Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

Almeida, Fernando R.; Brayner, Ângelo; Rodrigues, Joel J. P. C.; Maia, José Everardo Bessa

doi:10.3390/s17061317

Cited by 22 publications

(18 citation statements)

References 24 publications

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“…The LEACH protocol for WSN allows all data from nodes within the cluster to be processed locally, reducing the set of data that needs to be transmitted to the end user [ 27 ]. This idea was first proposed in [ 28 ], and the basic concept was to transmit data from sensor nodes, through its cluster head (CH), to the BS in rounds, and for each round, there is a permutation of the CH nodes to prevent their dying quickly.…”

Section: A Comprehensive Energetic Budget Of a Sensor Node In Leacmentioning

confidence: 99%

A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System

Mouapi

Hakem

2018

Sensors

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Energy Harvesting techniques are increasingly seen as the solution for freeing the wireless sensor nodes from their battery dependency. However, it remains evident that network performance features, such as network size, packet length, and duty cycle, are influenced by the sum of recovered energy. This paper proposes a new approach to defining the specifications of a stand-alone wireless node based on a Radio-frequency Energy Harvesting System (REHS). To achieve adequate performance regarding the range of the Wireless Sensor Network (WSN), techniques for minimizing the energy consumed by the sensor node are combined with methods for optimizing the performance of the REHS. For more rigor in the design of the autonomous node, a comprehensive energy model of the node in a wireless network is established. For an equitable distribution of network charges between the different nodes that compose it, the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for this purpose. The model considers five energy-consumption sources, most of which are ignored in recently used models. By using the hardware parameters of commercial off-the-shelf components (Mica2 Motes and CC2520 of Texas Instruments), the energy requirement of a sensor node is quantified. A miniature REHS based on a judicious choice of rectifying diodes is then designed and developed to achieve optimal performance in the Industrial Scientific and Medical (ISM) band centralized at 2.45 GHz. Due to the mismatch between the REHS and the antenna, a band pass filter is designed to reduce reflection losses. A gradient method search is used to optimize the output characteristics of the adapted REHS. At 1 mW of input RF power, the REHS provides an output DC power of 0.57 mW and a comparison with the energy requirement of the node allows the Base Station (BS) to be located at 310 normalm from the wireless nodes when the Wireless Sensor Network (WSN) has 100 nodes evenly spread over an area of 300 × 300 m2 and when each round lasts 10 min. The result shows that the range of the autonomous WSN increases when the controlled physical phenomenon varies very slowly. Having taken into account all the dissipation sources coexisting in a sensor node and using actual measurements of an REHS, this work provides the guidelines for the design of autonomous nodes based on REHS.

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Section: A Comprehensive Energetic Budget Of a Sensor Node In Leacmentioning

confidence: 99%

A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System

Mouapi

Hakem

2018

Sensors

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“…The work in [ 22 ] explores the concept of multidimensional behavioral clustering [ 44 ] for reducing message transmission in traditional WSN. In that sense, the sink node creates virtual clusters by clustering the sensors nodes according to the correlations of the measurements from all monitored (dimensions).…”

Section: Related Workmentioning

confidence: 99%

“…However, such approach can lead to an overhead of messages, increasing the sensor node energy consumption (which is generally battery-powered). Nevertheless, recent works [ 19 , 20 , 21 , 22 ] proposed a different approach, clustering sensor nodes based on the similarity/correlation of the sensor nodes’ measurements, instead of physical distance, as many traditional works on WSN field [ 5 , 23 ]. Thus, the middleware can select only a small subset of nodes, whose measurements represent the most variance of the data, to start transmitting.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

Lemos

Filho

Rabelo

et al. 2018

Sensors

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Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user’s queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user’s queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.

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“…The Pearson correlation coefficient [10] calculates the similarity by a common score item or a vector of users. When calculating the correlation of the recommendation system, divided into similarity between computing users, the similarity between computing project.…”

Section: Similarity Measurementioning

confidence: 99%

Time Decay Friend Recommender System for Social Network

peng¹,

Li²,

Zhou³

et al. 2018

Proceedings of the 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks &

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With the rapid development of the Internet, the recommendation system is to solve the problem of information overload and the emergence of a personalized information filtering technology. Collaborative Filtering is the most successful and widely technologies to date. But user's interests are not static, it will vary over time, and in which the social network as well. This paper selects a single forgetting function algorithm to improve research, but different users forgetting rules are not the same, it is the inadequacy of this algorithm. At the same time, the selection of experimental data object is not targeted, it cannot fully considering the characteristic of social platform for all users. The experimental results show that the proposed algorithm can efficiently improve recommendation quality.

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Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

Cited by 22 publications

References 24 publications

A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System

A New Approach to Design Autonomous Wireless Sensor Node Based on RF Energy Harvesting System

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

Time Decay Friend Recommender System for Social Network

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