We are currently working on using wireless communication to help make electricity accessible in underserved markets. Our hardware is currently being used in a wirelessly managed microgrid deployment in rural Les Anglais, Haiti. The system consists of a cloud-based monitoring and control service, a local embedded gateway infrastructure and a mesh network of wireless smart meters deployed at 52 buildings. Each smart meter device has an 802.15.4 radio that enables remote monitoring and control of electrical service. The meters communicate over a scalable multi-hop TDMA network back to a central gateway that manages load within the system. The gateway also provides an 802.11 interface for an on-site operator and a cellular modem connection to a cloud-backend that manages and stores billing and usage data. The cloud backend allows occupants in each home to pre-pay for electricity at a particular peak power limit using a text messaging service. The system activates each meter within seconds and locally enforces power limits with provisioning for theft detection. We believe that this fine-grained micro-payment model can enable sustainable power in otherwise unfeasible areas.
[ Website | IPSN 2014 | ICCPS 2016 | Sparkmeter | Slides]
Visual Light Landmarks
The omnipresence of indoor lighting makes it an ideal vehicle for pervasive communication with mobile devices. In this paper, we present a communication scheme that enables interior ambient LED lighting systems to send data to mobile devices using either cameras or light sensors. By exploiting rolling shutter camera sensors that are common on tablets, laptops and smartphones, it is possible to detect high-frequency changes in light intensity reflected off of surfaces and in direct line-of-sight of the camera. We present a demodulation approach that allows smartphones to accurately detect frequencies as high as 8kHz with 0.2kHz channel separation. In order to avoid humanly perceivable flicker in the lighting, our system operates at frequencies above 2kHz and compensates for the non-ideal frequency response of standard LED drivers by adjusting the light’s duty-cycle.
[ Website | IPSN 2014 | Slides]
Indoor Ranging for Mobile Devices
We are developing an indoor ultrasonic ranging technique that can be used to localize modern mobile devices like smartphones and tablets. The method uses a communication scheme in the audio bandwidth just above the human hearing frequency range where mobile devices are still sensitive.
[ Website | SenSys 2012 | Slides]
Sensor Andrew: A Living Laboratory for Infrastructure Sensing
Sensor Andrew is a multi-disciplinary campus-wide scalable sensor network that is designed to host a wide range of sensing and low-power applications. The goals of Sensor Andrew are to support ubiquitous large-scale monitoring and control of infrastructure in a way that is extensible, easy to use, and provides security while maintaining privacy. Target applications currently being developed on the Project Anonymous network include infrastructure monitoring, first-responder support, quality of life for the disabled, water distribution systems monitoring and optimization, building power monitoring and control, social networking, and biometric sensors for campus security. Sensor Andrew is now powered by the Mortar.io platform.
[Project Website][Tech Report][IBM Journal][IPSN Demo]
Drone-RK: A Real-Time Distributed UAV Platform
Drone-RK is an open-source real-time distributed UAV development infrastructure that focuses on the software infrastructure required for self-contained autonomous UAV application development. Drone-RK currently runs on the Parrot AR.Drone hardware platform. Drone-RK provides Resource Kernel (RK) extensions to the standard Linux kernel that provide real-time scheduling extensions such that tasks in the system can specify their resource demands such that the operating system can provide timely, guaranteed and controlled access to system resources (CPU, network, sensors and actuators). The Drone-RK development platform provides APIs for local sensing, control and processing as well as various demonstration applications. In order to support rich autonomous behaviors, the platform provides hooks to incorporate additional hardware components (GPS, digital compasses, ultrasonic ranging, etc).
[Project Website] [ICCPS 2012]
Distributed time-series data management
As sensor networks gain traction and begin to scale, we will be increasingly faced with challenges associated with managing large-scale time-series data. We present a cloud-to-edge partitioned architecture called Respawn that is capable of serving large amounts of time-series data from a continuously updating datastore with access latencies low enough to support interactive real-time visualization. Respawn targets sensing systems where resource-constrained edge node devices may only have limited or intermittent network connections linking them to a cloud-backend. The cloud-backend provides aggregate storage and transparent dispatching of data queries to edge node devices. Data is downsampled as it enters the system creating a multi-resolution representation capable of low- latency range-base queries. Lower-resolution aggregate data is automatically migrated from edge nodes to the cloud-backend both for improved consistency and caching. In order to further mask latency from users, edge nodes automatically identify and migrate blocks of data that contain statistically interesting features.
[Website | RTSS 2013]
WaterBot is a real-time conductivity sensor and data logger, designed to enable inexpensive and convenient monitoring of well and watershed systems with high temporal frequency and high spatial density. Conductivity of water is an indirect measure of Total Disolved Solids (TDS), which is a frontline indicator of water quality. Collecting baseline data and setting up mobile sensor networks in areas impacted by water source contamination is essential for citizen science and civic monitoring projects.
The CMUcam4 is a fully programmable embedded computer vision sensor. In the latest version of the CMUcam, we simplified the design by removing the FIFO memory buffer. The main processor is the Parallax P8X32A (Propeller Chip) connected to an OmniVision 9665 CMOS camera sensor module. For more information please see CMUcam4 wiki
Nano-RK: A Real-Time Wireless Sensor Network Operating System
Nano-RK is a reservation-based real-time operating system (RTOS) with multi-hop networking support for use in wireless sensor networks. Nano-RK currently runs on the FireFly Sensor Networking Platform as well as the MicaZ motes. It includes a light-weight embedded resource kernel (RK) with rich functionality and timing support using less than 2KB of RAM and 16KB of ROM. Nano-RK supports fixed-priority preemptive multitasking for ensuring that task deadlines are met, along with support for CPU, network, as well as, sensor and actuator reservations. Tasks can specify their resource demands and the operating system provides timely, guaranteed and controlled access to CPU cycles and network packets. Together these resources form virtual energy reservations that allows the OS to enforce system and task level energy budgets.
[Project Website] [RTSS 05 | Slides ] [RHS | Slides]
Low-power Clock Synchronization using AC Power Lines
In this work, we present a novel low-power hardware module for achieving global clock synchronization by tuning to the magnetic field radiating from existing AC power lines. This signal can be used as a global clock source for battery-operated sensor nodes to eliminate drift between nodes over time even when they are not passing messages. With this scheme, each receiver is frequency-locked with each other, but there is typically a phase-offset between them. Since these phase offsets tend to be constant, a higher-level compensation protocol can be used to globally synchronize a sensor network.
[Project Website] [SenSys 09 | Slides]
Building Power Monitoring and Control
The Sensor Andrew Gateway Agent (SAGA) Sensor Network Dashboard is a web interface that provides users with a way to view, actuate and manage a subnet of wireless sensor nodes. The front-end is designed to run locally on a router acting as a gateway between the sensor network and the Internet. Information collected from sensor nodes is stored locally for quick retrieval even if outside network connectivity is lost or unavailable. When network connectivity is available, data can be pushed through use of the Sensor Andrew infrastructure to external agents which can for example archive historical events or perform higher-level processing. This also enables secure bi-directional communication to gateways behind firewalls or with dynamic IP address like those found in broadband connected homes. The web interface allows devices to be easily configured with aliases and grouped together based on type. Individual and groups of sensors can be plotted to show relative comparisons of sensor values. For example, plotting a group of energy metering devices will show a comparison of which devices are consuming what fraction of the energy.
[Project Website] [IPSN 09 Demo]
Rate-Harmonized Scheduling for Saving Energy
Many modern power-aware processors and microcontrollers have built-in support for active, idle and sleep operating modes. In sleep mode, substantially more energy savings can be obtained but it requires a significant amount of time to switch into and out of that mode. Hence, a significant amount of energy is lost due to idle gaps between executing tasks that are shorter than the required time for the processor to enter the sleep mode. We present a technique called Rate- Harmonized Scheduling that naturally clusters task execution such that processor idle times are lumped together. We next introduce the Energy-Saving Rate-Harmonized Scheduler which guarantees that every idle duration on the processor can be used to put the processor into sleep mode. This property can be used to even eliminate the idle power mode in processors but nevertheless it is predictable, analyzable, and saves more energy.
[RTSS 08 | Slides]
FireFly Real-Time Sensor Networking Platform (v1 & v2)
The FireFly Sensor Networking Platform is a low-cost low-power hardware platform. In order to better support real-time applications, the system is built around maintaining global time synchronization. The main Firefly board uses an Atmel ATmega1281 8-bit micro-controller with 8KB of RAM and 128KB of ROM along with TI's (formerly Chipcon) CC2420 IEEE 802.15.4 standard-compliant radio transceiver for communication. The maximum packet size supported by 802.15.4 is 128 bytes and the maximum raw data rate is 250Kbps.
[Project Website][SECON 06][RTSJ 06]
Previous Projects (no longer active)
The goal of the CMUcam project is to provide simple vision capabilities to small embedded systems in the form of an intelligent sensor. The CMUcam3 extends upon this idea by providing a flexible and easy to use open source development environment that complements a low cost hardware platform. The CMUcam3 is an ARM7TDMI based fully programmable embedded computer vision sensor. The main processor is the NXP LPC2106 connected to an Omnivision CMOS camera sensor module. Custom C code can be developed for the CMUcam3 using a port of the GNU toolchain along with a set of open source libraries and example programs. Executables can be flashed onto the board using the serial port with no external downloading hardware required.
[Project Website][Technical Report]
FireFly Mosaic: Visual-Enabled Wireless Sensor Networks
FireFly Mosiac is a Vision-Enabled sensor network comprised of smart cameras that can perform distributed image processing tasks. In this work we demonstrate a system that can monitor peopleâ€™s daily activities in the home. The system automatically combines information extracted from multiple overlapping cameras to recognize various regions in the house where particular activities frequently occur. Examples of such activities include washing dishes, preparing food, eating dinner, sitting on the couch or sleeping. Once these daily activity clusters are defined, the system builds a model which tracks the duration and transition frequency between various tasks. This information can be monitored over time to detect changes in behavior, or it can be used to give contextual clues to other in-home systems. For example, a fall detection system could use this type of context information to lower its probability of triggering if the user is sleeping or has a visitor in a nearby location.
[RTSS 07 | Slides]
Micro-climate Enhanced RF Localization
We propose micro-climate sensing as an effective means of enhancing conventional RF-based localization. Our system targets people tracking applications in dynamic indoor environments, such as nursing homes, hospitals and office spaces that require simple deployment and where conventional RF tracking may suffer from timevarying signal attenuation or dropped packets. RF-based localization approaches suffer as the environment changes over time. To help mitagate these effects we use time synchronized windows of sensor samples to associate a mobile node with its nearby beacon nodes. In assisted living environments sensor networks likely already have basic sensors to collect contextual information about users and to monitor the environment. We propose using light, humidity, temperature and audio data samples over a short window of time to model the micro-climate of a beacon node. Using micro-climate matching in conjunction with RF signal strength decreases the worst-case localization error significantly by a factor of more than 3 (from 25m to 8m) while making the system more resilient to environment changes. Micro-climate data helps ensure at least room level location tracking even in buildings like hospitals with many rooms in close proximity.
[SMC 07 | Slides]
RT-Link and Voice Streaming for Coal Mine Safety
RT-Link is a TDMA based multi-hop wireless sensor networking MAC protocol. It utilizes tight time synchronization from either in-band message passing, or an out-of-band AM carrier current beacon. RT-Link was deployed as part of a coal mine safety system that could track miner locations as well as stream voice in the event of a disaster, The sensor network functions with a low duty-cycle during normal operation and is able to switch over to a high-rate mode for voice communication during emergencies. RT-Link supports on-demand rate control and can switch the networks operation based on the current application's throughput and end-to-end delay requirements.
[Project Website][RTSS 06 | Slides][SECON 06 | Slides]
eWatch is a wearable computing system that can be worn on your wrist. It has several sensors that gather information about the user and the environment. It can attract the user's attention with visual and/or tactile notifications. eWatch supports wireless Bluetooth communication which allows it to interface with a computer, PDA, or cellular phone. The wrist watch form factor allows open exposure to the environment making it ideal for ascertaining user state information. This also means that eWatch is instantly viewable and always accessible to the user.
[Project Website][BSN 06]
The CMUcam2 includes all of the functionality of the original CMUcam in an enhanced form and a lot of new functionality. It uses an updated processor that has more RAM, more ROM, and more I/O pins. This allowed us to add functionality like frame differencing, edge detection, and color histogramming. The CMUcam2 also uses a frame buffer which allows for faster processing, multiple operations per frame, more control over communication speeds and better looking frame dumps.
[Project Website][ECV 05]