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

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

MQTT+: Enhanced syntax and broker functionalities for data filtering, processing and aggregation

In the last few years, the Message Queueing Telemetry Transport (MQTT) publish/subscribe protocol emerged as the de facto standard communication protocol for IoT, M2M and wireless sensor networks applications. Such popularity is mainly due to the extreme simplicity of the protocol at the client side, appropriate for low-cost and resource-constrained edge devices. Other nice features include a very low protocol overhead, ideal for limited bandwidth scenarios, the support of different Quality of Services (QoS) and many others. However, when an edge device is interested in performing processing operations over the data published by multiple clients, the use of MQTT may result in high network bandwidth usage and high energy consumption for the end devices, which is unacceptable in resource constrained scenarios. To overcome these issues, we propose in this paper MQTT+, which provides an enhanced protocol syntax and enrich the pub/sub broker with data filtering, processing and aggregation functionalities. MQTT+ is implemented starting from an open source MQTT broker and evaluated in different application scenarios

Demonstrating MQTT+: An advanced broker for data filtering, processing and aggregation

The Message Queueing Telemetry Transport (MQTT) publish/subscribe protocol is the de facto standard at the application layer for IoT, M2M and wireless sensor networks applications. This demonstration showcases MQTT+, an advanced version of MQTT which provides an enhanced protocol syntax and enriches the broker with data filtering, processing and aggregation functionalities. Such features are ideal in all those applications in which edge devices are interested in performing processing operations over the data published by multiple clients, where using the original MQTT protocol would result in unacceptably high network bandwidth usage and energy consumption for the edge devices. MQTT+ is implemented starting from an open source MQTT broker and evaluated in different application scenarios which are demonstrated live using the Node-RED IoT prototyping framework