Zhengrong Ji

Exploiting medium access diversity in rate adaptive wireless LANs

Recent years have seen the growing popularity of multi-rate wireless network devices (e.g., 802.11a cards) that can exploit variations in channel conditions and improve overall network throughput. Concurrently, rate adaptation schemes have been developed that selectively increase data transmissions on a link when it offers good channel quality. In this paper, we propose a Medium Access Diversity (MAD) scheme that leverages the benefits of rate adaptation schemes by aggressively exploiting multiuser diversity. The basic mechanism of MAD is to obtain instantaneous channel condition information from multiple receivers and selectively transmit data to a receiver that improves the overall throughput of the network, while maintaining temporal fairness among multiple data flows. We identify and address the challenges in the design and implementation of MADs three phases: channel probing, data transmission, and receiver scheduling. We also use analytical models to examine the tradeoff between network performance improvement and overhead of channel probing, and derive an asymptotic performance bound for the receiver scheduling algorithms used by MAD. Results from the analysis and the extensive simulations demonstrate that, on average, MAD can improve the overall throughput of IEEE 802.11 wireless LANs by 50% as compared with the best existing rate adaptation scheme.

MAYA: Integrating hybrid network modeling to the physical world

The flourish of large-scale network applications across the Internet and or MANET has raised a challenge to network modeling environments that support experimentation and analysis of close interactions between real applications and network dynamics. To facilitate such experimentations, this paper presents MAYA, a multiparadigm network modeling framework including discrete event models, analytical models and physical network interfaces, together with its illustrative implementation using QualNet, fluid flow TCP model and physical network interface. MAYA framework allows users to interface simulated networks directly with physical networks, while attaining real-time constraints even for large-scale networks by incorporating above multiparadigm network modeling techniques. It also gives user the flexibility to emulate applications on nodes in both real and simulated networks. Experiments are conducted to validate the interoperation of QualNet and fluid flow model, to examine the performance of MAYA as well as to evaluate the optimization techniques, namely interleaved execution of fluid flow model and causality-preserve realtime synchronization relaxation. Experimental results indicate that MAYA is a scalable and extensible solution to modeling of close interactions between real application and network dynamics.

TWINE: A Hybrid Emulation Testbed for Wireless Networks and Applications

In this paper, we present TWINE, a high fidelity, efficient emulation framework that combines the accuracy and realism of emulated and physical networks and the scalability and repeatability of simulation in an integrated testbed, for evaluation of real protocols and applications. Our measurements show that the TWINE emulation kernel has a memory footprint of less than 100KB, and occupies no more than 3.5% CPU cycles. Thanks to such small overhead and the accurate modelling of physical layer events(at microseconds level), application throughput measured in TWINE is within 5% of the measured throughput from an equivalent physical wireless LAN. A single commodity PC in TWINE can emulate at least four wireless hosts or simulate sixty nodes in real time at microseconds granularity. This paper also illustrates TWINE’s novel capabilities via two case studies: a protocol to maintain fairness in mesh networks and an adaptive streaming media application operating in heterogeneous wireless networks. The results from the case studies clearly show the benefit of the TWINE evaluation methodology, by identifying a mismatch between the performance of the protocol or application based on actual user experience versus its performance as measured using traditional network performance metrics such as application throughput.

Low-cost attacks against packet delivery, localization and time synchronization services in under-water sensor networks

Under-Water Sensor Networking (UWSN) is a novel network paradigm that is being proposed to explore, monitor and protect the oceans. The unique characteristics of the aquatic environment, namely huge propagation delay, absence of GPS signaling, floating node mobility, and limited (acoustic) link capacity, are very different from those of ground sensor networks. Since underwater networks are mostly autonomous and very difficult to directly monitor by humans, a very important requirement is the built-in protection from automated malicious attacks. In this paper we show that the aquatic environment is particularly vulnerable to attacks and security must be integrated into the UWSN architecture to protect its localization, synchronization and packet delivery services.

iMASH: interactive mobile application session handoff

Mobile computing research has often focused on untethering an in-use computing device, rather than enabling the mobility of the computation task itself. This paper presents an architecture, implementation, and experimental evidence that together validate a new continuous computing concept, application session handoff. The iMASH architecture leverages previous work on proxies, content adaptation, and client awareness to provide a unique, middleware-enable capability for continuous computing. Implementation in both socket- and RPC-based environments shows that very fast, secure session handoff of non-trivial client/server applications across heterogeneous client devices and network is feasible: experiments on a number of applications yielded handoff latencies ranging from 0.5s to 2s.

WHYNET: a hybrid testbed for large-scale, heterogeneous and adaptive wireless networks

We present an overview of the WHYNET (Wireless HYbrid NETwork) testbed, currently being developed for realistic and scalable evaluation of next-generation wireless network protocols and applications. WHYNET framework enables seamless integration of physical, simulation and emulation components in a single framework, and allows the use of any combination of those components when evaluating a target wireless network scenario. In this article, we describe the rationale behind our hybrid testbed approach, and give an overview of the architectural components of the hybrid testbed and key technical challenges addressed in its design. Further, we present several case studies to demonstrate the value of the hybrid testbed for realistic and scalable evaluation of a broad range of wireless network scenarios, focusing on cross-layer interactions, heterogeneous and large-scale wireless networks.

Scalable simulation of large-scale wireless networks with bounded inaccuracies

Discrete event network simulators have emerged as popular tools for verification and performance evaluation for various wireless networks. Nevertheless, the desire to model such networks at high fidelity implies high computational costs, prohibiting most researchers from simulating wireless networks with thousands of nodes. There have been attempts on performance optimizations for large-scale wireless network simulation, but they have not appropriately modeled accumulation of weak interference, thereby suffering inaccuracies which may be magnified by upper layer protocols. This paper presents analysis of the effects of common optimization techniques for large-scale wireless network simulation on the overall network performance and also proposes modifications and novel techniques that introduce only limited inaccuracies or no additional inaccuracy at all. The study quantifies the effects of those optimizations on the simulation results for given thresholds and network parameters, and also identifies thresholds tolerable to most network studies. The experimental results show that these optimizations can improve the runtime performance of an already efficient wireless network simulator substantially, by a factor of up to 55 for wireless networks with 3200 nodes without compromising accuracy of the simulation results.

Optimizing parallel execution of detailed wireless network simulation

With parallel and discrete event simulation (PDES) techniques, the runtime performance of detailed wireless network simulation can be improved significantly without compromising fidelity of the simulation results. However, modelling characteristics of wireless communications such as signal propagation and interference may severely hinder the potential speedup yielded by PDES. This paper proposes various optimization techniques to address three major concerns in achieving efficient parallel execution of wireless network simulation: i.e., (1) reducing communication and computation overhead of simulating signal propagation across multiple logical processes; (2) reducing synchronization overhead among logical processes; (3) minimizing event scheduling overhead within individual logical processes. These techniques have been implemented in a parallel version of GloMoSim and QualNet. The experimental results with mobile ad hoc networking scenarios demonstrate that the proposed optimization techniques can improve the performance of parallel wireless network simulation by up to an order of magnitude.

Modeling environmental mobility and its effect on network protocol stack

In this paper we address the effects of environmental mobility, that is, the ambient motion of entities like people and vehicles in the vicinity of wireless communication, on the channel characteristics and wireless network performance. We present a three step process of measurements, modeling and network simulations to quantify the significance of environmental mobility. Our field experiments show that presence of people not only cause deep fades but also distorts the fading distribution. We model the shadowing loss by three knife-edge diffraction model and propose a two-state Markov process channel fading behavior. The models are validated against measurement data and implemented in a network simulator. The models are scalable and incur execution overhead less than 15%. We also show the impact of environment mobility on protocol performance by means of two simulation case studies. We show that MAC layer data rate adaptation behavior is sensitive to environmental mobility and can result in 40% packets being delivered at lower rates. Second study on ad-hoc network performance show the throughput is decreased by 20%. We have identified that with environmental mobility the links are more sensitive to interference and the routes are less stable

Improving scalability of wireless network simulation with bounded inaccuracies

Discrete event network simulators have emerged as popular tools for verification and performance evaluation of wireless networks. Nevertheless, the desire to model such networks at high fidelity implies high computational costs, limiting most researchers the ability to simulate networks with thousands of nodes. Previous attempts to optimize simulation of large-scale wireless networks have not appropriately modeled accumulation of weak interference, thereby suffering inaccuracies that may be further magnified in the evaluation of upper-layer protocols. This article presents a comprehensive analysis on the effects of common optimization techniques for large-scale wireless network simulation on the overall network performance. Based on the analysis, it formulates distance limit derivation and mobility update reduction that introduce bounded inaccuracy to the radio propagation simulation. It further proposes a novel technique, Lazy Event Scheduling with Corrective Retrospection, that reduces simulation events twenty-five fold without introducing any inaccuracy at all. The experimental results show that these optimizations can substantially improve the runtime performance of an already efficient wireless network simulator, by a factor of up to 55 for wireless networks with 3200 nodes without compromising the simulations accuracy.