Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers-a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts' rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.
We present Peekaboo, a new privacy-sensitive architecture for smart homes that leverages an in-home hub to pre-process and minimize outgoing data in a structured and enforceable manner before sending it to external cloud servers. Peekaboo's key innovations are (1) abstracting common data pre-processing functionality into a small and fixed set of chainable operators, and (2) requiring that developers explicitly declare desired data collection behaviors (e.g., data granularity, destinations, conditions) in an application manifest, which also specifies how the operators are chained together. Given a manifest, Peekaboo assembles and executes a pre-processing pipeline using operators pre-loaded on the hub. In doing so, developers can collect smart home data on a need-to-know basis; third-party auditors can verify data collection behaviors; and the hub itself can offer a number of centralized privacy features to users across apps and devices, without additional effort from app developers. We present the design and implementation of Peekaboo, along with an evaluation of its coverage of smart home scenarios, system performance, data minimization, and example built-in privacy features.
Enabled by various sensing and data networking devices, modern buildings are beginning to generate extraordinary amounts of sensory data. The organization and availability of this data is currently a challenge, especially for researchers who seek to devise intelligent data-driven methods for energy efficient use of building systems. Most current solutions tend to be ad-hoc and proprietary, and thus do not support mechanisms for easy data acess and sharing.
Managing power consumption and improving energy efficiency is a key driver in the design of computing devices today. This is true for both battery powered mobile devices as well as mains-powered desktop PCs and servers. In case of mobile devices, the focus of optimization is on energy efficiency to maximize battery lifetime. In case of mains- powered devices, we seek to optimize power consumption to reduce energy costs, thermal and environmental concerns. Traditionally, there are two main mechanisms to improve energy efficiency in systems: slowdown techniques that seek to reduce processor speed or radio power against the rate of work done, and shutdown techniques that seek to shut down specific components or subsystems - such as processor, radio, memory - to reduce power used by these components when not in use. The adverse effect of using these techniques is either reduced performance (e.g., increase in latency) and/or usability or loss of functionality. The thesis behind this dissertation is that improved energy efficiency can be achieved through system architectures that seek to design and exploit collaboration among heterogeneous but functionally similar subsystems. For instance, multiple radio interfaces with different power/performance characteristics can collaborate to provide an energy- efficient wireless communication subsystem. Furthermore, we show that in systems where such heterogeneity is not naturally present, we can introduce heterogeneous components to improve overall energy efficiency. We show that using collaboration, individual subsystems and even entire platforms can be shut down more aggressively to reduce energy consumption, while reducing adverse impacts on performance or usability. We have used collaboration to do energy efficient operation in several contexts. For battery powered mobile devices we show that wireless radios are the dominant power consumers, and then describe several techniques that use various heterogeneous radios present on these devices in a collaborative manner to improve their battery lifetime substantially, on average by two to three times and in some cases up to 8 times. First we present Cell2Notify , a technique in which a lower power radio is used purely to wakeup a higher power radio. Next, we present CoolSpots and SwitchR , systems that build a hierarchy of collaborative radios to choose the most appropriate radio interface, taking into account application characteristics as well as various energy and performance metrics. In the case of wall-powered desktop and laptop Personal Computers (PCs) we show that the dominant power consumers are the processors themselves. We then describe Somniloquy , an architecture that augments a PC with a separate low power secondary processor that can perform some of the functions of the host PC on its behalf. We show that by using the primary processor (i.e. the PC) collaboratively with the secondary processor we can shut down PCs opportunistically, and as a result reduce the overall energy consumption by up to 80% in most cases
As embedded computers of all shapes and sizes are connected to the Internet en masse, the opportunity to exploit their combined capabilities and power is an attractive engineering challenge. Working out the kinks associated with heterogeneous data, lack of standardization, and interoperability challenges will enable an entirely new computing paradigm.
Batteryless energy-harvesting sensing systems are attractive for low maintenance but face challenges in real-world applications due to the low quality of service from sporadic and unpredictable energy availability. To overcome this challenge, recent data-driven energy management techniques optimize energy usage to maximize application performance even in low harvest scenarios by learning energy availability patterns in the environment. These techniques require prior knowledge of the environment in which the sensor nodes are deployed to work correctly. In the absence of historical data, the application performance deteriorates. We present an approach that leverages meta reinforcement learning to increase the application performance of newly deployed batteryless sensor nodes without historical data. Our system, called Marble, exploits information from other sensor node locations to expedite the learning of newly deployed sensor nodes, and improves the application performance in the initial days of deployment. For more details, we refer readers to the full paper of Marble [5].
Thermostats are primary interfaces for occupants of office buildings to express their comfort preferences. However, standard thermostats are often ineffective due to inaccessibility, lack of information, or limited responsiveness, leading to occupant discomfort. Software thermostats based on web or smartphone applications provide alternative interfaces to occupants with minimal deployment cost. However, their usage and effectiveness have not been studied extensively in real settings. In this paper we present Genie, a novel software-augmented thermostat that we deployed and studied at our university over a period of 21 months. Our data shows that providing wider thermal control to users does not lead to system abuse and that the effect on energy consumption is minimal while improving comfort and energy awareness. We believe that increased introduction of software thermostats in office buildings will have important effects on comfort and energy consumption and we provide key design recommendations for their implementation and deployment.
