Seung Jun Baek

In this paper, we investigate the use of proactive multipath routing to achieve energy-efficient operation of ad hoc wireless networks. The focus is on optimizing tradeoffs between the energy cost of spreading traffic and the improved spatial balance of energy burdens. We propose a simple scheme for multipath routing based on spatial relationships among nodes. Then, combining stochastic geometric and queueing models, we develop a continuum model for such networks, permitting an evaluation of dif

Temporal parameters during swallowing are analyzed for objective and quantitative evaluation of videofluoroscopic swallowing studies (VFSS). Manual analysis by clinicians is time-consuming, complicated and prone to human error during interpretation; therefore, automated analysis using deep learning has been attempted. We aimed to develop a model for the automatic measurement of various temporal parameters of swallowing using deep learning. Overall, 547 VFSS video clips were included. Seven tempo

In this paper, we propose a stochastic geometric model to study the energy burdens seen in a large scale hierarchical sensor network. The network makes use of aggregation nodes, for compression, filtering, and/or data fusion of locally sensed data. Aggregation nodes (AGNs) then relay the traffic to mobile sinks. While aggregation may substantially reduce the overall traffic on the network, it may have the deleterious effect of concentrating loads on paths between AGNs and the sinks—such inhomoge

We consider a queuing model with applications to electric vehicle (EV) charging systems in smart grids. We adopt a scheme where Electric Service Company (ESCo) broadcasts one bit signal to consumers indicating on-peak periods for the grid. EVs randomly suspend/resume charging based on the signal. To model the dynamics of the population of EVs we analyze an M/M/∞ queue with random interruptions, and propose estimates using time-scale decomposition. Using the estimates we show how ESCo can optimal

We propose a scheme to reduce the overhead associated with channel state information (CSI) feedback required for opportunistic scheduling in wireless access networks. We study the case where CSI is partially overheard by mobiles and thus one can suppress transmitting CSI reports for time varying channels of inferior quality. We model the mechanism of feedback suppression as a Bayesian network, and show that the problem of minimizing the average feedback overhead is NP-hard. To deal with hardness

We consider a joint routing and scheduling scheme for data collection in wireless sensor networks leveraging compressive sensing under the protocol interference model. We propose the construction of a connected dominating set as a network backbone for efficient routing. A hybrid compressive sensing technique, which combines conventional and compressive data gathering schemes, is used to aggregate data over the backbone. Pipelined scheduling is developed for fast aggregation of compressed data ov

We consider a queuing model with applications to electric vehicle (EV) charging systems in smart grids. We adopt a scheme where an Electric Service Company (ESCo) broadcasts a one bit signal to EVs, possibly indicating ‘on-peak’ periods during which electricity cost is high. EVs randomly suspend/resume charging based on the signal. To model the dynamics of EVs we propose an M/M/∞ queue with random interruptions, and analyze the dynamics using time-scale decomposition. There exists a trade-off: o

Safety is a major concern for autonomous vehicle driving. The autonomous vehicles relying solely on ego-vehicle sensors have limitations in dealing with collisions. The risk can be reduced by communicating with other vehicles sharing sensed information. In this paper, we study a real-time multisource data fusion scheme based on Dempster–Shafer theory of evidence (DS) through cooperative vehicle-to-vehicle (V2V) communications. The classical DS can produce erroneous outputs when confidences are h

Learning positional information of nodes in a graph is important for link prediction tasks. We propose a representation of positional information using representative nodes called landmarks. A small number of nodes with high degree centrality are selected as landmarks, which serve as reference points for the nodes' positions. We justify this selection strategy for well-known random graph models and derive closed-form bounds on the average path lengths involving landmarks. In a model for power-la

We investigate how to improve link quality without degrading data rate by exploiting tradeoffs between diversity and spatial multiplexing gains in multi-input multi-output ad-hoc networks. When the set of input rates for a MIMO network is given, we propose that maximizing the minimum diversity gain among the links provides a reasonable solution to optimize the overall link error probability when there is a reasonably high signal to noise ratio. We verify the performance using simulation based on