A data collection infrastructure is vital for generating sufficient amounts and diversity of data necessary for developing algorithms in home-based health monitoring. However, the manageability—deployment and operation efforts—of such an infrastructure has long been overlooked. Even a small size of a dozen homes may incur enormous manual efforts on the research team. In this article, we present Proteus, an easily managed infrastructure designed to automate much of the work in deploying and operating such systems. We develop new components and combine with mature technologies to minimize the human efforts required. Proteus includes: 1) scalable, continuous deployment, operation, and update of devices with automatic bootstrapping; 2) automatic fault and error monitoring and recovery with watchdogs and LED feedback, and complementary edge and cloud storage backups; and 3) an easy-to-use data-agnostic pipeline for integrating new modalities. We demonstrate our system’s robustness through different sets of experiments: three sensor nodes (SNs) running for 24 days sending data (17.4 Mb/s aggregate rate), 10 SNs for 14 days (58 Mb/s aggregate rate), and 32 emulated sensors (419.2 Mb/s aggregate rate). All such experiments have data loss rates less than 1%. Further we reduce human efforts by 25-fold and code required for adding new data modality by 25-fold. We also share our experience and lessons learned during the design, development, and pilot deployment of Proteus. Our results show that Proteus is a promising solution for enabling research teams to effectively manage home-based health monitoring at small to medium sizes.

With the plethora of wireless devices in limited spaces running multiple WiFi standards (802.11 a/b/g/n/ac), gaining an understanding of network latency, loss, and medium utilization becomes extremely challenging. Microsecond timing fidelity with protocols is critical for network performance evaluation and design; such fine-grained timing offers insights that simulations cannot deliver (e.g., precise timing through hardware and software implementations). However, currently there is no suitable efficient, lightweight tool for such purposes. This paper introduces Aletheia, an open-source tool that enables users to select their interested attributes in WiFi frames, quantify and visualize microsecond granularity medium utilization using low-cost commodity edge devices for easy deployment. Aletheia uses selective attribute extraction to filter frame fields; it reduces data storage and CPU overhead by 1 and 2 orders of magnitude, respectively, compared to existing tools (e.g., Wireshark, tshark). It provides flexible tagging and visualization features to examine the medium and perform different analysis to understand protocol behavior under different environments. We use Aletheia to capture and analyze 120M frames in 24 hours at 4 locations to demonstrate its value in production and research network performance evaluation and troubleshooting. We find that WiFi management beacons can consume medium heavily (up to 40%); the common practice of categorizing networks based on environment types (e.g., office vs. home) is problematic, calling for a different evaluation methodology and new designs.

Scalable, fine-grained access control for Internet-of-Things is needed in enterprise environments, where tens of thousands of users need to access smart objects which have a similar or larger order of magnitude. Existing solutions offer all-or-nothing access, or require all access to go through a cloud backend, greatly impeding access granularity, robustness and scale. In this paper, we propose Heracles, an IoT access control system which achieves robust, fine-grained access control and responsive execution at enterprise scale. Heracles adopts a capability-based approach using secure, unforgeable tokens that describe the authorizations of users, to either individuals or collections of objects in single or bulk operations. It has a 3-tier architecture to provide centralized policy and distributed execution desired in enterprise environments. Extensive analysis and performance evaluation on a testbed prove that Heracles achieves fine-grained access control and responsive execution at enterprise scale. Compared with systems using access control list, Heracles eliminates or reduces by 10x-100x the updating overhead under frequent changes of subject memberships and policies. Besides, Heracles achieves responsive execution: it takes 0.57 second to access 18 objects which are scattered 1-9 hops away, and execution on a 1-hop or 2-hop object needs only 0.07 or 0.13 second respectively.

Peer communication among edge devices (e.g., mobiles, vehicles, IoT and drones) is frequently data-centric: most important is obtaining data of desired content from suitable nodes; who generated or transmitted the data matters much less. Typical cases are robust one-to-many data sharing: e.g., a vehicle sending weather, road, position and speed data streams to nearby cars continuously. Unfortunately, existing address-based wireless communication is ill-suited for such purposes. We propose V-MAC, a novel data-centric radio that provides a pub/sub abstraction to replace the point-to-point abstraction in existing radios. It filters frames by data names instead of MAC addresses, thus eliminating complexities and latencies in neighbor discovery and group maintenance in existing radios. V-MAC supports robust, scalable and high rate multicast with consistently low losses across receivers of varying reception qualities. Experiments using a Raspberry Pi and a commodity WiFi dongle based prototype show that V-MAC reduces loss rate from WiFi broadcast's 50-90% to 1-3% for up to 15 stationary receivers, 4-5 moving people, and miniature and real vehicles. It cuts down filtering latency from 20μs in WiFi to 10μ s for up to 2 million data names, and improves cross stack latency 60-100× for TX/RX paths. We have ported V-MAC to 4 major WiFi chipsets (including 802.11 a/b/g/n/ac radios), 6 different platforms (Android, embedded and FPGA systems), 7 Linux kernel versions, and validated up to 900Mbps multicast data rate and interoperation with regular WiFi. We will release V-MAC as a mature, reusable asset for edge computing research.

The lack of digital floor plans is a huge obstacle to pervasive indoor location based services (LBS). Recent floor plan construction work crowdsources mobile sensing data from smartphone users for scalability. However, they incur long time (e.g., weeks or months) and tremendous efforts in data collection. In this paper, we propose BatMapper, which explores a previously untapped sensing modality-acoustics-forfast, fine grained, and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal, and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Experiments in real buildings show BatMapperachieves 1 - 2 cm distance accuracy in ranges up around 4 m; a 2~3 minute walk generates fine grained corridor shapes, detects doors at 92 percent precision and 1~2 mlocation error at 90-percentile; and tens of seconds of measurement gestures produce room geometry with errors <; 0:3 m at 80-percentile, at 1 - 2 orders of magnitude less data amounts and user efforts.

Scalable, fine-grained access control for Internet-of-Things is needed in enterprise environments, where thousands of subjects need to access possibly one to two orders of magnitude more objects. Existing solutions offer all-or-nothing access, or require all access to go through a cloud backend, greatly impeding access granularity, robustness and scale. In this paper, we propose Heracles, an IoT access control system that achieves robust, fine-grained access control at enterprise scale. Heracles adopts a capability-based approach using secure, unforgeable tokens that describe the authorizations of subjects, to either individual or collections of objects in single or bulk operations. It has a 3-tier architecture to provide centralized policy and distributed execution desired in enterprise environments, and delegated operations for responsiveness of resource-constrained objects. Extensive security analysis and performance evaluation on a testbed prove that Heracles achieves robust, responsive, fine-Qrained access control in large scale enterprise environments.

Data-centric networks provide content instead of address (what vs. where) based communication primitives, and have been argued to be the proper candidate for data dissemination in high mobility vehicular networks (e.g., delivering road side accident video clips to affected drivers in both directions). However, current Medium Access Control (MAC) layers filter incoming frames based on destination addresses, not content. The data-centric network community has resorted to MAC broadcast, with high and greatly varying frame loss rates. We propose V-MAC, a data-centric MAC layer that filters frames by content. It supports one to many multicast at MAC level, and ensures a uniform and controllable small frame loss rate across all receivers, despite their varying reception qualities. We have created a V-MAC prototype using Raspberry Pis and WiFi dongles. Experiments under extremely noisy environment show that it reduces frame loss from 50% (broadcast) to less than 10%, and consistently among multiple receivers.

This demo presents BatMapper, an acoustics sensing technology for fast, fine-grained and low cost floor plan construction. BatMapper operates by emitting sound signal and capturing its reflections by two microphones on smartphones. We develop robust probabilistic echo-object association and outlier removal algorithms to identify the correspondence between distances and objects, thus the geometry of corridors. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Additionally, we leverage structure cues in indoor environments for user trace calibration. The demo will enable any person to hold the smartphone and walk along a corridor to map the corridor shape and detect doors in real-time.

The lack of digital floor plans is a huge obstacle to pervasive indoor location based services (LBS). Recent floor plan construction work crowdsources mobile sensing data from smartphone users for scalability. However, they incur long time (e.g., weeks or months) and tremendous efforts in data collection, and many rely on images thus suffering technical and privacy limitations. In this paper, we propose BatMapper, which explores a previously untapped sensing modality -- acoustics -- for fast, fine grained and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Experiments in real buildings show BatMapper achieves 1-2cm distance accuracy in ranges up around 4m; a 2-3 minute walk generates fine grained corridor shapes, detects doors at 92% precision and 1~2m location error at 90-percentile; and tens of seconds of measurement gestures produce room geometry with errors <0.3m at 80-percentile, at 1-2 orders of magnitude less data amounts and user efforts.

Continuous tracking of the device location in 3D space is a popular form of user input, especially for virtual/augmented reality (VR/AR), video games and health rehabilitation. Conventional inertial based approaches are well known for inaccuracy caused by large error drifts. Computer vision approaches can produce accuracy tracking but have privacy concerns and are subject to lighting conditions and computation complexity. Recent work exploits accurate acoustic distance measurements for high precision tracking. However, they require additional hardware (e.g., multiple external speakers), which adds to the costs and installation efforts, thus limiting the convenience and usability. In this paper, we propose BatTracker, which incorporates inertial and acoustic data for robust, high precision and infrastructure-free tracking in indoor environments. BatTracker leverages echoes from nearby objects and uses distance measurements from them to correct error accumulation in inertial based device position prediction. It incorporates Doppler shifts and echo amplitudes to reliably identify the association between echoes and objects despite noisy signals from multi-path reflection and cluttered environment. A probabilistic algorithm creates, prunes and evolves multiple hypotheses based on measurement evidences to accommodate uncertainty in device position. Experiments in real environments show that BatTracker can track a mobile device's movements in 3D space at sub-cm level accuracy, comparable to the state-of-the-art infrastructure based approaches, while eliminating the needs of any additional hardware.