Ruan, Sichao. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 62-65). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Multihop Ad Hoc Wireless Networks --- p.1 / Chapter 1.1.1 --- Introduction to Multihop Ad Hoc Networks --- p.2 / Chapter 1.1.2 --- Scalability of Ad Hoc Wireless Networks --- p.3 / Chapter 1.2 --- Hidden Terminal Problem --- p.3 / Chapter 1.3 --- Exposed Terminal Problem --- p.5 / Chapter 1.4 --- Overview of the Thesis --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- MAC Protocols for Wireless Networks --- p.8 / Chapter 2.1.1 --- Aloha --- p.8 / Chapter 2.1.2 --- CSMA/CA --- p.9 / Chapter 2.1.3 --- IEEE 802.11 DCF Standard --- p.10 / Chapter 2.2 --- Related Work --- p.12 / Chapter 2.2.1 --- Schemes for Hidden Node Problem --- p.12 / Chapter 2.2.2 --- Schemes for Exposed Node Problem --- p.13 / Chapter 2.3 --- Tradeoff between Hidden and Exposed Nodes --- p.14 / Chapter 2.4 --- The Effect of Carrier Sensing Range --- p.17 / Chapter 3 --- Analysis on Carrier Sensing Range --- p.18 / Chapter 3.1 --- Analysis Model --- p.18 / Chapter 3.1.1 --- Terminal Configurations --- p.18 / Chapter 3.1.2 --- Timing/Packet Parameters --- p.19 / Chapter 3.1.3 --- Protocol Approximation --- p.20 / Chapter 3.1.4 --- Throughput Measurement --- p.21 / Chapter 3.2 --- Derivation of Throughput --- p.21 / Chapter 3.2.1 --- Channel Modeling --- p.22 / Chapter 3.2.2 --- Actual Transmission Rate --- p.24 / Chapter 3.2.3 --- Case One --- p.24 / Chapter 3.2.4 --- Case Two --- p.26 / Chapter 3.2.5 --- Mathematical Form of Throughput --- p.28 / Chapter 3.2.6 --- Analysis Results --- p.30 / Chapter 3.3 --- Implications --- p.31 / Chapter 3.3.1 --- Value of Sensing Range in CSMA/CA --- p.31 / Chapter 3.3.2 --- Need for New MAC Protocols --- p.32 / Chapter 4 --- MAC Protocols by Congestion Control --- p.34 / Chapter 4.1 --- Motivations and Principles --- p.34 / Chapter 4.1.1 --- Balancing Hidden and Exposed Nodes --- p.35 / Chapter 4.1.2 --- Controlling Carrier Sensing Range --- p.36 / Chapter 4.1.3 --- Non-homogenous Sensing Range --- p.36 / Chapter 4.2 --- Algorithm Descriptions --- p.38 / Chapter 4.2.1 --- Core Concept --- p.38 / Chapter 4.2.2 --- LDMI Control Scheme --- p.40 / Chapter 4.2.3 --- Tahoe Control Scheme --- p.41 / Chapter 5 --- Simulation Analysis --- p.44 / Chapter 5.1 --- Simulation Configurations --- p.44 / Chapter 5.1.1 --- Geometric Burst Traffic Model --- p.45 / Chapter 5.1.2 --- Network Topology --- p.46 / Chapter 5.1.3 --- Simulation Parameters --- p.47 / Chapter 5.2 --- Throughput Comparisons --- p.48 / Chapter 5.3 --- Fairness Comparisons --- p.50 / Chapter 5.3.1 --- Situation of Unfairness --- p.50 / Chapter 5.3.2 --- Fairness Measurement --- p.52 / Chapter 5.4 --- Convergence Comparisons --- p.54 / Chapter 5.5 --- Summary of Performance Comparison --- p.55 / Chapter 6 --- Conclusions --- p.56 / Chapter A --- Categories of CSMA/CA --- p.58 / Chapter A.1 --- 1-persistent CSMA/CA --- p.58 / Chapter A.2 --- non-persistent CSMA/CA --- p.58 / Chapter A.3 --- p-persistent CSMA/CA --- p.59 / Chapter B --- Backoff Schemes --- p.60 / Chapter B.1 --- Constant Window Backoff Scheme --- p.60 / Chapter B.2 --- Geometric Backoff Scheme --- p.60 / Chapter B.3 --- Binary Exponential Backoff Scheme --- p.61 / Bibliography --- p.62

Ke, Bingwen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 52-54). / Abstracts in Chinese and English. / Abstract --- p.3 / Acknowledgement --- p.5 / Content --- p.6 / Table of Figures --- p.8 / Chapter Chapter 1 --- Introduction --- p.9 / Chapter 1.1 --- Motivations and Contributions --- p.9 / Chapter 1.2 --- Organization of the Thesis --- p.11 / Chapter Chapter 2 --- Background --- p.12 / Chapter 2.1 --- Multiple-Input-Multiple-Output (MIMO) System --- p.12 / Chapter 2.1.1 --- Basic MIMO Structure --- p.12 / Chapter 2.1.2 --- Multiple User Detection (MUD) in MIMO Networks --- p.14 / Chapter 2.2 --- IEEE 802.11 --- p.16 / Chapter 2.2.1 --- CSMA/CA in 802.11 --- p.16 / Chapter 2.2.2 --- CSMA/CA(k) in 802.1 In --- p.18 / Chapter 2.2.3 --- Co-channel Transmission in MIMO WLAN --- p.19 / Chapter Chapter 3 --- Channel Correlation in MIMO Ad Hoc Networks --- p.20 / Chapter 3.1 --- Introduction of Channel Correlation --- p.20 / Chapter 3.2 --- Channel Correlation Threshold --- p.25 / Chapter Chapter 4 --- MAC with SINR Threshold --- p.28 / Chapter Chapter 5 --- Performance Evaluation of MWST in Fully-Connected Networks --- p.33 / Chapter Chapter 6 --- MAC with SINR Threshold (MWST) in Partially-Connected Networks --- p.38 / Chapter 6.1 --- Hidden Link Problem in Partially-Connected Networks --- p.38 / Chapter Chapter 7 --- Performance Evaluation in Partially-Connected Networks --- p.42 / Chapter 7.1 --- Fairness Issues in CSMA/CA(k) --- p.42 / Chapter 7.2 --- Fairness Performance of MWST --- p.45 / Conclusion --- p.50 / References --- p.52

Shen, Yuxiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 61-64). / Abstracts in Chinese and English. / 摘要........Error! Bookmark not defined / Abstract --- p.iii / Acknowledgement --- p.v / Content --- p.viii / List of Figures --- p.xi / List of Tables --- p.xii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background Overview --- p.1 / Chapter 1.2 --- Motivation and Related Work --- p.2 / Chapter 1.3 --- Our Contribution --- p.3 / Chapter 1.4 --- Organization of the Thesis --- p.5 / Chapter Chapter 2 --- Preliminaries --- p.6 / Chapter 2.1 --- TDMA Technology --- p.6 / Chapter 2.1.1 --- Features of TDMA --- p.8 / Chapter 2.2 --- Previous Study on TDMA Link Scheduling --- p.8 / Chapter 2.3 --- Typical Network and Interference Models --- p.10 / Chapter Chapter 3 --- System Model --- p.14 / Chapter 3.1 --- Physical Layer Interference Model --- p.14 / Chapter 3.2 --- Objective of the Problem --- p.15 / Chapter 3.3 --- Rate Matrices for Transmission Sets --- p.17 / Chapter 3.4 --- Airtime Allocation --- p.19 / Chapter Chapter 4 --- Problem Formulation and Its Solution --- p.20 / Chapter 4.1 --- LP Formulation of Optimal TDMA Link Scheduling --- p.21 / Chapter 4.2 --- Solution to the Optimal Air Time Allocation Problem --- p.22 / Chapter 4.3 --- n-length Chain Network --- p.24 / Chapter 4.3.1 --- Adaptive Rate Transmission --- p.25 / Chapter 4.3.2 --- Fixed Rate Transmission --- p.27 / Chapter Chapter 5 --- Bad Transmission Set Removal Algorithm (BTSR) --- p.30 / Chapter 5.1 --- A 7-node Chain Example --- p.30 / Chapter 5.2 --- BTSR Algorithm --- p.32 / Chapter Chapter 6 --- Randomized Decentralized Scheduling Algorithm (RDSA) --- p.35 / Chapter 6.1 --- RDSA Algorithm --- p.35 / Chapter 6.2 --- Pseudo Code of RDSA --- p.37 / Chapter 6.3 --- The Flow Chart of RDSA --- p.39 / Chapter Chapter 7 --- Performance Evaluation --- p.41 / Chapter 7.1 --- Performance of Cross-layer TDMA Link Scheduling --- p.41 / Chapter 7.2 --- Complexity Analysis and Comparisons for BTSR+LP and LP --- p.46 / Chapter 7.2.1 --- Complexity of LP Problem --- p.47 / Chapter 7.2.2 --- Problem Size Reduced by BTSR --- p.48 / Chapter 7.2.3 --- Revised BTSR Algorithm --- p.49 / Chapter 7.2.4 --- The Complexity Issues --- p.51 / Chapter 7.3 --- Performance and Complexity Issues for RDSA --- p.52 / Chapter Chapter 8 --- Conclusion and Future Work --- p.57 / Chapter 8.1 --- Conclusions --- p.57 / Chapter 8.2 --- Future Work --- p.58 / Bibliography

We present an algorithm to store data robustly in a large, geographically distributed network by means of localized regions of data storage that move in response to changing conditions. For example, data might migrate away from failures or toward regions of high demand. The PersistentNode algorithm provides this service robustly, but with limited safety guarantees. We use the RAMBO framework to transform PersistentNode into RamboNode, an algorithm that guarantees atomic consistency in exchange for increased cost and decreased liveness. In addition, a half-life analysis of RamboNode shows that it is robust against continuous low-rate failures. Finally, we provide experimental simulations for the algorithm on 2000 nodes, demonstrating how it services requests and examining how it responds to failures.

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.

The need for an effective clustering algorithm for Vehicle Ad Hoc Networks (VANETs) is motivated by the recent research in cluster-based MAC and routing schemes. VANETs are highly dynamic and have harsh channel conditions, thus a suitable clustering algorithm must be robust to channel error and must consider node mobility during cluster formation. This work presents a novel, mobility-based clustering scheme for Vehicle Ad hoc Networks, which forms clusters using the Affinity Propagation algorithm in a distributed manner. This proposed algorithm considers node mobility during cluster formation and produces clusters with high stability. Cluster performance was measured in terms of average cluster head duration, average cluster member duration, average rate of cluster head change, and average number of clusters. The proposed algorithm is also robust to channel error and exhibits reasonable overhead. Simulation results confirm the superior performance, when compared to other mobility-based clustering techniques.

TCP (Transmission Control Protocol) is a transport protocol designed for the wired Internet. In wireless networks packet losses occur more frequently due to the unreliability of the physical link. The main problem is that TCP treats all losses as congestion, which leads to a lower throughput. Ad hoc networks are multihop wireless networks of mobile nodes, where each node can allow other packets to pass through it. Topology changes often occur and may lead to packet losses and delays, which TCP misinterprets as congestion. We want to modify TCP to recognize the differences between link failure and congestion to improve the capacity. In our model we have built a connection in an ad hoc network where packet losses and partitions can be made. Simulation experiments show that we didn't get the problems we expected. This can be explained by low delays and because we buffered the packets during link failure. A simple modification of TCP was made and simulated, and showed that an improvement of performance is possible. More research should be done to make a modification of TCP that would further affect the throughput.

The need for an effective clustering algorithm for Vehicle Ad Hoc Networks (VANETs) is motivated by the recent research in cluster-based MAC and routing schemes. VANETs are highly dynamic and have harsh channel conditions, thus a suitable clustering algorithm must be robust to channel error and must consider node mobility during cluster formation. This work presents a novel, mobility-based clustering scheme for Vehicle Ad hoc Networks, which forms clusters using the Affinity Propagation algorithm in a distributed manner. This proposed algorithm considers node mobility during cluster formation and produces clusters with high stability. Cluster performance was measured in terms of average cluster head duration, average cluster member duration, average rate of cluster head change, and average number of clusters. The proposed algorithm is also robust to channel error and exhibits reasonable overhead. Simulation results confirm the superior performance, when compared to other mobility-based clustering techniques.

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.

An ad-hoc network is a packet radio network in which individual mobile nodes perform routing functions. Typically, an ad-hoc networking concept allows users wanting to communicate with each other while forming a temporary network, without any form of centralized administration. Each node participating in the network performs both the host and router function, and willing to forward packets for other nodes. For this purpose a routing protocol is needed. A novel approach utilizes the uniqueness of such a network i. e. distance, location and speed of the nodes, introducing a Distance Routing Effect Algorithm for Mobility (DREAM). The protocol uses the <i>distance effect</i> and the <i>mobility rate</i> as a means to assure routing accuracy. When data needs to be exchanged between two nodes, the directional algorithm sends messages in the recorded direction of the destination node, guaranteeing the delivery by following the direction. The improved algorithm suggested within this thesis project includes an additional parameter, direction of travel, as a means of determining the location of a destination node. When data needs to be exchanged between two nodes, the directional algorithm sends messages in the recorded direction of the destination node, guaranteeing the delivery by following the direction. The end result is an enhancement to the delivery ratio, of the sent to the received packet. This also allows the reduction in the number of control packets that need to be distributed, reducing the overall control overhead of the Improved Dream protocol.