Energy Harvesting for Wireless Sensor Networks Review

Akbari, Saba

doi:10.15439/2014f85

Cited by 56 publications

(32 citation statements)

References 32 publications

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“…Energy harvesting [6] refers to collecting energy from surrounding (sun, wind) or other sources of energies (body heat, finger stroke, foot strikes) and converting these energies to electrical energy. Energies from external sources can be harvested to power the nodes to increase their lifetime.…”

Section: B Energy Harvesting In Wsnmentioning

confidence: 99%

Improving Energy Conservation in Wireless Sensor Network Using Energy Harvesting System

Rashid¹,

Khan²,

Gul³

et al. 2018

ijacsa

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Abstract-Wireless Sensor Networks assume an imperative part to monitor and gather information from complex geological ranges. Energy conservation plays a fundamental role in WSNs since such sensor networks are designed to be located in dangerous and non-accessible areas and has gained popularity since the last decade. The main issue of Wireless Sensor Network is energy consumption. Therefore, management of energy consumption of the sensor node is the main area of our research. Sensor nodes use non-changeable batteries for power supply and the lifetime of Sensor node greatly depends on these batteries. The replacement of these batteries is very difficult in many applications, such as an alternative solution to this problem is to use Energy Harvesting system in Wireless Sensor Network to provide a permanent power supply to sensor nodes. This process of extracting energies from nature and converting it into electrical energy is called energy harvesting. Energy can be harvested from the environment for sensor nodes. There are many sources of energies in nature like solar, wind and thermal which can be harvested and used for WSNs. In this research, we suggest to use energy harvesting system for Cluster Heads in a clustering based Wireless Sensor Networks. We will compare our proposed technique to a well-known clustering algorithm Low Energy Adaptive Cluster Hierarchy (LEACH).

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Section: B Energy Harvesting In Wsnmentioning

confidence: 99%

Improving Energy Conservation in Wireless Sensor Network Using Energy Harvesting System

Rashid¹,

Khan²,

Gul³

et al. 2018

ijacsa

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“…WSN can detect and monitor spatial and temporal parameters for decision making in agricultural farm management [107][108][109], thereby increasing efficiency and productivity while minimizing undesirable impacts on environment [110]. WSN devices are mainly powered by either batteries or solar energy [111]. The potential drawbacks associated with these power sources make the sensors unreliable.…”

Section: Bes For Agricultural Monitoringmentioning

confidence: 99%

Development of Bioelectrochemical Systems to Promote Sustainable Agriculture

Abu-Reesh

2015

Agriculture

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Bioelectrochemical systems (BES) are a newly emerged technology for energy-efficient water and wastewater treatment. Much effort as well as significant progress has been made in advancing this technology towards practical applications treating various types of waste. However, BES application for agriculture has not been well explored. Herein, studies of BES related to agriculture are reviewed and the potential applications of BES for promoting sustainable agriculture are discussed. BES may be applied to treat the waste/wastewater from agricultural production, minimizing contaminants, producing bioenergy, and recovering useful nutrients. BES can also be used to supply irrigation water via desalinating brackish water or producing reclaimed water from wastewater. The energy generated in BES can be used as a power source for wireless sensors monitoring the key parameters for agricultural activities. The importance of BES to sustainable agriculture should be recognized, and future development of this technology should identify proper application niches with technological advancement.

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“…DPM must focus on minimizing the active time of the radio transceiver, as the transceiver draws tens of milliwatts [Akbari2014] and is thus usually the major power consumer of the sensor node [Jain2015]. To reduce the time spent idly listening, i.e., waiting for a message to be received, the in-network time synchronization must be sufficiently accurate to tightly synchronize the activities of senders and receivers.…”

Section: Wireless Sensor Network and Wireless Sensor Node Architecturesmentioning

confidence: 99%

A heterogeneous system architecture for low-power wireless sensor nodes in compute-intensive distributed applications

Engel

Koch

Siebel

2015

2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops)

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Die Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung -Keine kommerzielle Nutzung -Keine Bearbeitung 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0 Erklärung zur DissertationHiermit versichere ich, die vorliegende Dissertation ohne Hilfe Dritter nur mit den angegebenen Quellen und Hilfsmitteln angefertigt zu haben. Alle Stellen, die aus Quellen entnommen wurden, sind als solche kenntlich gemacht. Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner Prüfungsbehörde vorgelegen. Darmstadt, den 22.11.2016 (Andreas Engel) AbstractWireless Sensor Networks (WSNs) combine embedded sensing and processing capabilities with a wireless communication infrastructure, thus supporting distributed monitoring applications. WSNs have been investigated for more than three decades, and recent social and industrial developments such as home automation, or the Internet of Things, have increased the commercial relevance of this key technology. The communication bandwidth of the sensor nodes is limited by the transportation media and the restricted energy budget of the nodes. To still keep up with the ever increasing sensor count and sampling rates, the basic data acquisition and collection capabilities of WSNs have been extended with decentralized smart feature extraction and data aggregation algorithms. Energy-efficient processing elements are thus required to meet the ever-growing compute demands of the WSN motes within the available energy budget.The Hardware-Accelerated Low Power Mote (HaLoMote) is proposed and evaluated in this thesis to address the requirements of compute-intensive WSN applications. It is a heterogeneous system architecture, that combines a Field Programmable Gate Array (FPGA) for hardware-accelerated data aggregation with an IEEE 802.15.4 based Radio Frequency System-on-Chip for the network management and the top-level control of the applications. To properly support Dynamic Power Management (DPM) on the HaLoMote, a Microsemi IGLOO FPGA with a non-volatile configuration storage was chosen for a prototype implementation, called Hardware-Accelerated Low Energy Wireless Embedded Sensor Node (HaLOEWEn). As for every multi-processor architecture, the inter-processor communication and coordination strongly influences the efficiency of the HaLoMote. Therefore, a generic communication framework is proposed in this thesis. It is tightly coupled with the DPM strategy of the HaLoMote, that supports fast transitions between active and idle modes. Low-power sleep periods can thus be scheduled within every sampling cycle, even for sampling rates of hundreds of hertz.In addition to the development of the heterogeneous system architecture, this thesis focuses on the energy consumption trade-off between wireless data transmission and insensor data aggregation. The HaLOEWEn is compared with typical software processors in terms of runtime and energy efficiency in the context of three monitoring applications. The building blocks of these applications comprise hardware-acce...

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Energy Harvesting for Wireless Sensor Networks Review

Cited by 56 publications

References 32 publications

Improving Energy Conservation in Wireless Sensor Network Using Energy Harvesting System

Improving Energy Conservation in Wireless Sensor Network Using Energy Harvesting System

Development of Bioelectrochemical Systems to Promote Sustainable Agriculture

A heterogeneous system architecture for low-power wireless sensor nodes in compute-intensive distributed applications

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