A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks

Villas, Leandro A.; Boukerche, Azzedine; Oliveira, Horácio A. B. F.; Araújo, Regina B.; Loureiro, Antônio A. F.

doi:10.1016/j.adhoc.2011.08.005

Cited by 95 publications

(61 citation statements)

References 20 publications

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“…The heuristic algorithm MCT for CDA and its distributed implementation DMCT were also proposed in [14]. Recently, Villas et al [15] proposed dYnamic and scalablE tree Aware of Spatial correlatTion (YEAST) scheme by exploiting the spatial correlation between sensor nodes. The sensor nodes that detect the same event are grouped in a correlated region and the group head is selected and rotated in each round.…”

Section: Related Workmentioning

confidence: 99%

Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

Wang

2013

J Wireless Com Network

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Data aggregation is a very important method to conserve energy by eliminating the inherent redundancy of raw data in wireless sensor networks (WSNs). In this article, we developed an automatic auto regressive-integrated moving averagemodeling-based data aggregation scheme in WSNs. The main idea behind this scheme is to decrease the number of transmitted data values between sensor nodes and aggregators by utilizing time series prediction model. The proposed scheme can effectively save the precious battery energy of wireless sensor nodes while keeping the predicted data values of aggregators within application-defined error threshold. We show through experiments with real data that the predicted data values of our proposed scheme fit the real sensed data values very well and fewer messages are transmitted between sensor nodes and aggregators than the native data aggregation scheme. Furthermore, the characteristics of the proposed data aggregation scheme are also discussed in this article.

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Section: Related Workmentioning

confidence: 99%

Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

Wang

2013

J Wireless Com Network

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“…Nodes detecting the same event are grouped in one correlated region and each correlated region have only one representative node whose role is rotated after every round to balance the energy consumption among the nodes. 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.…”

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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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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“…Moreover, the control information results in more energy depletion and vice versa. All the studied algorithms in this paper are evaluated via three scales: low, medium, and high [49,50].…”

Section: Performance Metrics Of Cluster-based Wsnmentioning

confidence: 99%

Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy

Zeb

Islam

Zareei

et al. 2016

International Journal of Distributed Sensor Networks

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Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network orientation, WSN can be of two types: flat network and hierarchical or cluster-based network. Various advantages of cluster-based WSN are energy efficiency, better network communication, efficient topology management, minimized delay, and so forth. Consequently, clustering has become a key research area in WSN. Different approaches for WSN, using cluster concepts, have been proposed. The objective of this paper is to review and analyze the latest prominent cluster-based WSN algorithms using various measurement parameters. In this paper, unique performance metrics are designed which efficiently evaluate prominent clustering schemes. Moreover, we also develop taxonomy for the classification of the clustering schemes. Based on performance metrics, quantitative and qualitative analyses are performed to compare the advantages and disadvantages of the algorithms. Finally, we also put forward open research issues in the development of low cost, scalable, robust clustering schemes.

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A spatial correlation aware algorithm to perform efficient data collection in wireless sensor networks

Cited by 95 publications

References 20 publications

Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks

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

Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy

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