3,340 research outputs found
Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks
The battery-less Internet of Things (IoT) devices are a key element in the
sustainable green initiative for the next-generation wireless networks. These
battery-free devices use the ambient energy, harvested from the environment.
The energy harvesting environment is dynamic and causes intermittent task
execution. The harvested energy is stored in small capacitors and it is
challenging to assure the application task execution. The main goal is to
provide a mechanism to aggregate the sensor data and provide a sustainable
application support in the distributed battery-less IoT network. We model the
distributed IoT network system consisting of many battery-free IoT sensor
hardware modules and heterogeneous IoT applications that are being supported in
the device-edge-cloud continuum. The applications require sensor data from a
distributed set of battery-less hardware modules and there is provision of
joint control over the module actuators. We propose an application-aware task
and energy manager (ATEM) for the IoT devices and a vector-synchronization
based data aggregator (VSDA). The ATEM is supported by device-level federated
energy harvesting and system-level energy-aware heterogeneous application
management. In our proposed framework the data aggregator forecasts the
available power from the ambient energy harvester using long-short-term-memory
(LSTM) model and sets the device profile as well as the application task rates
accordingly. Our proposed scheme meets the heterogeneous application
requirements with negligible overhead; reduces the data loss and packet delay;
increases the hardware component availability; and makes the components
available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure
Intermittent Computing: Challenges and Opportunities
The maturation of energy-harvesting technology and ultra-low-power computer systems has led to the advent of intermittently-powered, batteryless devices that operate entirely using energy extracted from their environment. Intermittently operating devices present a rich vein of programming languages research challenges and the purpose of this paper is to illustrate these challenges to the PL research community. To provide depth, this paper includes a survey of the hardware and software design space of intermittent computing platforms. On the foundation of these research challenges and the state of the art in intermittent hardware and software, this paper describes several future PL research directions, emphasizing a connection between intermittence, distributed computing, energy-aware programming and compilation, and approximate computing. We illustrate these connections with a discussion of our ongoing work on programming for intermittence, and on building and simulating intermittent distributed systems
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
- …