Energy-Efficient Scheduling Mechanism for Indoor Wireless Sensor Networks

Yu, Tianqi; Akhtar, Auon Muhammad; Shami, Abdallah; Wang, Xianbin

doi:10.1109/vtcspring.2015.7145818

Cited by 4 publications

(11 citation statements)

References 13 publications

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“…We conduct the simulations by Castalia [ 24 ] based on OMNet++4.1 [ 25 ], which provides realistic and accurate wireless channel models, radio models (CC2420 and CC1000), and MAC models (IEEE 802.15.4) so that the simulation results would become meaningful [ 26 ]. We compare the proposed algorithm EBMS with the state-of-the-art approaches SSA-DC (Simple node Scheduling Approach in view of Data Correlation) [ 4 ], OBSP (Optimized Back-off Sleep Protocol) [ 11 ], and DSDA (Distributed Saturation Degree-based Algorithm) [ 13 ] by running them in the same networks with same parameters in the simulations.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…To minimize energy consumption and extend network lifetime, a common technique is to put some sensors in sleep mode and put others in active mode for the sensing and communication tasks [ 3 ]. A simple node scheduling approach in view of data correlation (SSA-DC) is proposed in Reference [ 4 ]. The authors divide the networks into clusters using an adaptive dual-metric K-means (DK-means) method.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the main idea of sensor scheduling management is to keep a minimal number of sensor nodes or their modules active, in order to provide a necessary coverage of the sensing field, and to put the other nodes or their modules in sleep mode to save energy. Some existing research assign each sensor node a sleep time, and periodically put nodes to sleep, such as References [ 4 , 5 ]. This kind of sleeping scheme is easy to implement but it cannot adapt to the dynamic features of target tracking applications.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks

Jiang

Zhao

2018

Sensors

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One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks

Jiang

Zhao

2018

Sensors

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“…Recently a few approaches relying on correlated information to preserve energy were proposed. In [10] and [11], the authors propose updating mechanisms, developed independently of, but closely related to, our work. In [10], the authors propose an algorithm that clusters sources based on their spatial and temporal correlation.…”

Section: Related Workmentioning

confidence: 99%

Using Correlated Information to Extend Device Lifetime

Hribar

Costa

Kaminski

et al. 2019

IEEE Internet Things J.

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The massive device deployments in the Internet of Things (IoT) generate immense amounts of data that can be leveraged to improve overall network performance. This work outlines how data gathered from correlated sensor nodes can be used to improve the timeliness of updates of another sensor node in the network. We consider a system of two correlated information sources, i.e. sensor nodes, which periodically send updates to a gateway, regarding the observed physical phenomenon distributed in space and evolving in time. The optimal use of updates in such a system greatly depends on the correlation between the two sources, and to explore this effect we investigate three different models of the covariance between independently obtained observations of the phenomenon of the interest. We extract values for the parameters in the covariance models from data coming from a real sensor network, to provide the reader with a realistic feel for scaling parameters values and the applicability of our analysis in a real scenario. We demonstrate that using correlated information results in a significant increase in device lifetime and compare our approach to others proposed in the literature.

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“…It executes the idea of genetic approach and utilizes the local improvement to enhance the optimal solution among the cover sets that can perform full area coverage without blind points (points not covered by any sensor). The work in [20] presents an adaptive clustering method which divides the sensor nodes into clustering groups based on data correlation among the sensor data. The nodes that represent the group send the data to the sink which eventually remove the data redundancy to conserve the energy of the network and improves the network lifetime.…”

Section: Literature Reviewmentioning

confidence: 99%

Sensor Activity Scheduling Protocol for Lifetime prolongation in Wireless Sensor Networks

Hussein

Idrees

2017

KJAR

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Abstract:In Wireless Sensor Network (WSN), the dense sensor nodes deployment in the sensing field can be exploited in conserving the energy of the whole network, where the data of these nodes can be highly correlated. Therefore, it is necessary to turn off the unnecessary nodes that sense similar sensor readings so as to reduce the redundant sensed readings and decrease the communication overhead thus extend the WSN lifetime. This article suggests a Sensor Activity Scheduling (SAS) protocol for lifetime improvement of WSNs. SAS works in a periodic way. It exploits the spatial correlation among sensed sensor data in order to produce the best sensor activities schedule in WSNs. SAS composed of three phases: data collection, decision-based optimization, and sensing. SAS measures the similarity degree among the sensed data that collected in the first phase. It makes a decision of which sensors stay active during the sensing phase in each period and put the other nodes into low power sleep whilst keeping a good accuracy level to the received data at the sink to conserve the power and enhance the lifetime of the WSN. Several experiments based on real sensed data and by using OMNeT++ simulator demonstrate that SAS can save energy and extend the WSN lifetime efficiently compared with the other methods.

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Energy-Efficient Scheduling Mechanism for Indoor Wireless Sensor Networks

Cited by 4 publications

References 13 publications

Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks

Energy-Balanced Multisensory Scheduling for Target Tracking in Wireless Sensor Networks

Using Correlated Information to Extend Device Lifetime

Sensor Activity Scheduling Protocol for Lifetime prolongation in Wireless Sensor Networks

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