Minimizing energy consumption in large scale wireless sensor network using adaptive duty cycle algorithm

Dongre, Laxmi D.; Gulhane, Veena

doi:10.1109/i2ct.2014.7092194

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…At present, this kind of network mostly adopts tree-like and star-like structures and seldom arranges the aggregation node; the sensor node can reach the base station directly or through a few hops. Due to the high deployment efficiency of high-density sensors and strong environmental adaptability, they profoundly impact many fields, such as national defense and military, smart home, agricultural engineering, environmental monitoring, and many other fields [5,6].…”

Section: Introductionmentioning

confidence: 99%

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Yang

Zhou

2021

Scientific Programming

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Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

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Section: Introductionmentioning

confidence: 99%

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Yang

Zhou

2021

Scientific Programming

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“…To set the sleep period appropriately, adaptive duty cycle scheduling algorithms have been considered [14][15][16]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Design of energy‐efficient intermittently connected sensor networks

Kimura

Fukuoka

Hirata

et al. 2021

IEEJ Transactions Elec Engng

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This paper introduces the concept of intermittently connected sensor networks where sensors are sleep most of time and become active randomly and rarely. In the networks, packet collision probability and power consumption can be reduced. Meanwhile, the delivery delay increases with the use of the low activation values. In this paper, we design the energy-efficient intermittently connected sensor networks, considering this trade-off of activation control. We first provide the algorithm that computes the optimal packet forwarding rule achieving the minimum delivery delay, which can be calculated using the activation rates. We then propose the activation rate control methods, which control the activation rates of the sensors based on the density of the sensors and/or the number of wake-ups of sensors having relay packets. By doing this, we can improve the delay performance, while suppressing the increase in the packet collision probability and the power consumption of sensors. Through simulation experiments, we demonstrate the effectiveness of our activation control methods in the intermittently connected sensor networks.

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Analyzing performance of adaptive duty cycle scheduling algorithm and attribute based efficient data delivery for code dissemination in multihop wireless sensor network

Gulhane

Malik

2015

2015 17th International Conference on Advanced Communication Technology (ICACT)

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Minimizing energy consumption in large scale wireless sensor network using adaptive duty cycle algorithm

Cited by 5 publications

References 9 publications

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Design of energy‐efficient intermittently connected sensor networks

Analyzing performance of adaptive duty cycle scheduling algorithm and attribute based efficient data delivery for code dissemination in multihop wireless sensor network

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