generally hosted in the cloud. However, such a centralised approach does not scale well considering the massive numbers of IoT devices forecasted in the next future, thus calling for distributed solutions in which multiple brokers cooperate together. Indeed, distributed brokers can be moved from traditional cloud-based infrastructure to the edge of the network (as it is envisioned by the upcoming MEC technology of 5G cellular networks), with clear improvements in terms of latency, for example. This paper proposes MQTT-ST, a protocol able to create such a distributed architecture of brokers, organised through a spanning tree. The protocol uses in-band signalling (i.e., reuses MQTT primitives for the control messages) and allows for full message replication among brokers, as well as robustness against failures. We tested MQTT-ST in different experimental scenarios and we released it as open-source project to allow for reproducible research

End-to-end delay prediction based on traffic matrix sampling

In this paper we focus on the problem of predicting Quality of Service (QoS), and in particular end-to-end delay, by using traffic matrix samples. To this aim, we study different models based on machine learning as a promising tool to characterize performance in complex computer networks. More specifically, we first provide a simulation platform, based on NS 3 network simulator, in which each Origin-Destination (OD) flow is a mixture of UDP and TCP traffic and we generate useful data for our study. We present three datasets over which we gradually vary the network characteristics: incoming traffic intensity, link capacities, and propagation delays. The datasets are leveraged to train machine learning models, namely Neural Networks and Random Forests, to predict end-to-end delay starting from the knowledge of OD traffic matrix samples. The robustness of these models is evaluated in different test scenarios. Numerical results show that both models are able to accurately forecast the end-to-end delay over all tested datasets, with Random Forests outperforming Neural Networks with gaps as high as 40%