Investigation of reactive TCP and link characteristics estimation for wireless links

Master'sMASTER OF SCIENC

Improving TCP Performance in the Mobile, High Speed, Heterogeneous and Evolving Internet

Ph.DDOCTOR OF PHILOSOPH

Global stability of vaccine-age/staged-structured epidemic models with nonlinear incidence

We consider two classes of infinitely dimensional epidemic models with nonlinear incidence, where one assumes that the rate of a vaccinated individual losing immunity depends on the vaccine-age and another assumes that, before the vaccine begins to wane, there is a period during which the vaccinated individuals have complete immunity against the infection. The first model is given by a coupled ordinary-hyperbolic differential system and the second class is described by a delay differential system. We calculate their respective basic reproduction numbers, and show they characterize the global dynamics by constructing the appropriate Lyapunov functionals

A shared opportunistic infrastructure for long-lived wireless sensor networks

In this paper, a Shared Opportunistic Infrastructure (SOI) is proposed to reduce total cost of ownership for long-lived wireless sensor networks through exploiting human mobility. More specifically, various sensor nodes are opportunistically connected with their corresponding servers through smart phones carried by people in their daily life. In this paper, we will introduce the motivations, present the architecture, discuss the feasibility, and identify several research opportunities of SOI

Exploiting rush hours for energy-efficient contact probing in opportunistic data collection

In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the use of mobile devices carried by people in their daily life to collect sensor data opportunistically. As the movement of these mobile nodes is, by definition, uncontrolled, contact probing becomes a challenging task, particularly for sensor nodes which need to be aggressively duty-cycled to achieve long life. It has been reported that when the duty-cycle of a sensor node is fixed, SNIP, a sensor node-initiated probing mechanism, performs much better than mobile node-initiated probing mechanisms. Considering that the intended applications are delay-tolerant, mobile nodes tend to follow some repeated mobility patterns, and contacts are distributed unevenly in temporal, SNIP-RH is proposed in this paper to further improve the performance of contact probing through exploiting Rush Hours during which contacts arrive more frequently. In SNIP-RH, SNIP is activated only when the time is within Rush Hours and there are enough data to be uploaded in the next probed contact. As for the duty-cycle, it is selected based on the mean of contact length that is learned on line. Both analysis and simulation results indicate that under a typical simulated road-side wireless sensor network scenario, SNIP-RH can significantly reduce the energy consumed for probing the contacts, that are necessary for uploading the sensed data, or significantly increase the probed contact capacity under a sensor node's energy budget for contact probing

SNIP: A Sensor Node-Initiated Probing mechanism for opportunistic data collection in sparse wireless sensor networks

In many potential wireless sensor network applications, the cost of the base station infrastructure can be prohibitive. Instead, we consider the opportunistic use of mobile devices carried by people in daily life to collect sensor data. As the movement of these mobile nodes is by definition uncontrolled, contact probing is a challenging task, particularly for sensor nodes which need to be duty-cycled to achieve long life. We propose a Sensor Node-Initiated Probing mechanism for improving the contact capacity when the duty cycle of a sensor node is fixed. In contrast to existing mobile node-initiated probing mechanisms, in which the mobile node broadcasts a beacon periodically, in SNIP the sensor node broadcasts a beacon each time its radio is turned on according to its duty cycle. We study SNIP through both analysis and network simulation. The evaluation results indicate that SNIP performs much better than mobile-initiated probing. When the fixed duty cycle is lower than 1%, the probed contact capacity can be increased by an order of 2-10; alternatively, SNIP can achieve the same amount of probed contact capacity with much less energy consumption

Contact probing mechanisms for opportunistic sensor data collection in sparse wireless sensor networks

In many emerging wireless sensor network scenarios, the use of a fixed infrastructure of base stations for data collection is either infeasible, or prohibitive in terms of deployment and maintenance costs. Instead, we consider the use of mobile devices (i.e. smartphones) carried by people in their daily life to collect data from sensor nodes opportunistically. As the movement of these mobile nodes is, by definition, not controlled for the purpose of data collection, synchronization through contact probing becomes a challenging task, particularly for sensor nodes, which need to be aggressively duty-cycled to conserve energy and achieve long lifetimes. This paper formulates this important problem, providing an analytical solution framework and systematically investigating the effective use of contact probing for opportunistic data collection. We present two new solutions, Sensor Node-Initiated Probing (SNIP) and SNIP-Rush Hours, the latter taking advantage of the temporal locality of human mobility. These schemes are evaluated using numerical analysis and COOJA network simulations, and the results are validated on a small sensor testbed and with the real-world human mobility traces from Nokia MDC Dataset. Our experimental results quantify the relative performance of alternative solutions on sensor node energy consumption and the efficacy of contact probing for data collection, allowing us to offer insights on this important emerging problem

Analysis of smartphone user mobility traces for opportunistic data collection in wireless sensor networks

The increasing ubiquity of smartphones coupled with the mobility of their users will allow the use of smartphones to enhance the operation of wireless sensor networks. In addition to accessing data from a wireless sensor network for personal use, and the generation of data through participatory sensing, we propose the use of smartphones to collect data from sensor nodes opportunistically. For this to be feasible, the mobility patterns of smartphone users must support opportunistic use. We analyze the dataset from the Mobile Data Challenge by Nokia, and we identify the significant patterns, including strong spatial and temporal localities. These patterns should be exploited when designing protocols and algorithms, and their existence supports the proposal for opportunistic data collection through smartphones

Data pre-forwarding for opportunistic data collection in wireless sensor networks

Opportunistic data collection in wireless sensor networks uses passing smartphones to collect data from sensor nodes, thus avoiding the cost of multiple static sink nodes. Based on the observed mobility patterns of smartphone users, sensor data should be preforwarded to the nodes that are visited more frequently with the aim of improving network throughput. In this article, we construct a formal network model and an associated theoretical optimization problem to maximize the throughput subject to energy constraints of sensor nodes. Since a centralized controller is not available in opportunistic data collection, data pre-forwarding (DPF) must operate as a distributed mechanism in which each node decides when and where to forward data based on local information. Hence, we develop a simple distributed DPF mechanism with two heuristic algorithms, implement this proposal in Contiki-OS, and evaluate it thoroughly. We demonstrate empirically, in simulations, that our approach is close to the optimal solution obtained by a centralized algorithm. We also demonstrate that this approach performs well in scenarios based on real mobility traces of smartphone users. Finally, we evaluate our proposal on a small laboratory testbed, demonstrating that the distributed DPF mechanism with heuristic algorithms performs as predicted by simulations, and thus that it is a viable technique for opportunistic data collection through smartphones