Particle swarm optimization applications to mpeg-4 transmission over ZigBee
type: CreativeWork
description: The IEEE 802.15.4 standard commonly known as ZigBee is a wireless sensor targeted at applications that require low data rate, low power and less expense. IEEE 802.15.4 standard is limited to a through-rate of 250kbps maximum providing supports for small packet file transitions and is design to provide highly efficient connectivity. Hence, IEEE 802.15.4 is not designed and cannot be used to transfer large amounts of data. Therefore, in this research MPEG-4 video transmission over ZigBee is the aim as its bandwidth is too low and the limitation could become a real problem which makes the video transmission over IEEE 802.15.4 networks difficult to achieve. Due to the low bandwidth of the ZigBee any large amount of data needs to be optimized at the targeted bitrate. Optimization techniques are widely used in engineering and computer science as well as being used in real environment applications to overcome complex issues and in particular; an artificial intelligence technique known as Particle Swann Optimization (PSO) has becoming very popular. PSO is a population-based stochastic optimization technique, inspired by the social behavior of flocks of birds, and colonies of ants and bees. Such intelligence is decentralized, self-organized and distributed throughout an environment and used by swarm to solve problems. In this research, the problem solving strategy decided on the use of PSO to optimize the transmission of MPEG-4 video over ZigBee, which requires a much lower computation and accordingly it can be executed faster. A novel solution to transmit MPEG-4 over IEEE 802.15.4 has been developed and this research further utilizes a technique to regulate the quantization patterns and output an optimal frame rate by using an "Adaptive Scalar Quantization", which prevents excessive data loss of MPEG-4 video over IEEE 802.15.4 transmission. The computer simulation results confirm that adaptive scalar quantization video coding do improve the quality of picture and reduce dat…
abstract: The IEEE 802.15.4 standard commonly known as ZigBee is a wireless sensor targeted at applications that require low data rate, low power and less expense. IEEE 802.15.4 standard is limited to a through-rate of 250kbps maximum providing supports for small packet file transitions and is design to provide highly efficient connectivity. Hence, IEEE 802.15.4 is not designed and cannot be used to transfer large amounts of data. Therefore, in this research MPEG-4 video transmission over ZigBee is the aim as its bandwidth is too low and the limitation could become a real problem which makes the video transmission over IEEE 802.15.4 networks difficult to achieve. Due to the low bandwidth of the ZigBee any large amount of data needs to be optimized at the targeted bitrate. Optimization techniques are widely used in engineering and computer science as well as being used in real environment applications to overcome complex issues and in particular; an artificial intelligence technique known as Particle Swann Optimization (PSO) has becoming very popular. PSO is a population-based stochastic optimization technique, inspired by the social behavior of flocks of birds, and colonies of ants and bees. Such intelligence is decentralized, self-organized and distributed throughout an environment and used by swarm to solve problems. In this research, the problem solving strategy decided on the use of PSO to optimize the transmission of MPEG-4 video over ZigBee, which requires a much lower computation and accordingly it can be executed faster. A novel solution to transmit MPEG-4 over IEEE 802.15.4 has been developed and this research further utilizes a technique to regulate the quantization patterns and output an optimal frame rate by using an "Adaptive Scalar Quantization", which prevents excessive data loss of MPEG-4 video over IEEE 802.15.4 transmission. The computer simulation results confirm that adaptive scalar quantization video coding do improve the quality of picture and reduce dat…
author: Samizadeh, Iman
keywords: 620 Engineering & allied operations
date published: 2012
date modified: 2026-07-24T02:54:38Z
provider: London Metropolitan University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: London Metropolitan University