Power management in SMAC-based energy-harvesting wireless sensor networks using queuing analysis

Tadayon, Navid; Khoshroo, Sasan; Askari, Elaheh; Wang, Honggang; Michel, Howard E.

doi:10.1016/j.jnca.2012.01.009

Cited by 57 publications

(40 citation statements)

References 23 publications

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“…Recently, energy consumption has become an increasingly important factor and attracted lots of attention in many fields, such as cloud computing (Guenter et al, 2011), mobile ad hoc (Min and Kim, 2007), wireless sensor network (Tadayon et al, 2013) and HPC architectures (Benedict). It also holds in the unique VN embedding context.…”

Section: Q4mentioning

confidence: 99%

Adaptive multi-objective artificial immune system based virtual network embedding

Zhang

Lin

et al. 2015

Journal of Network and Computer Applications

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Section: Q4mentioning

confidence: 99%

Adaptive multi-objective artificial immune system based virtual network embedding

Zhang

Lin

et al. 2015

Journal of Network and Computer Applications

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“…In rehabilitation applications, solar cells can not be used since sensors are often worn under the patient clothes [75]. However, power scavenging methods through motion (Piezoelectric [76]) or body heat (thermopile [77]) have a good potential.…”

Section: Energy Savingmentioning

confidence: 99%

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

Hadjidj¹,

Souil²,

Bouabdallah³

et al. 2013

Journal of Network and Computer Applications

141

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Rehabilitation supervision has emerged as a new application of wireless sensor networks (WSN), with unique communication, signal processing and hardware design requirements. It is a broad and complex interdisciplinary research area on which more than one hundred papers have been published by several research communities (electronics, bio-mechanical, control and computer science). In this paper, we present WSN for rehabilitation supervision with a focus on key scientific and technical challenges that have been solved as well as interdisciplinary challenges that are still open. We thoroughly review existing projects conducted by several research communities involved in this exciting field. Furthermore, we discuss the open research issues and give directions for future research works. Our aim is to gather information that encourage engineers, clinicians and computer scientists to work together in this field to tackle the arising challenges.

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“…Therefore, researchers have made great effort to reduce energy consumption and to prolong network lifetime in various application fields [1][2][3][4][5][6][7]. Recently, energy harvesting technologies have been proposed [8] and various problems in energy harvesting sensor networks have been studied [9][10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

Chen

Zeng

Zhao

2016

International Journal of Distributed Sensor Networks

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Target tracking is a typical application in wireless sensor networks. Both energy efficiency and tracking performance are important issues that need to be considered. They are a pair of contradictions most of the time. Saving energy often sacrifices tracking performance, while enhancing tracking performance needs to consume more energy. In this paper, an efficient sleep scheduling algorithm is put forward to tackle the above problem in energy harvesting sensor networks. At first, we modify the probabilitybased prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). Secondly, a double-storage energy harvesting architecture is employed to increase residual energy of sensor nodes and to extend network lifetime. Simulation results reveal that the proposed sleep scheduling algorithm can improve tracking performance and prolong network lifetime compared with the PPSS algorithm and the proposed algorithm without energy harvesting.

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Power management in SMAC-based energy-harvesting wireless sensor networks using queuing analysis

Cited by 57 publications

References 23 publications

Adaptive multi-objective artificial immune system based virtual network embedding

Adaptive multi-objective artificial immune system based virtual network embedding

Wireless sensor networks for rehabilitation applications: Challenges and opportunities

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks

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