Artificial Bee Colony based Energy Aware and Energy Efficient Routing Protocol

F.,

doi:10.3844/jcssp.2012.227.231

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…The proposed methodology has high efficiency in terms of utilizing the energy resources. Visu et al (2012;Yong-Chang and Gang, 2008) Various approaches are worked with in order to achieve high optimality and to route the data packets. The main key idea is to provide node identity with packet information which was achieved in the previous attempt in the field of Local field information.…”

Section: Proposed Methodologymentioning

confidence: 99%

Straight Line Routing on Next Hop Graph in Wireless Sensor Networks

Rani¹,

Kannan²

2015

Journal of Computer Science

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Information processing and information routing in wireless sensor network is one of the most important key area of recent research. Building an optimal routing protocol for information routing is a tough task in dealing with the sensor nodes. Information exchange can be done with the major possible way in order to provide a secure communication and reliable data delivery, here in this study we focus on routing protocol with reliable delivery. Hence we proposed a new algorithm/protocol which optimally routes the data packets from source to destination with efficient utilization of energy resources. The proposed protocol is based on next hop graph node in terms of reliable data packet routing by framing the route in the form of graphs. The simulation results showed in the performance analysis phase denotes the proposed method outperforms the existing routing protocol in terms of reliability and robustness in efficient routing. The proposed methodology achieves the connectivity and connectivity path in WSN which is critical in WSN.

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Section: Proposed Methodologymentioning

confidence: 99%

Straight Line Routing on Next Hop Graph in Wireless Sensor Networks

Rani¹,

Kannan²

2015

Journal of Computer Science

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“…Our approach only needs the local information about the nodes, which emits the Particle Swarm characteristics (Guo and Jia, 2012) of decentralized control and dynamic morphing topologies. Parallelly it supports multipath routing which enhances the operability and very less energy consumption during packet transmission (Visu et al, 2012).…”

Section: Full Description Of Wsn Routing Algorithm Based On Fast Reacmentioning

confidence: 99%

“…Wireless sensor network differs from other wireless network such ad hoc and infrastructure based which enables wide area of research in the sensor networks (Visu et al, 2012;Sedighizadeh and Masehian, 2009;Sombuntham and Kachitvichayanukul, 2010;Ruyan et al, 2010). Routing and optimality in energy consuming is one of the greatest challenges in the wireless sensor network.…”

Section: Introductionmentioning

confidence: 99%

“…In traditional wireless communication model ranges very large scale in energy consumption and high complexity to route the packets (Rong et al, 2005;Visu et al, 2012;Sedighizadeh and Masehian, 2009;Sombuntham and Kachitvichayanukul, 2010;Ruyan et al, 2010;Guo and Jia, 2012). In recent development in wireless communication which yields the sensor node to develop for multi-function (Arif and Rani, 2012) largely in sense of mobility patterns, random mobility, Probabilistic mobility, Controlled mobility, Predictable mobility (Alfawaer et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Ffa-Context Aware Energy Efficient Routing Using Fast Reactive and Adaptive Algorithm

Rani¹

2014

American Journal of Applied Sciences

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A vital issue of routing is how to efficiently strengthen energy consumption of the whole network and to avoid the energy-hole which leads to node failure or node death; this scenario makes the network to work improperly and leads to network death, Here we proves our hypothesis with linearity between routing and linear principles. Our proposed work effectively utilizes the adaption of Optimized particle swarm algorithm (Fast Reactive and fast adaptive algorithm) in order to solve the routing problem in wireless sensor networks to avoid the energy hole. Our approach effectively defines the node leader based on energy level and path optimization for node traversing using particle swarm which effectively utilizes the solution for problem in linearity into routing problem. Our algorithm FFA redefines the particle swarm rules which are reliable in routing strategies and adapts to the working principle. The optimization in our algorithm is to balance energy level and to withstand for its context. This study proves the accuracy using our algorithm respectively by theoretical survey and analysis with simulated results.

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“…So, it is bound with some defaults, which may affect in some particular data optimizations. In order to improve the performance, a new algorithm is proposed to replace the existing ABC algorithm (Dervis and Ozturk, 2011;Changsheng et al, 2010;Bahriye and Karaboga, 2012;Suguna, 2011;Visu et al, 2012). In this approach, an ABC algorithm with FCM operator is introduced to improve the optimization efficiency of ABC algorithm.…”

Section: Jcsmentioning

confidence: 99%

Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

Makkithaya¹,

Natarajan²

2013

Journal of Computer Science

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Clustering task aims at the unsupervised classification of patterns in different groups. To enhance the quality of results, the emerging swarm-based algorithms now-a-days become an alternative to the conventional clustering methods. In this study, an optimization method based on the swarm intelligence algorithm is proposed for the purpose of clustering. The significance of the proposed algorithm is that it uses a Fuzzy CMeans (FCM) operator in the Artificial Bee Colony (ABC) algorithm. The area of action of the FCM operator comes at the scout bee phase of the ABC algorithm as the scout bees are introduced by the FCM operator. The experimental results have shown that the proposed approach has provided significant results in terms of the quality of solution. The comparative study of the proposed approach with existing algorithms in the literature using the datasets from UCI Machine learning repository is satisfactory.

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Artificial Bee Colony based Energy Aware and Energy Efficient Routing Protocol

Cited by 5 publications

References 16 publications

Straight Line Routing on Next Hop Graph in Wireless Sensor Networks

Straight Line Routing on Next Hop Graph in Wireless Sensor Networks

Ffa-Context Aware Energy Efficient Routing Using Fast Reactive and Adaptive Algorithm

Artificial Bee Colony Algorithm Integrated With Fuzzy C-Mean Operator for Data Clustering

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