Adaptive Quality of Service Control for MQTT-SN

Internet of Things and wireless sensor networks applications are becoming more and more popular nowadays, supported by new communication technologies and protocols tailored to their specific requirements. This paper focuses on improving the performance of a Wireless Sensor Network operated by the MQTT-SN protocol, one of the most popular publish/subscribe protocols for IoT applications. In particular, we propose a dynamic Quality of Service (QoS) controller for the MQTT-SN protocol, capable of evaluating the status of the underlying network in terms of end-to-end delay and packet error rate, reacting consequently by assigning to a node the best QoS value. We design and implement the QoS controller in a simulated environment based on the ns-3 network emulator and we perform extensive experiments to prove its effectiveness compared to a non-controlled scenario. The reported results show that, by controlling the Quality of Service, it is possible to manage effectively the number of packets successfully received by each device and their average latency, to improve the quality of the communication of each end node

Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points

The Internet of Things is in constant growth, with millions of devices used every day in our homes and workplaces to ease our lives. Such a strict coexistence between humans and smart devices makes the latter digital witnesses of our every-day lives through their sensor systems. This opens up to a new area of digital investigation named IoT Forensics, where digital traces produced by smart devices (network traffic, in primis) are leveraged as evidences for forensic purposes. It is therefore important to create tools able to capture, store and possibly analyse easily such digital traces to ease the job of forensic investigators. This work presents one of such tools, named Feature-Sniffer, which is thought explicitly for Wi-Fi enabled smart devices used in Smart Building/Smart Home scenarios. Feature-Sniffer is an add-on for OpenWrt-based access points and allows to easily perform online traffic feature extraction, avoiding to store large PCAP files. We present Feature-Sniffer with an accurate description of the implementation details, and we show its possible uses with practical examples for device identification and activity classification from encrypted traffic produced by IoT cameras. We release Feature-Sniffer publicly for reproducible research.Comment: Paper accepted for publication at IEEE 8th World Forum of Internet of Things (IEEE WF-IOT 2022

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

A Framework for Storage-Accuracy Optimization of IoT Forensic Analysis

The 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

Designing a Forensic-Ready Wi-Fi Access Point for the Internet of Things

Recent advances in the Internet of Things are leading to a proliferation of smart devices in our daily life. Having so many connected devices around us potentially introduces new witnesses that can be a reference for forensic investigations. For these reasons, IoT Forensics has become a popular research area with the goal of extracting information from IoT devices to be used as potential evidence. This work presents Feature-Sniffer , a framework to be installed in Wi-Fi access points with the aim of facilitating the extraction of network traffic information from IoT devices, to be later used for forensic purposes. The tool allows the on-the-fly computation of traffic features from connected IoT devices by using a simple user interface for its configuration. After presenting the tool logic and its implementation details, we present an accurate analysis of the tool computational impact on two different consumer Wi-Fi access points. Finally, we present four different IoT forensics use cases, in which network traffic features extracted with the proposed tool from consumer IoT devices are analyzed with machine learning techniques with the goal of 1) identifying the device producing the traffic; 2) recognizing the activity performed by the user; 3) detecting the user’s passage through a room door; and 4) detecting and classifying user interactions with a smart speaker. We conclude the work by presenting an analysis of possible storage optimization for evidence preservation with the use of lossy compression techniques

COVID-19-related death in patients with alcohol or substance use disorders.

BACKGROUND People with substance or alcohol use disorders (SUDs/AUDs) are likely to be more vulnerable to COVID-19 infection than the general population, but the evidence of COVID-19-related mortality in these patients is unclear. OBJECTIVES The aim of the study was to verify whether patients with AUD and SUD have a higher mortality rate for COVID-19-related mortality compared to the general population. METHOD We performed a follow-up study to assess mortality in 2020 in a cohort of patients diagnosed for the first time with AUDs or SUDs at the Public Health Services in the metropolitan area of Bologna (Northern Italy) from 2009 to 2019. RESULTS SUDs/AUDs patients present an excess mortality with respect to the general population for all causes of death and for COVID-19-related mortality. CONCLUSIONS Our data support the need for prevention strategies in SUDs/AUDs patients such as vaccinations