Asymptotic Performance In Large-Scale Wireless Sensor Networks And Wireless Ad-hoc Networks

In the past decade, large-scale wireless sensor networks (WSNs) and wireless ad-hoc networks have attracted more and more attentions. In large-scale WSNs and wire-less ad-hoc network, asymptotic analysis mainly focuses on the limiting behaviors, e.g., throughput capacity, lifetime of networks. It has been shown that the asymptotic per-formances are very important to guide the designing of network systems. In literature, the asymptotic performances, e.g., the throughput capacity bound of unicast, multicast, have been widely studied.In wireless sensor networks (WSNs), a major challenge is how to prolong the net-work lifetime while maintaining a certain data collection rate for resource-limited static sensors. To achieve this goal, many mobility-assisted data collection (MADC) schemes have been proposed in the literature. However, there is a lack of systematic analysis on the behaviors of the MADC models, in terms of both the throughput capacity, defined as the maximal data collection rate, and the lifetime that shall be associated with a spe-cific data collection rate. In Chapter2, we address this issue in a large-scale WSN from a theoretical perspective, which has not been studied before. Specifically, we first propose a general mobility sink assisted data collection (MSADC) model that includes many key features of MSADC, such as the velocity of a mobile node, and the travel-ing path of a mobile node. We then develop a comprehensive theoretical approach to obtain the achievable throughput capacity and lifetime. By applying the proposed ap-proach, we investigate the behaviors of WSNs with one or more mobile sinks, which are two important MSADC scenarios. Our analysis not only shows how a WSN under an MSADC model can outperform a static WSN, but also provides insights on how to adjust the MSADC parameters to improve the data collection rate and to maximize the lifetime. Finally, Our analysis is validated through extensive simulations.To improve the throughput capacity of data collection in WSNs, another impor- tant approach is using mobile relays. In the Chapter3, we investigate the throughput capacity of WSNs where multiple mobile relays are deployed to collect data from static sensors and forward them to a static sink. To facilitate the discussion, we propose a new mobile relay assisted data collection (MRADC) model. Based on this model, we analyze the achievable throughput capacity of large-scale WSNs using a constructive approach, which can achieve a certain throughput by choosing appropriate mobility pa-rameters. Our analysis illustrates that, if the number of relays is less than a threshold, then the throughput capacity can be increased linearly with more relays. On the other hand, if the number is greater than the threshold, then the throughput capacity becomes a constant, and the capacity gain over a static WSN depends on two factors:the transmis-sion range and the impact of interference. To verify our analysis, we conduct extensive simulation experiments, which validate the selection of mobility parameters, and which demonstrate the same throughput behaviors obtained by analysis.In most existing wireless networks, end users obtain data content from the wired network, typically, the Internet. In this manner, virtually all of their traffic must go through a few access points, which implies that the capacity of wireless network is lim-ited by the aggregated transmission data rate of these access points. To fully exploit the capability of wireless network, we envision that future wireless networks shall be able to provide data content within themselves. In the Chapter4, we address the behavior of such networks from a theoretical perspective. Specifically, we consider that multicast is used for distributed content delivery, and we investigate the asymptotic upper bound of the throughput capacity for distributed content delivery in large-scale wireless ad hoc networks (DCD-WANET). Our analysis shows how the upper bound of throughput capacity is affected by the geometric size of the network, the number of data items, the popularity of the data content, and the number of storage nodes that contain those data items. In particular, our theoretical results show that, if the number of storage nodes exceed a critical threshold, the upper bound grows with the number of storage nodes, according to a power-law where the scaling exponent depends on the popularity of data items. We also provide the data item placement strategy to achieve the upper bound of throughput capacity for DCD-WANET.