Face routing is a simple method for routing in wireless ad-hoc networks. It only uses location information about nodes to do routing and it provably guarantees message delivery in static connected plane graphs. However, a static connected plane graph is often difficult to obtain in a real wireless network. This thesis extends face routing to more realistic models of wireless ad-hoc networks. We present a new version of face routing that generalizes and simplifies previous face routing protocols and develop techniques to apply face routing directly on general, non-planar network graphs. We also develop techniques for face routing to deal with changes to the graph that occur during routing. Using these techniques, we create a collection of face routing protocols for a series of increasingly more general graph models and prove the correctness of these protocols.

Face routing is a simple method for routing in wireless ad-hoc networks. It only uses location information about nodes to do routing and it provably guarantees message delivery in static connected plane graphs. However, a static connected plane graph is often difficult to obtain in a real wireless network. This thesis extends face routing to more realistic models of wireless ad-hoc networks. We present a new version of face routing that generalizes and simplifies previous face routing protocols and develop techniques to apply face routing directly on general, non-planar network graphs. We also develop techniques for face routing to deal with changes to the graph that occur during routing. Using these techniques, we create a collection of face routing protocols for a series of increasingly more general graph models and prove the correctness of these protocols.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

Energy saving, despite recent advances in extending battery life, is still an important issue in wireless ad hoc networks. An often adopted method is power management, which can help in reducing the transmission power consumption and thus can prolong the battery life of mobile nodes. In this paper, we present a new approach of power management for the wireless ad-hoc networks. Firstly, we propose a clustering algorithm. The clustering algorithm is incooperated with power adjustment and energy-efficient routing procedure to achieve the goal of reducing the transmission power. We use clusterheads to monitor a mobile node's transmission power and to conduct the routing path between any source-destination pair. Not only the lifetime of network is increased but also the interference in communication channel is reduced. As a result, the transmission quality is improved and the network throughput is enhanced. By simulation, we showed that our algorithm outperforms the traditional clustering algorithm both in power saving and in throughput.

In scuba diving any problem that can be solved underwater will be solved underwater. This helps to prevent a dive from being disrupted. If a diver is separated from the group and is unable to find the group within a short time both the diver and dive group must surface to find each other and rejoin. To prevent the separation of divers a Stray Diver Alert System has been devised involving wireless communication to track the diver's position relative to the dive masters. Underwater communication holds many challenges that are not found in above water networks. Through simulation, it has been shown that the communication requirements for the Stray Diver Alert can be met with existing technology and protocols. This has been done by evaluating the resolution, power consumption and physical size of the device for three different communication protocols. This has shown that current technology is capable of meeting the requirements of the stray diver alert system.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

In this thesis, we propose a novel trust management scheme for improving routing reliability in wireless ad hoc networks. It is grounded on two classic autoregression models, namely Autoregressive (AR) model and Autoregressive with exogenous inputs (ARX) model. According to this scheme, a node periodically measures the packet forwarding ratio of its every neighbor as the trust observation about that neighbor. These measurements constitute a time series of data. The node has such a time series for each neighbor. By applying an autoregression model to these time series, it predicts the neighbors future packet forwarding ratios as their trust estimates, which in turn facilitate it to make intelligent routing decisions. With an AR model being applied, the node only uses its own observations for prediction; with an ARX model, it will also take into account recommendations from other neighbors. We evaluate the performance of the scheme when an AR, ARX or Bayesian model is used. Simulation results indicate that the ARX model is the best choice in terms of accuracy.

No description available.

No description available.