Projects

Opportunism vs. Cooperation in Wireless Mesh Networks

A wide range of forwarding strategies have been developed for multi-hop wireless networks, considering the broadcast nature of the wireless medium and the presence of random fading that causes unreliable transmissions. Two recently proposed strategies are: opportunistic forwarding that exploits relay diversity by opportunistically selecting an overhearing relay, and cooperative forwarding that relies on the synchronized transmissions of relays to reinforce the signal strength. Although these strategies are well-known in the literature, there lacks a thorough analysis of their network level performance in a realistic SINR setting. We begin by characterizing the gap between opportunistic forwarding and cooperative forwarding for single flow in general linear networks via Markov chains and via recurrence relations. We also present fixed-point model to provide an efficiently computable lower bound to the throughput of the Markov chain model to facilitate the comparison of performance in large networks. Extensive simulations are also performed. Our work helps us understand that interference resulting from the larger number of transmissions under cooperative forwarding mitigates the potential gains achievable with cooperative forwarding.

QoE management of Multiple Video Streams in Wireless Networks

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. Prior work has studied this problem in the context of transmitting a single video stream. In this paper, we investigate multiplexing schemes to transmit multiple video streams from a base station to mobile clients. We present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch our scheme reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum `playout lead' across all videos. We show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy scheduling algorithm. Our greedy algorithm is optimal when the channel quality of a user remains unchanged within an epoch. Our experimental results, based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed, show that the lead-aware greedy approach results in a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or fewer average number of stalls per client.

Markov Model for Received Power in Wireless Networks

A wide range of wireless channel models have been developed to model variations in received signal strength. In contrast to prior work, which has focused primarily on channel modeling on a short, per- packet timescale (millisecond), we develop and validate a finite-state Markov chain model that captures variations due to shadowing, which occur at coarser time scales. The Markov chain is constructed by partitioning the entire range of shadowing into a finite number of intervals. We determine the Markov chain transition matrix in two ways: (i) via an abstract modeling approach in which shadowing effects are modeled as a log-normally distributed random variable affecting the received power, and the transition probabilities are derived as functions of the variance and autocorrelation function of shadowing; (ii) via an empirical approach, in which the transition matrix is calculated by directly measuring the changes in signal strengths collected in a 802.16e (WiMAX) network. We validate the abstract model by comparing its steady state and transient performance predictions with those computed using the empirically derived transition matrix and those observed in the actual traces themselves.

Optimizing Control Overhead for Power-aware Routing in Wireless Networks

We analyze the tradeoff between the amount of signaling overhead incurred in path selection in a MANET with time-varying wireless channels and the application-level throughput and end-to-end power expended on the selected path. Here, increased overhead increases the accuracy of the link-state estimates used in path selection but decreases the amount of bandwidth available for application use. We develop an information-theoretic, bounding approach to quantify the signaling overhead. Specifically, we investigate (i) the time granularity at which link state is sampled and communicated, and (ii) the minimum number of bits needed to encode this link state information, such that the expected power consumption within a sampling interval is minimized subject to a fixed source-destination goodput constraint. We formulate an optimization problem that provides a numerically computable solution to these questions, and quantitatively demonstrate that short sampling intervals incur significant overhead while long intervals fail to take advantage of the temporal correlation in link state. Additionally, we find that using a small number of bits per sample do not provide sufficient information about the network while using too many bits provide little additional information at the expense of increased overhead. Our work can be used by network operators as a tool to determine parameters like the optimal state update frequency and the number of bits per sample.