An expansion cluster routing algorithm based on RSSI for an efficient data transmission

Hong, Sung-Il; Lin, Chi-Ho

doi:10.1109/icact.2016.7423261

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…The distance is estimated using the RSSI metric. The RSSI of each node to the sink node is used for cluster formation in wireless sensor network [8]. The problems prevailing in LEACH routing protocol including packet forwarding and cluster head node selection is resolved.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Receiver Signal Strength Indicator Based Clustered Routing Algorithm for Wireless Sensor Network

Lakshmi,

Sureshz,

Maruti

2020

IJEAT

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Robust and efficient algorithms for routing and other process for a wireless sensor network are under active development due to technological advancements on wireless transmission systems. Each of the sensor nodes in a wireless sensor network either transmits or forwards the data packets to the base station. The main objective of the majority of the work in the literature is to save the energy consumption efficiently. The cluster based routing mechanism helps to achieve low energy consumption within the network. The network organizes its nodes as a cluster and selects a particular node as cluster head to manage the transmission within and between clusters. The majority of the clustering approach selects the cluster head using a thresholding based approach. Nodes having energy level higher than the threshold are the candidates for the cluster head selection. In the proposed approach the nodes remaining energy and the sum of distance between individual nodes to the cluster head node is considered. Optimal cluster head selection will help to increase the overall life time of the network. The distance between the sensor nodes is estimated using RSSI (Received Signal Strength Indicator) and other parameters measured from the physical layer. Experiments are conducted with simulation environment created with the NS-2 simulator and efficiency of the approach is analyzed in detail.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Receiver Signal Strength Indicator Based Clustered Routing Algorithm for Wireless Sensor Network

Lakshmi,

Sureshz,

Maruti

2020

IJEAT

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“…The use of real-time network parameters such as network link, radio strength, energy level and channel gain etc can make CH selection more efficient. Such hypotheses have been asserted by researcher [54] where authors applied RSSI, energy level, channel gain and distance to perform CH selection. However, applying multiple parameters to decide CH is intricate and can become more trivial under dynamic topology.…”

Section: Mobile-wsn Modelmentioning

confidence: 99%

QoS Oriented and Energy Efficient Routing Protocol for Cooperative MIMO Based Mobile WSN: Q-E2RPC

Reddy¹,

Devanagavi²,

Thippeswamy³

2019

JESTR

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In this paper, a robust mobility assisted WSN routing protocol named QoS oriented and Energy Efficient Routing Protocol for Cooperative Multiple Input Multiple Output (MIMO) based mobile WSNs (Q-E2RPC) has been developed that exploits the efficiency of network partitioning, Fuzzy Clustering Mean (FCM) and Expectation Maximization (EM) based clustering, Fuzzy Logic Controller (FLC) based Cluster Head (CH) selection and mobile sink based data gathering to meet QoS demands and energy efficiency. Unlike classical clustering methods FCM and EM as a cumulative solution enables optimal clustering, which is followed by the multiple network parameters based CH selection. The use of single mobile sink avoided multi-hop transmission and signaling overheads that eventually reduced energy consumption. Q-E2RPC protocol exhibited timely data delivery, low energy consumption, and reduced signaling overheads. The results exhibited that Q-E2RPC outperforms other state-of-art techniques in terms of higher throughput, low delay and energy consumption, and higher network efficacy.

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“…An energy efficient clustering method is dependent of two entities, one is the cluster formation and the other is the CH selection. Many of the authors have proposed cluster formation and CH selection methods, in which CHs are selected randomly or based on received signal strength indicator (RSSI) at FC from CHs, and, furthermore, CHs select their cluster members randomly or based on RSSI at CHs from sensors [ 24 , 25 ]. In [ 24 ], authors have proposed CH selection based on RSSI, energy level, reporting channel gain and distance between FC and CHs.…”

Section: Problem Statementmentioning

confidence: 99%

“…Many of the authors have proposed cluster formation and CH selection methods, in which CHs are selected randomly or based on received signal strength indicator (RSSI) at FC from CHs, and, furthermore, CHs select their cluster members randomly or based on RSSI at CHs from sensors [ 24 , 25 ]. In [ 24 ], authors have proposed CH selection based on RSSI, energy level, reporting channel gain and distance between FC and CHs. In [ 22 ], authors have proposed a modified version of a well-known cluster making method low energy adaptive clustering hierarchy (LEACH), in which CHs are selected based on energy levels.…”

Section: Problem Statementmentioning

confidence: 99%

“…The clustering techniques with respect to sensing efficiency have widely been shown in literature [ 19 , 20 ], but very few energy efficient schemes have been proposed [ 22 , 23 , 24 , 25 , 26 ], although the energy efficiency is one of the most important factors for the designing of sensor networks. To the best of the author’s knowledge, clustering schemes have mostly used perfect channels and have considered hard decisions.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network

Bhatti

Saeed

Nam

2016

Sensors

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We propose a novel cluster based cooperative spectrum sensing algorithm to save the wastage of energy, in which clusters are formed using fuzzy c-means (FCM) clustering and a cluster head (CH) is selected based on a sensor’s location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. The sensing information of a single sensor is not reliable enough due to shadowing and fading. To overcome these issues, cooperative spectrum sensing schemes were proposed to take advantage of spatial diversity. For cooperative spectrum sensing, all sensors sense the spectrum and report the sensed energy to FC for the final decision. However, it increases the energy consumption of the network when a large number of sensors need to cooperate; in addition to that, the efficiency of the network is also reduced. The proposed algorithm makes the cluster and selects the CHs such that very little amount of network energy is consumed and the highest efficiency of the network is achieved. Using the proposed algorithm maximum probability of detection under an imperfect channel is accomplished with minimum energy consumption as compared to conventional clustering schemes.

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An expansion cluster routing algorithm based on RSSI for an efficient data transmission

Cited by 5 publications

References 2 publications

Energy Efficient Receiver Signal Strength Indicator Based Clustered Routing Algorithm for Wireless Sensor Network

Energy Efficient Receiver Signal Strength Indicator Based Clustered Routing Algorithm for Wireless Sensor Network

QoS Oriented and Energy Efficient Routing Protocol for Cooperative MIMO Based Mobile WSN: Q-E2RPC

Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network

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