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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.

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Trend of research community w.r.t. nature of Multi-objective Optimization (MOO) formulations.
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f8-sensors-15-17572: Trend of research community w.r.t. nature of Multi-objective Optimization (MOO) formulations.

Mentions: In various applications of wireless sensor networks, the desirable objectives including but not limited to maximization of coverage, maximization of battery life, maximization of energy efficiency, minimization of cost, minimization of delay, maximization of throughput and minimization of packet error rate are formulated by using different optimization formulations. Different practical scenarios related to optimization give rise to different nature of optimization problem. Figure 8 shows a glimpse of the trend relating to different optimization formulations. It is evident that most of the desirable scenarios culminate in NP-Hard optimization formulations. For example in [207], optimization of connectivity, coverage, cost, network lifetime and service quality has been formulated as NP-Hard optimization problem. The problem of optimal channel assignment to maximize the throughput, improve fairness and handoff experience of the users have been formulated as NP-Hard problem in [195]. The other commonly used optimization formulations are combinatorial, non-convex, convex, mixed-integer linear programming, linear programming, non-linear programming, NP-Complete, mixed-integer non-linear programming, integer linear programming and concave.


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. nature of Multi-objective Optimization (MOO) formulations.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-15-17572: Trend of research community w.r.t. nature of Multi-objective Optimization (MOO) formulations.
Mentions: In various applications of wireless sensor networks, the desirable objectives including but not limited to maximization of coverage, maximization of battery life, maximization of energy efficiency, minimization of cost, minimization of delay, maximization of throughput and minimization of packet error rate are formulated by using different optimization formulations. Different practical scenarios related to optimization give rise to different nature of optimization problem. Figure 8 shows a glimpse of the trend relating to different optimization formulations. It is evident that most of the desirable scenarios culminate in NP-Hard optimization formulations. For example in [207], optimization of connectivity, coverage, cost, network lifetime and service quality has been formulated as NP-Hard optimization problem. The problem of optimal channel assignment to maximize the throughput, improve fairness and handoff experience of the users have been formulated as NP-Hard problem in [195]. The other commonly used optimization formulations are combinatorial, non-convex, convex, mixed-integer linear programming, linear programming, non-linear programming, NP-Complete, mixed-integer non-linear programming, integer linear programming and concave.

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.