Energy Optimization in Wireless Sensor Network through Natural Science Computing: A Survey

Wagh, Sanjeev J.; Prasad, Ramjee

doi:10.13052/jge1904-4720.342

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…It results in better residual energy as compared to other protocol. An increase in the node degree leads to an increase in the number of messages exchanged [40]. Poly has been providing better residual energy because.…”

Section: Complexity Analysis Of Poly and Proposed Protocolmentioning

confidence: 99%

Retracted: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network

Bhende

Wagh

2015

JGE

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Energy consumption in wireless sensor network is of paramount importance, which is demonstrated by a large number of algorithms, techniques and protocols that have been developed to save energy and to extend lifetime. Poly is the topology construction protocol. It is based on the idea of a polygon. Lifetime extension is one of the most critical research issues in the area of Wireless Sensor Network due to the severe resource limitation of sensor nodes. One of the key approaches for prolonging the sensor network's operable lifetime is to deploy an effective topology control protocol. We propose a Topology control algorithm for intelligent and reliable clustering, introducing energy harvesting nodes for maximizing the lifetime of network by supporting energy backup in the sensor field, proper localization of base station in the field to minimize the communication distance between cluster heads and the base station. Our simulation results demonstrate that IPoly performs consistently better in terms of energy efficiency message overhead, Energy overhead and reliability.

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Section: Complexity Analysis Of Poly and Proposed Protocolmentioning

confidence: 99%

Retracted: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network

Bhende

Wagh

2015

JGE

Self Cite

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“…Type‐2 fuzzy logic systems are developed to handle uncertainties in the linguistic definition of membership functions that exist in type‐1 fuzzy systems. Normal fuzzy systems are called as type‐1 fuzzy systems in which only the words are inferred to be uncertain 31–34 . It is known that membership functions formulate the fuzzy inputs and outputs, and the observed complete uncertainties are modelled into membership functions of the corresponding fuzzy sets.…”

Section: Type‐2 Fuzzy Logic Systemsmentioning

confidence: 99%

Energy optimization of internet of things in wireless sensor network models using type‐2 fuzzy neural systems

Rasi

Deepa

2021

Int J Communication

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Summary In this paper, type‐2 fuzzy logic design is employed to find the weight values of the radial basis function (RBF) neural network model, and thereby, the trained RBF neural network (RBFNN) model is intended to perform network energy optimization of the cloud‐assisted internet of things in wireless sensor networks (WSNs). RBF neural model comes under the class of feed forward neural network architecture and is a network with better generalization capability. RBFNN employs Gaussian activation function to determine the output of the network and the special feature of this activation function is that it follows a normal probability density function; hence it is a continuous activation function and provides better solutions than the other discrete activation functions. Due to which, in this work RBFNN is employed to determine network energy and to increase the life time of the network by selecting best cluster heads and also the network route. In RBFNN modelling, basically, the weights are initialized in a random manner, and this random initialization of weights at time results in the occurrences of global and local minima. The weights are tuned for their optimal values using type‐2 fuzzy model due to their capability of handling uncertainties, and since this problem of identifying optimal weight values of RBF neural model possesses highest level of uncertainties, type‐2 fuzzy system is applied here. Simulation process is done, and the results prove the effectiveness of the proposed approach in comparison with that of the existing approaches from previous literatures for the energy optimization problem.

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“…Most of the techniques are SSN based and very few are MSN and hybrid based. Bio-inspired optimization strategies are also implemented to optimize the performance of their work [16], [17]. Some of the techniques are now discussed.…”

Section: Literature Surveymentioning

confidence: 99%

Self Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

Nighot¹,

Ghatol²,

Thakare³

2018

IJIMAI

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Energy Optimization in Wireless Sensor Network through Natural Science Computing: A Survey

Cited by 6 publications

References 22 publications

Retracted: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network

Retracted: Reliable and Energy Efficient Topology Control Algorithm Based on Connected Dominating Set for Wireless Sensor Network

Energy optimization of internet of things in wireless sensor network models using type‐2 fuzzy neural systems

Self Organized Hybrid Wireless Sensor Network for Finding Randomly Moving Target in Unknown Environment

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