Energy-aware wireless microsensor networks

Raghunathan, Vijay; Schurgers, Curt; Park, Sung Min; Srivastava, Mani

doi:10.1109/79.985679

Cited by 1,375 publications

(683 citation statements)

References 20 publications

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“…At present, many wireless sensor nodes are batterypowered and operate on an extremely economical energy budget since continuous battery replacement is not an option for networks with thousands of physically embedded nodes [2]. Some specific examples of wireless sensor networks include the WiseNET platform developed by the Swiss Centre for Electronics and Microtechnology (CSEM) [3] and those discussed by Warneke et al [4] and Callahan [5].…”

Section: Introductionmentioning

confidence: 99%

Energy harvesting vibration sources for microsystems applications

Beeby

Tudor

White

2006

Meas. Sci. Technol.

2,632

1,468

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This paper reviews the state-of-the art in vibration energy harvesting for wireless, self-powered microsystems. Vibration-powered generators are typically, although not exclusively, inertial spring and mass systems. The characteristic equations for inertial-based generators are presented, along with the specific damping equations that relate to the three main transduction mechanisms employed to extract energy from the system. These transduction mechanisms are: piezoelectric, electromagnetic and electrostatic. Piezoelectric generators employ active materials that generate a charge when mechanically stressed. A comprehensive review of existing piezoelectric generators is presented, including impact coupled, resonant and human-based devices. Electromagnetic generators employ electromagnetic induction arising from the relative motion between a magnetic flux gradient and a conductor. Electromagnetic generators presented in the literature are reviewed including large scale discrete devices and wafer-scale integrated versions. Electrostatic generators utilize the relative movement between electrically isolated charged capacitor plates to generate energy. The work done against the electrostatic force between the plates provides the harvested energy. Electrostatic-based generators are reviewed under the classifications of in-plane overlap varying, in-plane gap closing and out-of-plane gap closing; the Coulomb force parametric generator and electret-based generators are also covered. The coupling factor of each transduction mechanism is discussed and all the devices presented in the literature are summarized in tables classified by transduction type; conclusions are drawn as to the suitability of the various techniques.

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Section: Introductionmentioning

confidence: 99%

Energy harvesting vibration sources for microsystems applications

Beeby

Tudor

White

2006

Meas. Sci. Technol.

2,632

1,468

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“…Energy required to receive a l bit packet for any node j is given by R i =lE elec . Also for sleep and idle mode energy consumption, we use the specifications of MEDUSA II sensor node given in [21]; energy consumed during sleep and idle mode would be equal to .02 mW and 22 mW respectively. Energy required to switch a node from sleep to active mode is assumed to be negligible.…”

Section: Resultsmentioning

confidence: 99%

“…This means that these nodes just switch off their processing unit, but their communication units are in idle state. According to [21], energy consumption of a sensor node in receiving and idle states is nearly equal to the energy consumed during transmission state.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Point Coverage in Wireless Sensor Networks: A Learning Automata Approach

Esnaashari

Meybodi

2008

Communications in Computer and Information Science

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Point coverage in wireless sensor networks is the problem of detecting some stationary or moving target points in the area of the network using as little sensor nodes as possible. This can be accomplished using different deployment strategies, rearrangement of nodes using a moving strategy, or designing a suitable schedule for making nodes on and off in such a way that in each time slice, only nodes which can sense the target points in that period are on. With dynamic point coverage, we refer to the problem of point coverage, where target points are moving, and hence rearrangement or scheduling should be dynamically adapted to the changing position of targets. In this paper, we propose a novel method for the problem of dynamic point coverage in wireless sensor networks using learning automata. Each node is equipped with a learning automaton which will learn (schedule) the proper on and off times of that node based on the movement nature of a single moving target.

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“…Therefore, techniques have been developed which allow nodes to change to a parsimonious sleep mode [32]. During the time periods a node is sleeping, it cannot receive any data.…”

Section: Model 4415 (Transmission Energy)mentioning

confidence: 99%