On improving the lifetime of wireless sensor networks using virtual scheduling backbone replacement

Sharmila, D.; Sujitha, R.; Rajkumar, G.

doi:10.1109/cict.2013.6558099

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Multiple parameters like Sensing Point size, mobile nature, amount of coverage and connections to the Sensing Point also have an impact on lifetime ratio. The scheduling based on backbone is helpful in reducing the energy consumption along with balance of energy consumption to improve lifetime ratio [13].…”

Section: Literaure Surveymentioning

confidence: 99%

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

L¹,

M²

2019

IJEAT

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WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

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Section: Literaure Surveymentioning

confidence: 99%

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

L¹,

M²

2019

IJEAT

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“…Nguyen et al [28] applied Virtual MIMO (VMIMO) concept to derive a distributed Cooperative Clustering Protocol (CCP), where they used VMIMO diversity gain by performing CN selection within each cluster. Sharmila et al [29] used the relationship between the data generated by each node and the distance between them to perform clustering. Applying CMIMO and data aggregation techniques authors derived energy efficient WSN routing protocol.…”

Section: Be Effective To Enable a Qos Oriented And Energy Efficient Rmentioning

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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“…A new energy model that considers the relationship between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. By using this model, the analysis can be done to check the effect of the cluster size on the average energy consumption per node [1].…”

Section: Combining Cooperative Mimo With Data Aggregationmentioning

confidence: 99%

“…A WSN is a co-operative network with collection of sensor nodes which are self organised through the radio links [1]. In the network, each node has the capability of processing themselves individually which contains multiple memory, transceiver and power source.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Wireless Sensor Networks based on Clustering Techniques

Kaur¹,

Waraich²

2015

IJCA

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In WSNs, Energy is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. These are battery powered sensor nodes. These small batteries have limited power and also may not easily rechargeable or removable. Long communication distance between sensors and a sink can greatly drain the energy of sensors and reduce the lifetime of a network. In WSNs, energy is a big factor to be considered. Various techniques are used to optimize energy level of sensor nodes of WSN. We will make a review of some of these wireless sensor network techniques which are used recently and are helpful in improving energy efficiency of wireless sensor nodes.

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On improving the lifetime of wireless sensor networks using virtual scheduling backbone replacement

Cited by 6 publications

References 13 publications

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

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

Energy Efficient Wireless Sensor Networks based on Clustering Techniques

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