Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.Comment: Submitted to Balkancom 201

Building up knowledge through passive WiFi probes

Inexpensive WiFi-capable hardware can be nowadays easily used to capture traffic from end users and extract knowledge. Such knowledge can be leveraged to support advanced services like user profiling, device classification. We review here the main building blocks to develop a system based on passive WiFi monitors, that is, cheap and viable sniffers which collect data from end devices even without an explicit association to any Wi-Fi network. We provide an overview of the services which can be enabled by such approach with three practical scenarios: user localization, user profiling and device classification. We evaluate the performance of each one of the three scenarios and highlight the challenges and threats for the aforementioned systems

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

Understanding the WiFi usage of university students

In this work, we analyze the use of a WiFi network deployed in a large-scale technical university. To this extent, we leverage three weeks of WiFi traffic data logs and characterize the spatio-temporal correlation of the traffic at different granularities (each individual access point, groups of access points, entire network). The spatial correlation of traffic across nearby access points is also assessed. Then, we search for distinctive fingerprints left on the WiFi traffic by different situations/conditions; namely, we answer the following questions: Do students attending a lecture use the wireless network in a different way than students not attending a lecture?, and Is there any difference in the usage of the wireless network during architecture or engineering classes? A supervised learning approach based on Quadratic Discriminant Analysis (QDA) is used to classify empty vs. occupied rooms and engineering vs. architecture lectures using only WiFi traffic logs with promising results

Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells.

The Golgi cells have been recently shown to beat regularly in vitro (Forti et al., 2006. J. Physiol. 574, 711-729). Four main currents were shown to be involved, namely a persistent sodium current (I(Na-p)), an h current (I(h)), an SK-type calcium-dependent potassium current (I(K-AHP)), and a slow M-like potassium current (I(K-slow)). These ionic currents could take part, together with others, also to different aspects of neuronal excitability like responses to depolarizing and hyperpolarizing current injection. However, the ionic mechanisms and their interactions remained largely hypothetical. In this work, we have investigated the mechanisms of Golgi cell excitability by developing a computational model. The model predicts that pacemaking is sustained by subthreshold oscillations tightly coupled to spikes. I(Na-p) and I(K-slow) emerged as the critical determinants of oscillations. I(h) also played a role by setting the oscillatory mechanism into the appropriate membrane potential range. I(K-AHP), though taking part to the oscillation, appeared primarily involved in regulating the ISI following spikes. The combination with other currents, in particular a resurgent sodium current (I(Na-r)) and an A-current (I(K-A)), allowed a precise regulation of response frequency and delay. These results provide a coherent reconstruction of the ionic mechanisms determining Golgi cell intrinsic electroresponsiveness and suggests important implications for cerebellar signal processing, which will be fully developed in a companion paper (Solinas et al., 2008. Front. Neurosci. 2:4)

A Visual Sensor Network for Parking Lot Occupancy Detection in Smart Cities

Technology is quickly revolutionizing our everyday lives, helping us to perform complex tasks. The Internet of Things (IoT) paradigm is getting more and more popular and is key to the development of Smart Cities. Among all the applications of IoT in the context of Smart Cities, real-time parking lot occupancy detection recently gained a lot of attention. Solutions based on computer vision yield good performance in terms of accuracy and are deployable on top of visual sensor networks. Since the problem of detecting vacant parking lots is usually distributed over multiple cameras, adhoc algorithms for content acquisition and transmission are to be devised. A traditional paradigm consists in acquiring and encoding images or videos and transmitting them to a central controller, which is responsible for analyzing such content. A novel paradigm, which moves part of the analysis to sensing devices, is quickly becoming popular. We propose a system for distributed parking lot occupancy detection based on the latter paradigm, showing that onboard analysis and transmission of simple features yield better performance with respect to the traditional paradigm in terms of the overall rate-energy-accuracy performance

Unraveling the effect of proliferative stress in vivo in hematopoietic stem cell gene therapy mouse study

The hematopoietic system of patients enrolled in hematopoietic stem cells (HSC) gene therapy (GT) treatments is fully reconstituted upon autologous transplantation of engineered stem cells. HSCs highly proliferate up to full restoration of homeostasis and compete for niche homing and engraftment. The impact of the proliferation stress in HSC on genetic instability remains an open question that cured patients advocate for characterizing long-term safety and efficacy. The accumulation of somatic mutations has been widely used as a sensor of proliferative stress. Vector integration site (IS) can be used as a molecular tool for clonal identity, inherited by all HSC progeny, to uncover lineage dynamics in vivo at single-cell level. Here we characterized at single-clone granularity the proliferative stress of HSCs and their progeny over time by measuring the accumulation of mutations from the DNA of each IS. To test the feasibility of the approach, we set-up an experimental framework that combines tumor-prone Cdkn2a-/- and wild type (WT) mouse models of HSC-GT and molecular analyses on different hematopoietic cell lineages after transplantation of HSCs transduced with genotoxic LV (LV.SF.LTR) or GT-like non-genotoxic LV (SIN.LV.PGK). The Cdkn2a-/- mouse model provided the experimental conditions to detect the accumulation of somatic mutations, since the absence of p16INK4A and p19ARF enhances the proliferative potential of cells that have acquired oncogenic mutations. As expected, mice transplanted with Cdkn2a-/- Lin- cells marked with LV.SF.LTR (N=24) developed tumors significantly earlier compared to mock (N=20, p<0.0001), while mice treated with SIN. LV.PGK (N=23) did not. On the other side, mice that received WT Lin- cells treated with LV.SF.LTR (N=25) or SIN.LV.PGK (N=24) vector have not developed tumors. Given this scenario, we expect that Cdkn2a-/- Lin- cells transduced with LV.SF.LTR are associated with higher mutation rates compared to the SIN.LV.PGK group and wild type control mice. The composition of peripheral blood, lymphoid (B and T) and myeloid compartments was assessed by FACS on samples collected every 4 weeks and IS identification. More than 200,000 IS have been recovered. To identify the presence of somatic mutations, the genomic portions of sequencing reads flanking each different IS were analyzed with VarScan2. The accumulation rates of mutations have been evaluated by our new Mutation Index (MI) which normalizes the number of mutations by clones and coverage. Considering that a large portion of IS has been discarded since not covered by a minimum number of 5 unique reads (genomes), the remaining number of IS contained >90% of reads in each group. The MI increased over time in both LV.SF.LTR groups, with higher values for the Cdkn2a-/-. On the other hand, treatment with SIN.LV.PGK resulted in lower MI in both groups compared to LV.SF.LTR groups, reflecting the higher clonal composition of the cells treated with the SIN.LV.PGK and the phenomenon of insertional mutagenesis in the LV.SF.LTR. Moreover, the higher MI values of the SIN.LV.PGK Cdkn2a-/- group compared with the WT group proved the induction of DNA fragility. Our results showed that the analysis of the accumulation of somatic mutations at single clone unraveled HSC proliferation stress in vivo, combining for the first time the analysis of acquired mutations with IS. We are now applying our model to different clinical trials, and studying HSCs sub- clonal trees by symmetric divisions, previously indistinguishable by IS only. Our study will open the doors to in vivo long-term non-invasive studies of HSC stability in patients