Design of structure-free and energy-balanced data aggregation in wireless sensor networks

Chao, Chih–Min; Hsiao, Tzu-Ying

doi:10.1016/j.jnca.2013.02.013

Cited by 73 publications

(48 citation statements)

References 18 publications

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“…To balance energy consumption, this technique implements scheduled data aggregation among the neighbors of the aggregator. Simulated results confirm that this technique improves aggregation gain and consume less energy [3]. Aggregation Aware Early Event Notification Technique-AAEENT: To improve the event notification delay and aggregation gain, AAEENT [27] scheme is proposed.…”

Section: Related Workmentioning

confidence: 72%

“…YEAST is evaluated in terms of accuracy of event detection which is 95% and can save 75% residual energy as compared to traditional data gathering techniques [30]. Structure Free and Energy Balanced-SFEB: Chao et al [3] proposed a novel approach called as structure free and energy balanced data aggregation for sensor networks. This scheme overcomes the drawbacks of DAA + RW protocols and is designed for spatial and temporal convergence of the data.…”

Section: Related Workmentioning

confidence: 99%

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Evaluation and Comparative Study of Structure Free Aggregation Techniques for Duty Cycled Wireless Sensor Networks

Sran¹,

Kaur²

2017

IJCA

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For delay sensitive event based applications, structure free data aggregation approach has been found to be more suitable because of the reduced structure maintenance overhead. However, in the literature, it has been observed that increasing the delay at intermediate sensor node increases aggregation and the event notification time. The objective is to investigate various structure free aggregation schemes in terms of aggregation gain, event notification time and the overall energy consumption. To achieve this objective, a comparative study of various structure free aggregation techniques is presented. First, we investigate the potential of aggregation in different structure free aggregation techniques for duty cycled sensor networks. Then the performance of the aggregation techniques is demonstrated with the help of simulations on Cooja network simulator in Contiki operating system. From the results, it has been observed that Tuned delay based aggregation scheme reduces energy consumption (upto 13%) as compared to Collect protocol. Moreover, it reduces the worst event notification time (upto 20%) in comparison to random delay based aggregation scheme.

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Section: Related Workmentioning

confidence: 72%

Section: Related Workmentioning

confidence: 99%

Evaluation and Comparative Study of Structure Free Aggregation Techniques for Duty Cycled Wireless Sensor Networks

Sran¹,

Kaur²

2017

IJCA

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“…To reduce the amount of communication data in WSN, a lot of correlation-based data aggregation methods have been proposed in [7,[16][17][18][19][20][21][22][23][24]. The traditional data aggregation methods which are used in WSN mainly include two categories, the first type is based on least square method, Bayesian estimation method, D-S evidence theory, and so on; the other is based on artificial intelligence theory of artificial neural network method, fuzzy reasoning method, and rough set method [25].…”

Section: Related Studiesmentioning

confidence: 99%

“…Reference [6] proposed if we want to use big data in the coal mine industry with WSN, we should consider how to decrease the energy consumption. Reference [7] proposed that data aggregation can be used in WSN and saved more energy. Based on above research work, this paper will focus on how to use WSN in the monitoring and prewarning system based on big data in coal mine industry.…”

Section: Introductionmentioning

confidence: 99%

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Chen

Wei

2018

Scientific Programming

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More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

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“…This study shows that although predominant data acquisition middleware frameworks [22,41,42,33,15,8,13,24,38,20,7] have succeeded in decreasing the overall energy consumption of the network by introducing power-aware innetwork processing algorithms, they have overlooked the important parameter of the underlying network topology. In particular, most of the approaches establish query dissemination and data acquisition on the premise of Query Routing Trees (QRTs) constructed in an ad hoc manner [22,41,32] where each sensor selects as its parent the first node from which a query was received.…”

Section: Introductionmentioning

confidence: 99%

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Andreou

Zeinalipour-Yazti

Samaras

et al. 2014

Journal of Network and Computer Applications

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Wireless Sensor Networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energyefficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot + , a distributed network-aware framework that optimizes network efficiency by combining three components: i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot + in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot + using real datasets and show that KSpot + provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN.

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Design of structure-free and energy-balanced data aggregation in wireless sensor networks

Cited by 73 publications

References 18 publications

Evaluation and Comparative Study of Structure Free Aggregation Techniques for Duty Cycled Wireless Sensor Networks

Evaluation and Comparative Study of Structure Free Aggregation Techniques for Duty Cycled Wireless Sensor Networks

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

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