A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks

Taherkordi, Amir; Mohammadi, Reza; Eliassen, Frank

doi:10.1109/waina.2008.130

Cited by 21 publications

(18 citation statements)

References 13 publications

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“…This method reduces the number of messages during transmission but at the same time the cost for node buffer is increased also delay increases in some critical messages in case of high support value. Taherkordi et al in [13], proposed a communication efficient distributed protocol for clustering the sensor data. In this author proposed a distributed version of K-Mean clustering algorithm and sends summarized data towards the sink.…”

Section: Literature Reviewmentioning

confidence: 99%

Review on data mining techniques in wireless sensor networks

Raut

Khandait²

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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Over the past few years, wireless sensor networks (WSN) have emerged as one of the most exciting fields in Computer Science research. A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomenon and processing them. The main aim of deploying applications based on WSNs is to make use of the data sensed by the sensors to raise the real-time decisions. The main limitations of WSNs are characteristics of sensor nodes and nature of sensor data generated by networks. Due to these limitations, traditional data mining techniques are not suitable to WSNs. As the data generated by WSNs is highly resource-constrained, huge in volume, fast changing, it is very challenging to design suitable data mining techniques for WSNs. This inspires to explore a novel and appropriate data mining technique capable of extracting knowledge from huge volume and variety of continuously arriving data from WSNs. In this paper different existing data mining techniques adopted for WSNs are examined with different classification, evaluation approaches, Finally, some research challenges related to adopting data mining techniques in WSNs are also pointed out.

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Section: Literature Reviewmentioning

confidence: 99%

Review on data mining techniques in wireless sensor networks

Raut

Khandait²

2015

2015 2nd International Conference on Electronics and Communication Systems (ICECS)

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“…Eventually, the leader node, which is similar to the cluster head, transmits the aggregated data to the sink. [n CCS [7], PEGAS[S [8] and DRAEM [9], sensors are formed by a chain. Since at most two nodes are designated to communicate with the base station, the energy dissipation is significantly reduced when it is compared to the cluster-based approach.…”

Section: Related Workmentioning

confidence: 99%

Competitive decision algorithm for constructing maximum lifetime spanning tree in wireless sensor networks

Xiong

Ai-bing

2014

2014 9th International Conference on Computer Science &Amp; Education

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In WSN, a tree-based topology is often used to collect data from sensor nodes. When constructing the tree, a key challenge is to conserve energy consumption among the nodes and maximum system lifetime. Previous works have shown that finding a maximum lifetime data gathering tree from all possible spanning trees is a NP-hard problem. Here we design a competitive decision algorithm for solving it by balancing the load between the sensor nodes. One feature of it is using some mathematical properties to reduce the size of the problem.Another feature is that there is no need to know the positions of sensor nodes, which are impractical for many applications.Simulation results show that this new approach greatly improves the lifetime of the network and is competitive whether applied in a dense or sparse network or whether the sink node is at the center of the network or outside it.

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“…For instance, Taherkordi et al in [11] propose multi-dimensional clustering algorithm, in which nodes are clustered according to their sensed attributes. On the other hand, the Distributed, Hierarchical Clustering and Summarization algorithm (DHCS) proposed in [12] provide a better solution for dense networks.…”

Section: Related Workmentioning

confidence: 99%