The low power energy aware processing (LEAP) embedded networked sensor system

Abstract

Abstract-A broad range of embedded networked sensor (ENS) systems for critical environmental monitoring applications now require complex, high peak power dissipating sensor devices, as well as on-demand high performance computing and high bandwidth communication. Embedded computing demands for these new platforms include support for computationally intensive image and signal processing as well as optimization and statistical computing. To meet these new requirements while maintaining critical support for low energy operation, a new multiprocessor node hardware and software architecture, Low Power Energy Aware Processing (LEAP), has been developed. This architecture integrates fine-grained energy dissipation monitoring and sophisticated power control scheduling for all subsystems including sensor subsystems. The LEAP architecture enables complex energy-aware algorithm design by providing a simple interface to control numerous platform and sensor power modes and report detailed energy usage information. This paper also describes experimental results of a new distributed node testbed based on LEAP demonstrating that by exploiting high energy efficiency components and enabling proper on-demand scheduling, the LEAP architecture meets both sensing performance and energy dissipation objectives for a broad class of applications. This testbed including the network of distributed LEAP nodes and a system producing physical, mobile events provides a development environment for LEAP-hosted algorithms. New design principles, detailed implementation, and in-network programming and remote debugging capabilities of this platform are also described. While this is the first report of the LEAP system, it has been deployed for nearly one year with 50 users developing energy aware systems.Keywords-embedded wireless networked sensor, energy-aware multprocessor platform, sensor platform hardware and software architecture

Variability-aware duty cycle scheduling in long running embedded sensing systems

“…Energy consumption can be directly measured with dedicated monitors [24], inferred from remaining battery capacity [19], or through variability-aware models that estimate energy expenditure by measuring conditions that affect power consumption, e.g. temperature and activity rates.…”

Multi-Radio Wireless Sensor Networks: Energy Efficient Solutions for Radio Activation

“…While the first generation sensors had limited processing and storage capabilities, advances in technology, in combination with increased application demands have resulted in more powerful second generation sensor nodes. These nodes possess relatively higher processing and storage capabilities achieved through the use of powerful CPUs, and large memories [1], [2]. These nodes are also capable of operating multiple radios simultaneously, each with a different power, range and bandwidth rating.…”

A DVS-assisted hard real-time I/O device scheduling algorithm

“…A task running on a hard real-time system must return response before its deadline to avoid any critical failure. Examples of hard real-time monitoring systems include medical-care systems (Khatib et al 2006), critical environmental monitoring systems (McIntire et al 2006;Alexandropoulos et al 2005), and driver-vigilance monitoring systems (Bergasa et al 2006). Many sensor devices consume more power and energy than a processor.…”