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.

Localization and coverage are two important and closely related problems in wireless ad hoc networks. Localization aims to determine the physical locations of devices in a network, while coverage determines if a region of interest is sufficiently monitored by devices. Localization systems require a high degree of coverage for correct functioning, while coverage schemes typically require accurate location information. This thesis investigates the relationship between localization and coverage such that new schemes can be devised which integrate approaches found in each of these well studied problems. This work begins with a thorough review of the current literature on the subjects of localization and coverage. The localization scheduling problem is then introduced with the goal to allow as many devices as possible to enter deep sleep states to conserve energy and reduce message overhead, while maintaining sufficient network coverage for high localization accuracy. Initially this sufficient coverage level for localization is simply a minimum connectivity condition. An analytical method is then proposed to estimate the amount of localization error within a certain probability based on the theoretical lower bounds of location estimation. Error estimates can then be integrated into location dependent schemes to improve on their robustness to localization error. Location error estimation is then used by an improved scheduling scheme to determine the minimum number of reference devices required for accurate localization. Finally, an optimal coverage preserving sleep scheduling scheme is proposed which is robust to localization error, a condition which is ignored by most existing solutions. Simulation results show that with localization scheduling network lifetimes can be increased by several times and message overhead is reduced while maintaining negligible differences in localization error. Furthermore, results show that the proposed coverage preserving sleep scheduling scheme results in fewer active devices and coverage holes under the presence of localization error.

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.

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Prior research in routing for wireless ad hoc networks has shown that multipath routing can enhance data delivery reliability and provide load balancing. Nevertheless, only a few multipath routing algorithms have been proposed and their interaction with transport layer protocols has not been thoroughly addressed in the literature. In this work, we propose the multipath “Fresnel zone” routing (FZR) algorithm for wireless ad hoc networks. FZR constructs multiple parallel paths from source to destination based on the concept of “Fresnel zones” in a wireless network. The zone construction method assigns intermediate routers into different “Fresnel zones” according to their capacity and efficiency in forwarding traffic. The central idea in FZR is to disperse traffic to different zones according to network load and congestion conditions, thus achieving better throughput and avoiding congestion at intermediate routers. FZR differs from most existing multipath routing approaches in that both source and intermediate nodes use multiple forwarding paths. FZR also adopts a combination of proactive and on-demand (reactive) approaches to reduce control overhead and latency for packet delivery. Simulation experiments have shown that FZR outperforms unipath distance vector routing, multipath distance vector (MDV) routing, and split multipath routing (SMR) algorithms in quasistatic wireless ad hoc networks. In our simulations, FZR achieves up to 100 percent higher average throughput using the User Datagram Protocol (UDP) and 50 percent higher average throughput using the Transmission Control Protocol (TCP). FZR can also provide better load balancing among different paths, improve network resource utilization, and enable fairer resource allocation among different data transmission sessions. Future work is needed to evaluate FZR in mobile scenarios. / Master of Science

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / An ad hoc network is a group of wireless nodes which do not rely on any fixed infrastructure. Hosts cooperate by forwarding packets for each other to communicate with nodes that are out of the radio transmission range. We propose a new routing algorithm that is based on the concept of multipoint relay nodes (MPR). The main focus of the Trust Level Routing protocol is the reliability and survivability of the network by applying costs to each MPR candidate. The cost calculation is based on the delay incurred, energy available at the MPR node, energy spent during transmission and number of packets sent on each link. We highlight the vulnerabilities in current link state routing algorithms and propose the use of light weight encryption algorithms to achieve a dependable routing algorithm. Network simulator (ns-2) is used to compare the protocol performance to other existing link state routing protocols.

Wireless ad hoc networks are expected to play an important role in civilian and military settings where wireless access to wired backbone is either ineffective or impossible. Wireless sensor networks are effective in remote data acquisition. Congestion control and power consumption in wireless ad hoc networks have received a lot of attention in recent research. Several algorithms have been proposed to reduce congestion and power consumption in wireless ad hoc and sensor networks. In this thesis, we focus upon two schemes, which deal with congestion control and power consumption issues. This thesis consists of two parts. In the first part, we describe a randomization scheme for congestion control in dynamic source routing protocol, which we refer to as RDSR. We also study a randomization scheme for GDSR protocol, a GPS optimized variant of DSR. We discuss RDSR and RGDSR implementations and present extensive simulation experiments to study their performance. Our results indicate that both RGDSR and RDSR protocols outperform their non-randomized counterparts by decreasing the number of route query packets. Furthermore, a probabilistic congestion control scheme based on local tuning of routing protocol parameters is shown to be feasible. In the second part we present a simulation based performance study of energy aware data centric routing protocol, EAD, proposed by X. Cheng and A. Boukerche. EAD reduces power consumption by requiring only a small percentage of the network to stay awake. Our experiments show that EAD outperforms the well-known LEACH scheme.

The growing number of wireless devices and wireless systems present many challenges on the design and operation of these networks. We focus on massively dense ad hoc networks and cellular systems. We use the continuum modeling approach, useful for the initial phase of deployment and to analyze broad-scale regional studies of the network. We study the routing problem in massively dense ad hoc networks, and similar to the work of Nash, and Wardrop, we define two principles of network optimization: user- and system-optimization. We show that the optimality conditions of an appropriately constructed optimization problem coincides with the user-optimization principle. For different cost functions, we solve the routing problem for directional and omnidirectional antennas. We also find a characterization of the minimum cost paths by extensive use of Green's theorem in directional antennas. In many cases, the solution is characterized by a partial differential equation. We propose its numerical analysis by finite elements method which gives bounds in the variation of the solution with respect to the data. When we allow mobility of the origin and destination nodes, we find the optimal quantity of active relay nodes. In Network MIMO systems and MIMO broadcast channels, we show that, even when the channel offers an infinite number of degrees of freedom, the capacity is limited by the ratio between the size of the antenna array at the base station and the mobile terminals position and the wavelength of the signal. We also find the optimal mobile association for the user- and system-optimization problem under different policies and distributions of the users.