Deliver more for less Energy

Most existing MANET Proactive routing protocols rely heavily on hopcount evaluation. Although this is simple and efficient, it sacrificesthe potential performance gains obtainable by considering dynamicnetwork conditions such as congestion levels and link stability.Remedies of this issue have been presented by considering variouspredictive dynamic routing metrics mostly related to bandwidth andenergy consumption. However, those predictions do not fully appreciatethe nonstationary nature of the dynamic routing metrics and possibleheterogeneous power consumption of the nodes. With these addedconcerns, in this work, in-depth investigation is conducted onprediction and evaluation of dynamic routing metrics including queuingdelay, energy cost, and link stability. Artificial neural network modelsand advanced statistical methods are used for prediction of thesemetrics. Queuing delay and energy cost are investigated individuallyand then evaluated compositively together with predicted link stability. Their effects on multiple routingobjectives are extensively simulated and analyzed.

Autorentext

Zhihao Guo obtained his Ph.D. degree in Computer Engineering from Case Western Reserve University in January 2008 and his Master''s degree in Electrical Engineering from the University of Alabama at Birmingham in May 2002. His research interests include routing, broadcasting, and topology control in mobile ad hoc networks, and wireless network emulation.

Klappentext

Most existing MANET Proactive routing protocols rely heavily on hop count evaluation. Although this is simple and efficient, it sacrifices the potential performance gains obtainable by considering dynamic network conditions such as congestion levels and link stability. Remedies of this issue have been presented by considering various predictive dynamic routing metrics mostly related to bandwidth and energy consumption. However, those predictions do not fully appreciate the nonstationary nature of the dynamic routing metrics and possible heterogeneous power consumption of the nodes. With these added concerns, in this work, in-depth investigation is conducted on prediction and evaluation of dynamic routing metrics including queuing delay, energy cost, and link stability. Artificial neural network models and advanced statistical methods are used for prediction of these metrics. Queuing delay and energy cost are investigated individually and then evaluated compositively togeth er with predicted link stability. Their effects on multiple routing objectives are extensively simulated and analyzed.