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Research On QoS Routing Algorithm For Wireless Multimedia Sensor Networks

Posted on:2010-08-13Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z W KeFull Text:PDF
GTID:1118360302481987Subject:Computer application technology
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With the technologies of multimedia sensors and embedded processors development, the research of Wireless Multimedia Sensor Networks (WMSNs) has been a gradual increase. Because multimedia data can often provide a wealth of information about surroundings of a network, wireless multimedia sensor networks can enhance the traditional sensor network applications and enable several new applications. For examples, the applications for military intelligence, surveillance, smart home care and target tracking are researched. In these applications in addition to end-to-end delay and bandwidth, more performance metrics, such as delay jitter and packet loss ratio must be considered by communication protocols, so the problem of routing for WMSNs has more challenging.More and more research of communication protocols uses game theories because of the sensor nodes in wireless sensor networks have potential selfish action. Based on studying and analysis of current relevant research contributions, this dissertation makes extensive research on how to provide routing protocols and algorithms with QoS guarantee in WMSNs. The main creative work can be seen as follows:(1) Point out a directed diffusion game routing protocol (DDGR).The sensor nodes are usually running on battery, it may refuse to transmit data to avoid excessive use of its energy, due to this potential selfish behavior, the research of routing protocols for wireless multimedia sensor networks can use game theories. In this dissertation, we provide a routing game model and a Directed Diffusion Game Routing protocol (DDGR). DDGR consists of four processes: interests'diffusion, QoS consultation, route reinforcement, and data transmission. The simulations indicate that DDGR can effectively improve the balance of energy consumption and prolong the lifetime of networks.(2) Point out an ant colony game routing algorithm (ACGR).An ant colony game routing algorithm based on ant colony routing algorithm is presented. The ants select its next hop based on Nash equilibrium of an ant routing game in ACGR. In the ant routing game model, we use the pheromone trails left by previous ants to define the payoffs of game players. We compare the performance of ACGR to the geography known ant colony routing algorithm, the simulations indicate that the performance of two algorithms is very similar, but because of ant colony game routing algorithm cancelled the iteration, the overhead of ACGR is far less than that of geography known ant colony routing algorithms.(3) Discuss the QoS routing for WMSNs based on crossover game.A genetic QoS routing algorithm based on crossover game is presented for wireless multimedia sensor networks. Using the mix Nash equilibrium of the crossover game instead of crossover probability selection can improve the speed of convergence and do not destroy the genetic pattern. The simulation results show that the performance of genetic QoS routing algorithm based on crossover game is better than the performance of basic genetic routing algorithm.(4) Discuss the many-to-one routing for WMSNs with genetic algorithmIn this dissertation, a many-to-one routing for WMSN based on genetic algorithm is discussed, and a selection game model is presented to improve the performance. The simulation results show that the algorithm with selection game can prolong the lifetime of networks.This dissertation is supported by National Natural Science Foundation of China (No.60672137), Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20060497015), Open projects of National Laboratory of Software Development Environment of China (No.SKLSDE-2009KF-2-02), and the Research Project of Hubei Province Department of Education in China (No. Q20082203,T200806).
Keywords/Search Tags:wireless sensor networks, QoS routing protocol, game theory, Nash equilibrium, optimization algorithms
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