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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.


Generic multi-objective optimization problem in wireless sensor networks.
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f1-sensors-15-17572: Generic multi-objective optimization problem in wireless sensor networks.

Mentions: The generic multi-objective optimization problem consists of four segments: (1) inputs; (2) required output; (3) objectives; and (4) constraints. Figure 1 shows different possibilities for each part of the problem. In the generic resource allocation problem, the input parameters/decision variables are set by the network operators or the regulatory authorities. For example, selection of transmit frequency is influenced by the surrounding radio frequency environment and the regulatory rules. The selection of frequency can affect the transmission range of the sensors and ultimately many important performance parameters namely, coverage, bit error rate and delay. Increasing or decreasing the transmit power can significantly impact many desirable objectives namely, maximizing energy efficiency, link quality, network life time, reliability, coverage, cost and packet error rate. In [38], the authors have proposed an optimization formulation to maintain sensing coverage by keeping a minimum number of active sensor nodes and a small amount of energy consumption in wireless sensor network. Energy consumption has been considered in [39] by simultaneously satisfying delay and reliability through a multiobjecitve optimization algorithm. Total energy and residual energy of the nodes can also affect many performance indicators for example, coverage, throughput, network life time and packet error rate. A multi-objective formulation has been used in [40] to achieve a tradeoff solution between energy consumption and packet error rate. Location and density of the sensors determine the overall cost and the network performance in terms of observability, coverage, transmission range, reliability and energy consumption. Practical optimization problems relating to wireless sensor networks are constrained by many factors namely, network connectivity, interference, quality of service, transmit energy, coverage, topology, density, cost, latency, reliability and delay. These constrained optimization problems are expected to precipitate in optimal location of sensors, optimal number of sensors, optimal scheduling, optimal transmit power, optimal coverage, optimal throughput, optimal delay, optimal cost, optimal packet error rate, fairness and reliability. Nature of multi-objective optimization problem will change in accordance with certain input parameters, required objective function to optimize and the constraints imposed by the specific area of sensor network deployment.


Wireless Sensor Network Optimization: Multi-Objective Paradigm.

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

Generic multi-objective optimization problem in wireless sensor networks.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-15-17572: Generic multi-objective optimization problem in wireless sensor networks.
Mentions: The generic multi-objective optimization problem consists of four segments: (1) inputs; (2) required output; (3) objectives; and (4) constraints. Figure 1 shows different possibilities for each part of the problem. In the generic resource allocation problem, the input parameters/decision variables are set by the network operators or the regulatory authorities. For example, selection of transmit frequency is influenced by the surrounding radio frequency environment and the regulatory rules. The selection of frequency can affect the transmission range of the sensors and ultimately many important performance parameters namely, coverage, bit error rate and delay. Increasing or decreasing the transmit power can significantly impact many desirable objectives namely, maximizing energy efficiency, link quality, network life time, reliability, coverage, cost and packet error rate. In [38], the authors have proposed an optimization formulation to maintain sensing coverage by keeping a minimum number of active sensor nodes and a small amount of energy consumption in wireless sensor network. Energy consumption has been considered in [39] by simultaneously satisfying delay and reliability through a multiobjecitve optimization algorithm. Total energy and residual energy of the nodes can also affect many performance indicators for example, coverage, throughput, network life time and packet error rate. A multi-objective formulation has been used in [40] to achieve a tradeoff solution between energy consumption and packet error rate. Location and density of the sensors determine the overall cost and the network performance in terms of observability, coverage, transmission range, reliability and energy consumption. Practical optimization problems relating to wireless sensor networks are constrained by many factors namely, network connectivity, interference, quality of service, transmit energy, coverage, topology, density, cost, latency, reliability and delay. These constrained optimization problems are expected to precipitate in optimal location of sensors, optimal number of sensors, optimal scheduling, optimal transmit power, optimal coverage, optimal throughput, optimal delay, optimal cost, optimal packet error rate, fairness and reliability. Nature of multi-objective optimization problem will change in accordance with certain input parameters, required objective function to optimize and the constraints imposed by the specific area of sensor network deployment.

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.