A two-tier data dissemination model for large-scale wireless sensor networks

Ye, Fan; Luo, Haiyun; Cheng, Jerry; Lu, Songwu; Zhang, Lixia

doi:10.1145/570645.570664

Cited by 739 publications

(256 citation statements)

References 13 publications

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“…In the grid-based protocol, TTDD [14] builds the grid on a per-source basis. A source transmits data by building a grid structure.…”

Section: Related Workmentioning

confidence: 99%

“…In data transmission phase, the token passing mechanism of each low layer chain is the same as that of PEGASIS. After receiving the gathered data, the leader of each low layer chain transmits to the high layer leader which eventually transmits to the BS.In the grid-based protocol, TTDD [14] builds the grid on a per-source basis. A source transmits data by building a grid structure.…”

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confidence: 99%

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Grid-Based Data Aggregation for Wireless Sensor Networks

Wang¹,

Chiang²,

Hsieh³

et al. 2014

JACN

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Abstract-In a wireless sensor network (WSN), a huge number of sensor nodes with limited battery power are generally deployed over a severe field to gather data. It is impractical to recharge or replace the batteries of the sensor nodes in such a severe environment. Therefore, an energy efficient protocol is essential to maximize the lifetimes of nodes. In this paper, we propose a grid-based data aggregation scheme (GBDAS) for WSNs. We partition the whole sensor field into a 2-D logical grid of cells. In each cell, the node with the most residual energy takes turn to be the cell head, responsible for aggregating its own data with the data sensed by the other sensor nodes of the cell, and then transmitting it out. In order to reduce the data transmissions to the base station (BS), we further link each cell head to form a chain. In the chain, the cell head with the most residual energy is designated in turn as the chain leader. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In GBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation shows that GBDAS outperforms Direct and PEGASIS.Index Terms-Base station, cell head, data aggregation, grid-based, wireless sensor networks. I. INTRODUCTIONWireless sensor networks (WSNs) with a variety of potential applications have been drawing a lot of attention in recent years [1], [2]. Such networks usually comprise of a great number of low-cost and low-power tiny sensor nodes with the capability of gathering, processing, and transmitting data [3]- [5]. Sensor nodes are typically powered by batteries so their energy is strictly constrained. After being deployed, these sensor nodes are left unattended. Therefore, it is critical to manage sensor nodes in an energy efficiency manner.Since the energy consumption of sensor nodes is significantly spent in data transmission, especially for a long distance. An energy efficient transmission protocol is essential to maximize the lifetimes of sensor nodes. This paper mainly addresses the problem of data transmission from sensor nodes to a remote base station (BS), where the end-user can access the data. Since the location of the BS is distant, the energy consumed by nodes to directly transmit Manuscript received July 10, 2013; revised September 10, 2013 their data to the BS is so considerable that they will die very soon. Therefore, it is important for an approach to use as few transmissions as possible to the BS and reduce the amount of data that needs to be transmitted to the BS [6].In our proposed scheme, only a small portion of nodes are responsible for disseminating the data to the BS instead to decrease the data transmissions. To reduce the amount of data transmitted to the BS, the dissemination nodes aggregate their own data with the data s...

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“…In the grid-based protocol, TTDD [14] builds the grid on a per-source basis. A source transmits data by building a grid structure.…”

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Grid-Based Data Aggregation for Wireless Sensor Networks

Wang¹,

Chiang²,

Hsieh³

et al. 2014

JACN

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show abstract

“…To improve the coverage, uses of multiple sinks have been reported in that sinks move randomly in a geographical area. In Ye et al (2002), Kinalis and Nikoletseas (2007) and Marta and Cardei (2009), authors mentioned about the path coordination of the moveable sink is encounter by another sink in a sensor field. In Yu et al (2010), authors investigated a scheme under which to know the energy status of sensors in sensor fields to track the location of random movement of sink, the sink periodically transmits a beacon message which contains location information.…”

Section: Introductionmentioning

confidence: 99%

Performance measures of fuzzy C-means algorithm in wireless sensor networks

Kumar

Chaturvedi

2017

IJCAET

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Abstract:The major issues that govern performance of wireless sensor networks (WSNs) are efficient uses of limited resources and appropriate routing decisions of network paths under the severely constrained energy scenario. In this work, to address these issues uses of k-means and fuzzy C-means algorithms are investigated for clusters formation and subsequent selection of cluster heads (CHs). For all these newly formed clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) followed by estimation of Euclidean distances. Depending upon the Euclidean distance measures between the sink node and the estimated energy-centroid (EC) of clusters these clusters are classified into five types. The RES estimation is exercised for all the CHs and sensor nodes (SNs) of the network. Outcomes of simulation results indicate superior performance of fuzzy-c means algorithm compared to k-means algorithm. Further, a case study is presented, wherein the sink is allowed to have some movements in the service area. Here, different quadrant of service area exhibits different pattern of query spatial distribution. The optimal location of sink is sought to support energy efficient operational aspects of the WSNs.

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“…Among existing approaches, TTDD [8] and GAF [9] use geographic location to partition the network into a 2-D mesh. (2) Each sen point (cluster) has at least one sensor.…”

Section: Introductionmentioning

confidence: 99%