Energy Management for Wireless Sensor Network Nodes

Sharma, Amit; Shinghal, Kshitij; Srivastava, Neelam; Singh, Raman

doi:10.7323/ijaet/v1_iss1_2

Cited by 20 publications

(11 citation statements)

References 10 publications

(7 reference statements)

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“…In this algorithm, the method of CH selection is totally random and based on a measure under the title of threshold as follows [3,4,5]:…”

Section: Related Workmentioning

confidence: 99%

“…So data transmission from lower levels to higher levels can reduce energy and this can threaten network lifetime [4].One important case in this kind of network is finding an optimum route through which the nodes can transmit data to BS [5].Data transmission can be performed single hop or hop to hop [6,7]. In single hop method, after receiving environment data by common nodes and transferring them to CHs , CHs transmit data to BS.…”

Section: Introductionmentioning

confidence: 99%

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KGAWSN: An Effective Way to Reduce Energy Consumption in Wireless Sensor Networks by K-means and Genetic Algorithms

Delavar¹,

Alirezaie²,

Baradaran³

2012

IJCA

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In this paper, we present a method by which we can significantly reduce energy consumption in sensor networks. In the proposed method, finding an optimum Cluster Head (CH) is performed by Genetic Algorithm (GA) and clustering the network environment is done by k-means algorithm. The results of simulation show that at the end of some certain part of running the proposed algorithm, the number of alive nodes increases, comparing with the same previous methods and this can lead to an increase in sensor network lifetime.

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“…In this algorithm, the method of CH selection is totally random and based on a measure under the title of threshold as follows [3,4,5]:…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

KGAWSN: An Effective Way to Reduce Energy Consumption in Wireless Sensor Networks by K-means and Genetic Algorithms

Delavar¹,

Alirezaie²,

Baradaran³

2012

IJCA

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“…It has two faces: spatial ubiquity-which inherently forces wireless communications and absence of wired power sources-and temporal ubiquity-which implies availability along functioning time (maximum energy autonomy) and also availability at any given time. Whichever the case, it leads to the common need of installation's runtime maximization and consequently minimization of energy demanded by sensing nodes [5]. There are many options to power wireless sensor nodes [6], but a real installation usually poses severe limitations: there is not unlimited power source available, energy from the environment is scarce and not enough for continuous running (e.g., indoors), maintenance of sensors is problematic (e.g., physically hard to reach to change batteries or expensive), and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, is critical to minimize node's power consumption while maintaining application's required quality of service. It is well known that power consumption has a high impact over quality of service offer by a WSN and its lifetime [3][4][5]7]; the paper is centered on its analysis.…”

Section: Introductionmentioning

confidence: 99%

Hardware Architecture Design for WSN Runtime Extension

Asensio

Blasco

Marco

et al. 2013

International Journal of Distributed Sensor Networks

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Internet of Things imposes demanding requirements on wireless sensor networks as key players in context awareness procurement. Temporal and spatial ubiquities are one of the essential features that meet technology boundaries in terms of energy management. Limited energy availability makes anywhere and anytime sensing a challenging task that forces sensor nodes to wisely use every bit of available power. One of the earliest and most determining decisions in the electronic design stage is the choice of the silicon building blocks that will conform hardware architecture. Designers have to choose between dual architectures (based on a lowpower microcontroller controlling a radio module) and single architectures (based on a system on chip). This decision, together with finite state machine design and application firmware, is crucial to minimize power consumption while maintaining expected sensor node performance. This paper provides keys for energy analysis of wireless sensor node architecture according to the specific requirements of any application. It thoroughly analyzes pros and cons of dual and single architectures providing designers with the basis to select the most efficient for each application. It also provides helpful considerations for optimal sensing-system design, analyzing how different strategies for sensor measuring and data exchanging affect node energy consumption.

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“…It is very difficult to detect the route and retain it as the confined quantity of power and frequent alteration in the locus of the position of the sensor nodes gives rise much changes which are very dynamic and tough to forecast. Although there has been an extensive research work towards energy [3][4][5][6] but still it has remain a major obstruction towards development of the effective routing algorithm in wireless sensor networks. In the cluster first approach, the network selects the cluster formation initially and then cluster leader is selected whereas in leader first approach, cluster leader is selected first and then formation of the cluster takes place.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Dynamic Reconfiguration of Routing Agents for WSN Data Aggregation

Prabhavathi¹,

Subramanyam²,

Rao

2015

Emerging Research in Computing, Information, Communication and Applications

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Wireless Sensor Network (WSN) has encapsulated the major attention from the research group owing to its potential features as well as some of its unsolved issues. From the various issues, an energy issue is particularly found to be a root cause of majority of the problems. In the past, there are various research attempts to enhance the network lifetime of WSN, but very few of the studies has proved fruitful. Moreover performing optimization of energy from routing as well as load-balancing perspectives are not witnessed any standard research attempts. Hence, this paper discusses about a framework that uses standard radio and energy model and performs energy optimization by introducing a novel routing agent. The routing agent is incorporated within aggregator node and base station to perform dynamic reconfiguration in case of energy depletion. Compared with standard LEACH algorithm, the proposed technique has better energy efficiency within optimal data aggregation duration.

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Energy Management for Wireless Sensor Network Nodes

Abstract: Abstract:Wireless

Cited by 20 publications

References 10 publications

KGAWSN: An Effective Way to Reduce Energy Consumption in Wireless Sensor Networks by K-means and Genetic Algorithms

KGAWSN: An Effective Way to Reduce Energy Consumption in Wireless Sensor Networks by K-means and Genetic Algorithms

Hardware Architecture Design for WSN Runtime Extension

Energy Efficient Dynamic Reconfiguration of Routing Agents for WSN Data Aggregation

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