7 research outputs found
Waldo: Batteryless Occupancy Monitoring with Reflected Ambient Light
Reliable and accurate room-level occupancy-tracking systems can enable many new advances in sensors and applications of modern smart buildings. This allows buildings to be more capable of adapting to the needs of their occupants in their day-to-day activities and better optimize certain resources, such as power and air conditioning, to do so. Unfortunately, existing occupancy-tracking systems are plagued by large size, high energy consumption, and, unsurprisingly, short battery lifetimes.
In this paper, we present Waldo, a batteryless, room-level occupancy monitoring sensor that harvests energy from indoor ambient light reflections, and uses changes in these reflections to detect when people enter and exit a room. Waldo is mountable at the top of a doorframe, allowing for detection of a person and the direction they are traveling at the entry and exit point of a room. We evaluated the Waldo sensor in an office-style setting under mixed lighting conditions (natural and artificial) on both sides of the doorway with subjects exhibiting varying physical characteristics such as height, hair color, gait, and clothing. 651 number of controlled experiments were ran on 6 doorways with 12 individuals and achieved a total detection accuracy of 97.38%. Further, it judged the direction of movement correctly with an accuracy of 95.42%. This paper also evaluates and discusses various practical factors that can impact the performance of the current system in actual deployments.
This work demonstrates that ambient light reflections provide both a promising low-cost, long-term sustainable option for monitoring how people use buildings and an exciting new research direction for batteryless computing
ETAP: Energy-aware Timing Analysis of Intermittent Programs
Energy harvesting battery-free embedded devices rely only on ambient energy
harvesting that enables stand-alone and sustainable IoT applications. These
devices execute programs when the harvested ambient energy in their energy
reservoir is sufficient to operate and stop execution abruptly (and start
charging) otherwise. These intermittent programs have varying timing behavior
under different energy conditions, hardware configurations, and program
structures. This paper presents Energy-aware Timing Analysis of intermittent
Programs (ETAP), a probabilistic symbolic execution approach that analyzes the
timing and energy behavior of intermittent programs at compile time. ETAP
symbolically executes the given program while taking time and energy cost
models for ambient energy and dynamic energy consumption into account. We
evaluated ETAP on several intermittent programs and compared the compile-time
analysis results with executions on real hardware. The results show that ETAP's
normalized prediction accuracy is 99.5%, and it speeds up the timing analysis
by at least two orders of magnitude compared to manual testing.Comment: Corrected typos in the previous submissio
The Parallel Persistent Memory Model
We consider a parallel computational model that consists of processors,
each with a fast local ephemeral memory of limited size, and sharing a large
persistent memory. The model allows for each processor to fault with bounded
probability, and possibly restart. On faulting all processor state and local
ephemeral memory are lost, but the persistent memory remains. This model is
motivated by upcoming non-volatile memories that are as fast as existing random
access memory, are accessible at the granularity of cache lines, and have the
capability of surviving power outages. It is further motivated by the
observation that in large parallel systems, failure of processors and their
caches is not unusual.
Within the model we develop a framework for developing locality efficient
parallel algorithms that are resilient to failures. There are several
challenges, including the need to recover from failures, the desire to do this
in an asynchronous setting (i.e., not blocking other processors when one
fails), and the need for synchronization primitives that are robust to
failures. We describe approaches to solve these challenges based on breaking
computations into what we call capsules, which have certain properties, and
developing a work-stealing scheduler that functions properly within the context
of failures. The scheduler guarantees a time bound of in expectation, where and are the work and
depth of the computation (in the absence of failures), is the average
number of processors available during the computation, and is the
probability that a capsule fails. Within the model and using the proposed
methods, we develop efficient algorithms for parallel sorting and other
primitives.Comment: This paper is the full version of a paper at SPAA 2018 with the same
nam