A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA

Norouzi, Ali; Babamir, Faezeh Sadat; Orman, Zeynep

doi:10.4236/wsn.2012.48028

Cited by 26 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is assumed that energy consumption under mode A is four times more than that of C , and that of B in turn is 2 times more than that of C . EC is obtained through [ 25 ].…”

Section: Data Aggregation In Wsnmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

Norouzi

Zaim

2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.

show abstract

“…It is assumed that energy consumption under mode A is four times more than that of C , and that of B in turn is 2 times more than that of C . EC is obtained through [ 25 ].…”

Section: Data Aggregation In Wsnmentioning

confidence: 99%

“…The final individuals provide route with almost uniform energy consumption. This feature makes a great contribution to the network's lifetime [ 25 ].…”

Section: Data Aggregation In Wsnmentioning

confidence: 99%

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

Norouzi

Zaim

2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…N slots for forwarding data of children and one slot to transmit its own data. 4. Once time-slot is assigned to i, its parent j is processed.…”

Section: B Slot Assignmentmentioning

confidence: 99%

TDMA scheduling of group aware tree in Wireless Sensor Networks

Vasavada

Srivastava

2014

2014 Annual IEEE India Conference (INDICON)

View full text Add to dashboard Cite

Data collection is an important operation in Wireless Sensor Networks. Sensors are deployed in given region of interest. They sense physical phenomenon and transfer data to a central node known as Sink. Tree is a widely used routing topology and Time Division Multiple Access (TDMA) is a widely used Medium Access Control (MAC) protocol. Sink is at the root of the tree. Often nodes in a tree aggregate the data coming from children with their own data. Data aggregation results in energy conservation and reduction in network traffic. Often sensor networks are heterogeneous in nature. Multiple groups (or types) of sensors are deployed in given region. In heterogeneous network, tree formation should consider presence of different groups. That is, a node should select node of same group as itself as parent. This is known as group aware tree formation. Other approach is static tree formation. In static tree formation, parent is selected without considering its group. Group aware tree formation increases chances of aggregation. Inspired from work done in [9] and [10], we have implemented group aware tree formation .Group aware tree formation is followed by TDMA scheduling of edges. Performance measures like energy consumption and number of time-slots used are examined. It is concluded that group aware tree formation results in less number of required timeslots compared to static tree and energy consumption is also less.

show abstract

“…To address this issue Norouzi et al [125] presented a method that utilizes genetic algorithm to find routes which balance energy and data load in a network. In this study, nodes monitor the area to aggregate data and then remove the redundant nodes in order to aggregate them according to the data aggregation spanning tree.…”

Section: Survey Of Existing Workmentioning

confidence: 99%

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Adnan

Razzaque

Ahmed

et al. 2013

Sensors

View full text Add to dashboard Cite

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

show abstract

A Tree Based Data Aggregation Scheme for Wireless Sensor Networks Using GA

Cited by 26 publications

References 6 publications

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

Genetic Algorithm Application in Optimization of Wireless Sensor Networks

TDMA scheduling of group aware tree in Wireless Sensor Networks

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Contact Info

Product

Resources

About