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Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Iqbal M, Naeem M, Anpalagan A, Ahmed A, Azam M - Sensors (Basel) (2015)

Bottom Line: We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints.A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks.Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Campus, Wah Cantt 47040, Pakistan. miqbal1976@gmail.com.

ABSTRACT
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

No MeSH data available.


Trend of research community w.r.t. multi-objective optimization techniques. Where, EC = Epsilon constrained, Lex = Lexicographic, WSS = Weighted sum of square, WCN = Weighted chevyshev norm, NBI = Normal boundary intersection, PAES = Pareto archived evolution strategy, WAVG = Weighted average, WSUM = Weighted sum and PO = Pareto optimal.
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f6-sensors-15-17572: Trend of research community w.r.t. multi-objective optimization techniques. Where, EC = Epsilon constrained, Lex = Lexicographic, WSS = Weighted sum of square, WCN = Weighted chevyshev norm, NBI = Normal boundary intersection, PAES = Pareto archived evolution strategy, WAVG = Weighted average, WSUM = Weighted sum and PO = Pareto optimal.

Mentions: Contrary to the single objective optimization, a solution to a multi-objective problem is a concept rather than a definition [3]. Therefore, there is usually no single global solution, and it is therefore necessary to find a set of solutions satisfying the optimality conditions. Pareto optimal solutions consist of solutions that are not dominated by any other solutions. A solution X is said to dominate Y if X is better or equal to Y in all attributes, and strictly better in at least one attribute [12]. Therefore, Pareto optimal solutions provide different trade-off scenarios where none is better than the other and the decision maker chooses one according to the preferences or specific requirements. Due to the its characteristics to achieve different trade-off solutions, Pareto optimal solution approaches are being preferred which is evident from Figure 6. In more than half of the articles in the literature, Pareto optimal approach has been used to solve the multi-objective optimization problems. Other commonly used technique is the weighted sum approach. It scalarizes a set of objectives into a single objective by assigning different weights to each objective. Conceptually this method is simplest and also widely used but it is affected by the selection of different weights. The selection of weights depends on the preference of each objective which is decided by the decision maker [11]. Therefore, the outcome of the approach is highly sensitive to the choice of the weights. There are few other less commonly used approaches namely, weighted average, Pareto archived evolution strategy, normal boundary intersection, weighted Chevyshev norm, weighted sum of square, lexicographic and epsilon constrained.


Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Iqbal M, Naeem M, Anpalagan A, Ahmed A, Azam M - Sensors (Basel) (2015)

Trend of research community w.r.t. multi-objective optimization techniques. Where, EC = Epsilon constrained, Lex = Lexicographic, WSS = Weighted sum of square, WCN = Weighted chevyshev norm, NBI = Normal boundary intersection, PAES = Pareto archived evolution strategy, WAVG = Weighted average, WSUM = Weighted sum and PO = Pareto optimal.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541950&req=5

f6-sensors-15-17572: Trend of research community w.r.t. multi-objective optimization techniques. Where, EC = Epsilon constrained, Lex = Lexicographic, WSS = Weighted sum of square, WCN = Weighted chevyshev norm, NBI = Normal boundary intersection, PAES = Pareto archived evolution strategy, WAVG = Weighted average, WSUM = Weighted sum and PO = Pareto optimal.
Mentions: Contrary to the single objective optimization, a solution to a multi-objective problem is a concept rather than a definition [3]. Therefore, there is usually no single global solution, and it is therefore necessary to find a set of solutions satisfying the optimality conditions. Pareto optimal solutions consist of solutions that are not dominated by any other solutions. A solution X is said to dominate Y if X is better or equal to Y in all attributes, and strictly better in at least one attribute [12]. Therefore, Pareto optimal solutions provide different trade-off scenarios where none is better than the other and the decision maker chooses one according to the preferences or specific requirements. Due to the its characteristics to achieve different trade-off solutions, Pareto optimal solution approaches are being preferred which is evident from Figure 6. In more than half of the articles in the literature, Pareto optimal approach has been used to solve the multi-objective optimization problems. Other commonly used technique is the weighted sum approach. It scalarizes a set of objectives into a single objective by assigning different weights to each objective. Conceptually this method is simplest and also widely used but it is affected by the selection of different weights. The selection of weights depends on the preference of each objective which is decided by the decision maker [11]. Therefore, the outcome of the approach is highly sensitive to the choice of the weights. There are few other less commonly used approaches namely, weighted average, Pareto archived evolution strategy, normal boundary intersection, weighted Chevyshev norm, weighted sum of square, lexicographic and epsilon constrained.

Bottom Line: We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints.A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks.Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Campus, Wah Cantt 47040, Pakistan. miqbal1976@gmail.com.

ABSTRACT
Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

No MeSH data available.