Dynamic Cluster Head Selection Method for Wireless Sensor Network

Jia, Dongyao; Huaihua, Zhu; Shengxiong, Zou; Hu, Po

doi:10.1109/jsen.2015.2512322

Cited by 176 publications

(86 citation statements)

References 24 publications

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“…Therefore, a new sensor node needs to be selected after certain given time. In wireless ad hoc networks or wireless sensor networks, there have been similar studies on selecting the cluster head (CH) [27,[32][33][34]. In order to select the CH, various parameters can be considered, such as the number of member nodes covered by the CH, the current residual energy level, and the history of CH nodes.…”

Section: System Modelmentioning

confidence: 99%

“…In order to estimate the probability of the interval in which the PU is idle, we sum up the conditional probabilities of the PUs idle (H 0 ) for each of the sensing results (D 0 , D 1 ) in intervals in which the SU operates and IA-based sensing is operated and in which the SU is idle and performs non-IA-based sensing. The estimate for the probability in the measurement window in which the PU is idle is expressed in the denominator of (31) as the sum of (32) and (33).…”

Section: Probabilistic Estimation Of Interference Ratio and Transmissmentioning

confidence: 99%

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Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Jang

Yoo

2018

J Wireless Com Network

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In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user's interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs.

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Section: System Modelmentioning

confidence: 99%

Section: Probabilistic Estimation Of Interference Ratio and Transmissmentioning

confidence: 99%

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Jang

Yoo

2018

J Wireless Com Network

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“…The first in the local cluster head node table is chosen as the coordinated cluster head, and the cluster head table will be sent to it. It also broadcasts the information of the change of the cluster head, and the coordinated cluster head conducts its verification [12][13][14].…”

Section: Beginmentioning

confidence: 99%

Topology of Wireless Sensor Network Based on the Perception Needs of the Internet of Things

Yuan-jun

2018

Int. J. Onl. Eng.

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Abstract-In order to adapt to the multi network convergence of IoT, this paper summarizes the development status, existing problems and further research priorities of the Internet of Things (IoT) and wireless sensor networks (WSNs). Based on that, a generation method of the energy-saving and intrusion-tolerant self-organizing redundancy cellular architecture wireless sensor network (SORCA-W) topology is proposed to realize the interconnection of multiple sensor networks based on the improvement of the SORCA protocol of the wireless sensor topology. Through this generation method, the network can be firstly divided into hexagonal topology network structures according to the location of nodes, then the neighbor node table can be judged and modified by collection of other network nodes and the network name can be added, so that multiple WSNs can be connected for communication to complete the multinetwork integration in the perception layer of IoT.Keywords Internet of Things; wireless sensor networks; perceptual requirements; topology IntroductionThe IoT, an integration of existing networks [1][2], is a ubiquitous network built on the Internet. It is growing along with the progress of various networks, especially the improvement on the process of the wireless sensor originals. WSNs are the most important part of the IoT [3] and an essential composition of the perception layer of the IoT. The functions of wireless sensor nodes become more diverse with the development of science and technology. Different perception nodes can perceive the information that people need and transfer it to the terminal applications timely and stably, which is a further expansion of the original network and a critical material foundation for the development of the IoT [4]. Security issues of WSNs will be more prominent under the growth of the IoT. The major problems and concerns are as follows: firstly, the existing security technologies fail to meet the requirement of multi-network integration of the IoT; secondly, how to avoid the impact of the newly-added nodes on other networks; thirdly, how to identify different network nodes and how to cooperate; fourthly, whether there are security risks during the message transmission and how to ensure the security of messages in reposting and how to repost messages of 24

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“…The sensor nodes are scattered over a large geographic area containing hundreds of nodes to monitor a target region. As the sensed data has to be forwarded to BS for further necessary action, therefore routing becomes important for transferring of data from node to node or BS efficiently [1][2][3][4]. The WSN has been acknowledged as one of the significant technologies of the 21st century.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

Jan

Farman

Javed

et al. 2017

Wireless Communications and Mobile Computing

118

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Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

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Dynamic Cluster Head Selection Method for Wireless Sensor Network

Cited by 176 publications

References 24 publications

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Q-learning-based dynamic joint control of interference and transmission opportunities for cognitive radio

Topology of Wireless Sensor Network Based on the Perception Needs of the Internet of Things

Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

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