Solving Wireless Network Scheduling Problem by Genetic Algorithm

Shams, Rehan; Khan, Fozia Hanif

doi:10.7718/iamure.ijmet.v4i1.420

Cited by 5 publications

(3 citation statements)

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“…Yang et al [24] have been applied a Binary Particle Swarm Optimization (BPSO) variant recently. Authors in [10,19] considered the standard Genetic Algorithm (GA) tool to provide the well-performing task allocation scheme. In this work, the proposed LGEA is used to search for the best task allocation scheme.…”

Section: Application To Task Allocation For Wireless Sensor Networkmentioning

confidence: 99%

Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

2016

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Evolutionary algorithms (EAs) are predominantly employed to find solutions for continuous optimization problems. As EAs are initially presented for continuous spaces, research on extending EAs to find solutions for binary spaces is in growing concern. In this paper, a logic gate-based evolutionary algorithm (LGEA) for solving some combinatorial optimization problems (COPs) is introduced. The proposed LGEA has the following features. First, it employs the logic operation to generate the trial population. Thereby, LGEA replaces common space transformation rules and classic recombination and mutation methods. Second, it is based on exploiting a variety of logic gates to search for the best solution. The variety among these logic tools will naturally lead to promote diversity in the population and improve global search abilities. The LGEA presents thus a new technique to combine the logic gates into the procedure of generating offspring in an evolutionary context. To judge the performance of the algorithm, we have solved the NP-hard multidimensional knapsack problem as well as a well-known engineering optimization problem, task allocation for wireless sensor network. Experimental results show that the proposed LGEA is promising.

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Section: Application To Task Allocation For Wireless Sensor Networkmentioning

confidence: 99%

Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

2016

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“…The node is localized by using the SVD trilateration method. The method use equations (7)(8)(9)(10)(11)(12)(13)(14) and estimated ranges (distances) from unknown node and anchor nodes. The distances are estimated by using the above methodology (based on RTT To A).…”

Section: Simulation Analysismentioning

confidence: 99%

“…There has been lot of research and development to tackle and address these issues [6], [7], [8], [9]. As with localization, the node has to be localize within the network from where the sensor data obtained as it is not viable to have the data without location stamps in many applications [10], [11]. In this paper, the methodology is purposed lo localized the unknown node based on the estimated range ( distance), channel impulse response and SVD trilateration method.…”

Section: Introductionmentioning

confidence: 99%

Localization of WSN node based on RTT ToA using ultra wide band & 802.15.4a channel

Meghani

Asif

2014

Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control

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Ahstract-A modified round trip time (RTT) time of arrival (ToA ) technique is present to measure the distance between two nodes in wireless sensor network. The ultra wide band spectrum and Gaussian monopulse shaping is used on MATLAB platform for simulations. The nodes are simulated under following envi ronments of 802.1S.4a channel model for distances measurement: residential, office, industrial, outdoor and open outdoor. The measured distances are analyzed and compared with set distances using calculated absolute error versus set distance graphs for different UWB frequencies, 802.1S.4a channels and sampling fre quencies. Finally, trilateration using singular value decomposition (SVD) method calculations are done for localization by using the measured distances. The position errors with respect to set positions are calculated for different trilateration layouts.

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Energy Efficient Vital Signs Monitoring System (VSMS) Using Wireless Sensor Networks

Hashmi

Alvi

Rehan

et al. 2014

Wireless Pers Commun

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Solving Wireless Network Scheduling Problem by Genetic Algorithm

Cited by 5 publications

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Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

Localization of WSN node based on RTT ToA using ultra wide band & 802.15.4a channel

Energy Efficient Vital Signs Monitoring System (VSMS) Using Wireless Sensor Networks

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Solving Wireless Network Scheduling Problem by Genetic Algorithm

Cited by 5 publications

References 0 publications

Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

Logic Gate-based Evolutionary Algorithm for the multidimensional knapsack problem-wireless sensor network application

Localization of WSN node based on RTT ToA using ultra wide band &amp; 802.15.4a channel

Energy Efficient Vital Signs Monitoring System (VSMS) Using Wireless Sensor Networks

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Localization of WSN node based on RTT ToA using ultra wide band & 802.15.4a channel