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The omnipresence of indoor lighting makes it an ideal vehicle for pervasive communication with mobiledevices. In this paper, we present a communication scheme that enables interior ambient LED lightingsystems 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. By modulating the PWM signal commonly used to drive LED lighting systems, we are able to encode data that can be used as localization landmarks. We show through experiments how a binary frequency shift keying modulation scheme can be used to transmit data at 1.25 bytes per second (fast enough to send an ID code) from up to 29 unique light sources simultaneously in a single collision domain. We also show how tags can demodulate the same signals using a light sensor instead of a camera for low-power applications.

In this paper, we propose a model-based design approach for developing wireless sensor-actuator networks that can map multiple sets of application-level interactions onto a single networking substrate while still enforcing individual requirements. We use a top-down design approach where the functional requirements for each application are graphically modeled using a tool called SysWeaver. Sensor networking applications add unique challenges for model-based design frameworks because the system deployment view is tightly coupled to an installation-specific network topology and link characteristics. Wireless devices can also be mobile and hence may not easily map to standard deployment views. We introduce a SysWeaver plugin called SenseWeaver that is able to capture live toplogy data from an instrumentation deployment and feed the topology and link characteristic information to the system model. A developer can then use SenseWeaver specify the functional requirements of multiple applications, analyze communication and task scheduling requirements based on actual topology data, and automatically generate customized code for each sensor network node.

In this paper, we present the architecture, design and experiences from a wirelessly managed microgrid deployment in rural Les Anglais, Haiti. The system consists of a three-tiered architecture with 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. This paper provides a chronology of our deployment and installation strategy that involved GPS-based site mapping along with various network conditioning actions required as the network evolved. Finally, we summarize key lessons learned and hypothesis about additional hardware that could be used to ease the tracing of faults like short circuits and downed lines within microgrids.

In this paper we present SAN-Logic, a lightweight logic-based programming paradigm that enables the dynamic progammability and configuration of sensor-actuator interactions in wireless sensor networks used to support Cyber-Physical Systems (CPS). Our goal is to simplify complex CPS design by providing a structured model of interactions that can be automatically mapped and deployed to a sensor-actuator network in an efficient and scalable manner. In contrast to sensor networking paradigms that distribute an application into individual sub-programs, SAN-Logic models the system as a set of boolean expressions which can be partitioned across the network like gates in a circuit. The user defines interactions as timed asynchronous sequential logic expressions with sensors and actuators representing the inputs and outputs of the system. This approach is highly scalable since once deployed each interaction takes place as a sequence of independent and asynchronous events. This allows SAN-Logic to operate in a fully distributed manner without a central authority. Using this framework, optimization takes place across multiple tasks enabling sharing of resources within the network which will be an important part of future CPS. Redundant routes and the stateless nature of combinational logic (along with periodic state update messages) allow the system to easily cope with packet-loss and failed nodes. A major benefit of this approach is the ability to leverage existing hardware design and synthesis tools used by the VLSI design community. We demonstrate how boolean manipulation of the logic can be used to alter the mapping of expressions onto the network and hence can be used for optimization and verification. We provide an approach using logic simplification and mapping that reduces message passing by factoring common terms across different data paths within tasks and placing intermediate terms such that they benefit from shorter paths. In complex systems, we see on average a 40- - % reduction in message passing as compared to an implementation that does not optimize communication patterns within and across tasks.

In this paper we present CMUcam3, a low-cost, open source, embedded computer vision platform. The CMUcam3 is the third generation of the CMUcam system and is designed to provide a flexible and easy to use open source development environment along with a more powerful hardware platform. The goal of the system is to provide simple vision capabilities to small embedded systems in the form of an intelligent sensor that is supported by an open source community. The hardware platform consists of a color CMOS camera, a frame buffer, a low cost 32-bit ARM7TDMI microcontroller, and an MMC memory card slot. The CMUcam3 also includes 4 servo ports, enabling one to create entire, working robots using the CMUcam3 board as the only requisite robot processor. Custom C code can be developed using an optimized GNU toolchain and executables can be flashed onto the board using a serial port without external downloading hardware. The development platform includes a virtual camera target allowing for rapid application development exclusively on a PC. The software environment comes with numerous open source example applications and libraries including JPEG compression, frame differencing, color tracking, convolutions, histogramming, edge detection, servo control, connected component analysis, FAT file system support, and a face detector.