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INTELLIGENT MULTIPLE-OBJECTIVE PROACTIVE ROUTING IN MANET WITH PREDICTIONS ON DELAY, ENERGY, AND LINK LIFETIME

Guo, Zhihao
http://rave.ohiolink.edu/etdc/view?acc_num=case1195705509

Abstract Details

2008, Doctor of Philosophy, Case Western Reserve University, Computer Engineering.
In this dissertation we develop an intelligent multiple objective routing mechanism that can be integrated with any MANET proactive protocol. Our system considers three routing objectives: minimizing average end-to-end delay, maximizing network energy lifetime, and maximizing packet delivery ratio. Our system measures and predicts on multiple dynamic network conditions including queuing delay, energy consumption, and link lifetime. Based on the predicted values, our system calculates multiple routing metrics including queuing delay, energy cost, and link stability cost, and updates the routing table with evaluation of these routing metrics. This dissertation is divided into three parts, part 1 and part 2 are dedicated to the exclusive handling of the metric of queuing delay and the metric of energy cost respectively, compositive handling of all three metrics are investigated in the part 3. These three parts are formatted as three independent papers. Multiple prediction methods are developed. Two optional types of feedforward neural network models, Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF), are used for queuing delay prediction in part I.; Autoregressive Integrated Moving Average (ARMIMA) are used for energy consumption prediction in part II; and double exponential smoothing is used for predicting both queuing delay and energy consumption in part III. Residual link lifetime is estimated using a heuristic of the pattern of link lifetime variation derived from the normal-like distributions of the link lifetimes in typical MANET mobility scenarios. In all the three parts, our system is integrated into OLSR, which is a well-known proactive MANET routing protocol. The regular topology control message exchange in OLSR is extended to convey the values of routing metrics. Our system uses a novel routing table computation algorithm called TierUp that evaluates node-state routing metrics (e.g., queuing delay) with reduced computational complexity compared to Dijkstra’s algorithm. Through extensive ns2 simulation our extended version of OLSR is capable of substantially improve the adaptability to the network dynamics and therefore achieve significant performance improvements in terms of all the three routing objectives. More over, our extended version of OLSR enables the routing decision maker to effectively set different preferences to different routing objectives.
Behnam Malakooti (Advisor)
OLSR_NN; OLSR_EA; OLSR_MO; multi-objective proactive MANET routing; TierUp; neural network; Multi-Layer Perceptron (MLP); Radial Basis Function (RBF); ARIMA; double exponential smoothing; normalized weighted utility function

Recommended Citations

Citations

  • Guo, Z. (2008). INTELLIGENT MULTIPLE-OBJECTIVE PROACTIVE ROUTING IN MANET WITH PREDICTIONS ON DELAY, ENERGY, AND LINK LIFETIME [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1195705509

    APA Style (7th edition)

  • Guo, Zhihao. INTELLIGENT MULTIPLE-OBJECTIVE PROACTIVE ROUTING IN MANET WITH PREDICTIONS ON DELAY, ENERGY, AND LINK LIFETIME. 2008. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1195705509.

    MLA Style (8th edition)

  • Guo, Zhihao. "INTELLIGENT MULTIPLE-OBJECTIVE PROACTIVE ROUTING IN MANET WITH PREDICTIONS ON DELAY, ENERGY, AND LINK LIFETIME." Doctoral dissertation, Case Western Reserve University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1195705509

    Chicago Manual of Style (17th edition)

case1195705509
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© 2007, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.