User Perceptions of Smart Home IoT Privacy

Smart home Internet of Things (IoT) devices are rapidly increasing in popularity, with more households including Internet-connected devices that continuously monitor user activities. In this study, we conduct eleven semi-structured interviews with smart home owners, investigating their reasons for purchasing IoT devices, perceptions of smart home privacy risks, and actions taken to protect their privacy from those external to the home who create, manage, track, or regulate IoT devices and/or their data. We note several recurring themes. First, users' desires for convenience and connectedness dictate their privacy-related behaviors for dealing with external entities, such as device manufacturers, Internet Service Providers, governments, and advertisers. Second, user opinions about external entities collecting smart home data depend on perceived benefit from these entities. Third, users trust IoT device manufacturers to protect their privacy but do not verify that these protections are in place. Fourth, users are unaware of privacy risks from inference algorithms operating on data from non-audio/visual devices. These findings motivate several recommendations for device designers, researchers, and industry standards to better match device privacy features to the expectations and preferences of smart home owners.Comment: 20 pages, 1 tabl

Dashbell: A Low-cost Smart Doorbell System for Home Use

Smart doorbells allow home owners to receive alerts when a visitor is at the door, see who the guest is, and communicate with the visitor from a smart device. They greatly improve people's life quality and contribute to the evolution of smart homes. However, the commercial smart doorbells are quite expensive, usually cost more than 190 US dollars, which is a substantial impediment on the pervasiveness of smart doorbells. To solve this problem, we introduce the Dashbell-a budget smart doorbell system for home use. It connects a WiFi-enabled device, the Amazon Dash Button, to a network and enables the home owner to answer the bell triggered by the dash button using a smartphone. The Dashbell system also enables fast fault detection and diagnosis due to its distributed framework.Comment: Accepted by IEEE PerCom 201

IoT2Vec: Identification of Similar IoT Devices via Activity Footprints

We consider a smart home or smart office environment with a number of IoT devices connected and passing data between one another. The footprints of the data transferred can provide valuable information about the devices, which can be used to (a) identify the IoT devices and (b) in case of failure, to identify the correct replacements for these devices. In this paper, we generate the embeddings for IoT devices in a smart home using Word2Vec, and explore the possibility of having a similar concept for IoT devices, aka IoT2Vec. These embeddings can be used in a number of ways, such as to find similar devices in an IoT device store, or as a signature of each type of IoT device. We show results of a feasibility study on the CASAS dataset of IoT device activity logs, using our method to identify the patterns in embeddings of various types of IoT devices in a household.Comment: 5 pages, 4 figure

Portable Smart Home Gateway for Easy Setup of Smart Home Devices

Smart home devices need to be set up to access home WiFi networks and registered to a user account before the device can be used in a home. The multi-step setup procedure can be different for different devices and can take considerable time and user effort. This disclosure describes a portable smart home gateway that simplifies the process of adding new smart home devices to a home network. The smart home gateway can connect to a home WiFi network and to other smart devices via a home networking protocol. New devices can be connected to the smart home gateway via USB (or other suitable connector). Upon connection, the smart home gateway can route requests from the device to the WiFi network and can automate the setup process

Android Powered Smart Mirror Device

In this project, I designed and built a consumer-level Smart Mirror. This amazing device is powered by Android. Additionally, it uses touch screen input, allows for multiple customizable user profiles, has wifi, Bluetooth, a microphone, and much more. The user interface was built from the ground up specifically for the Smart Mirror, so it is one of a kind. With the smart home inevitably in our future, this Smart Mirror takes a huge step in how we interact with technology. Similar to smart watches and other wearables, this device is meant for the casual at a glance notification means of use and something I call Eyes Up Interaction. Click here to see the kick-starter video for the mirror

Bass Response Enhancement For Smart Home Devices

This disclosure describes techniques for obtaining improved bass response from a smart home device that has a small factor. An exciter positioned at the base of the smart home device is utilized to improve the bass response by generating additional sound pressure from the surface that the smart display is placed upon. The type of surface is determined based on transmitting a gated (low pass filtered) impulse. Based on received feedback, the exciter(s) are selectively activated. Inputs from a microphone and an accelerometer are utilized to determine the transfer impedance of the contact surface. A smart amplifier is utilized to drive the exciter based on determined transfer impedance and current and voltage sensing (IV sense). Determination of the type of contact surface and its characteristics is made at a time of switching on of the smart home device and then periodically at predetermined intervals and/or when the smart home device is detected as having been moved

Detecting Rogue Manipulation of Smart Home Device Settings

Smart home devices control a home’s environmental and security settings. This includes devices that control home thermostats, sprinkler systems, light bulbs, and home appliances. Malicious manipulation of the settings of these devices by an outside adversary has caused emotional distress and could even cause physical harm. For example, researchers have reported that there is a rise in domestic abuse perpetrated via smart home devices; victims have reported their thermostat settings being unwittingly manipulated and being locked out of their house due to their smart lock code being changed. Rapid adoption of smart home devices by consumers has led to an urgent need to research mitigation strategies to protect consumers from device takeover. Currently there is not an easy way for home users to detect that a malicious actor is making unwanted changes to their smart home devices. Change requests to smart home devices travel across the network in the form of network packets. Most of time the payloads of the packets are encrypted using strong encryption methods, so it is not possible to simply read the contents of the packet to learn if the packet contains instructions for the smart device to change states. Previous research has successfully trained machine learning algorithms to identify unique network traffic patterns indicative of state change requests sent to smart home devices. This research extends previous research by identifying state change requests of smart home devices made by residents via a smart home device app on their smart phones or tablets. This research identified 13 key attributes of 3,178 encrypted network traffic connections. The attributes were used as features to train three machine learning algorithms to recognize state change requests. Four smart home devices were used chosen from the following categories: 1) devices with simple behaviors (turns on and off), 2) devices with complex behaviors (can be turned on for a set amount of time), and 3) devices that send a large amount of data (i.e. video camera). The success of identifying state change requests over encrypted traffic from a mobile app, combined with previous research that identified state changes sent to the smart home device, allows for the development of a system that could block unwanted state changes that originate from a malicious user located outside of the house. Therefore, this research contributes to the body of knowledge of smart home device security and could be extended to the identification of other networking patterns based on encrypted traffic

Smart Home and Artificial Intelligence as Environment for the Implementation of New Technologies

The technologies of a smart home and artificial intelligence (AI) are now inextricably linked. The perception and consideration of these technologies as a single system will make it possible to significantly simplify the approach to their study, design and implementation. The introduction of AI in managing the infrastructure of a smart home is a process of irreversible close future at the level with personal assistants and autopilots. It is extremely important to standardize, create and follow the typical models of information gathering and device management in a smart home, which should lead in the future to create a data analysis model and decision making through the software implementation of a specialized AI. AI techniques such as multi-agent systems, neural networks, fuzzy logic will form the basis for the functioning of a smart home in the future. The problems of diversity of data and models and the absence of centralized popular team decisions in this area significantly slow down further development. A big problem is a low percentage of open source data and code in the smart home and the AI when the research results are mostly unpublished and difficult to reproduce and implement independently. The proposed ways of finding solutions to models and standards can significantly accelerate the development of specialized AIs to manage a smart home and create an environment for the emergence of native innovative solutions based on analysis of data from sensors collected by monitoring systems of smart home. Particular attention should be paid to the search for resource savings and the profit from surpluses that will push for the development of these technologies and the transition from a level of prospect to technology exchange and the acquisition of benefits.The technologies of a smart home and artificial intelligence (AI) are now inextricably linked. The perception and consideration of these technologies as a single system will make it possible to significantly simplify the approach to their study, design and implementation. The introduction of AI in managing the infrastructure of a smart home is a process of irreversible close future at the level with personal assistants and autopilots. It is extremely important to standardize, create and follow the typical models of information gathering and device management in a smart home, which should lead in the future to create a data analysis model and decision making through the software implementation of a specialized AI. AI techniques such as multi-agent systems, neural networks, fuzzy logic will form the basis for the functioning of a smart home in the future. The problems of diversity of data and models and the absence of centralized popular team decisions in this area significantly slow down further development. A big problem is a low percentage of open source data and code in the smart home and the AI when the research results are mostly unpublished and difficult to reproduce and implement independently. The proposed ways of finding solutions to models and standards can significantly accelerate the development of specialized AIs to manage a smart home and create an environment for the emergence of native innovative solutions based on analysis of data from sensors collected by monitoring systems of smart home. Particular attention should be paid to the search for resource savings and the profit from surpluses that will push for the development of these technologies and the transition from a level of prospect to technology exchange and the acquisition of benefits