Seung Jun Baek

Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation

In this paper, we study how to reduce energy consumption in large-scale sensor networks, which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs-typically related to proximity-and, perhaps surprisingly, it is independent of the distribution of data sources. Next, we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes, and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8%-28% energy savings depending on the compression ratio.

Spatial energy balancing through proactive multipath routing in wireless multihop networks

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 different types of scenarios, i.e., with and without energy replenishing and storage capabilities. We propose a parameterized family of energy balancing strategies and study the spatial distributions of energy burdens based on their associated second-order statistics. Our analysis and simulations show the fundamental importance of the tradeoff explored in this paper, and how its optimization depends on the relative values of the energy reserves/storage, replenishing rates, and network load characteristics. For example, one of our results shows that the degree of spreading should roughly scale as the square root of the bits middot meters load offered by a session. Simulation results confirm that proactive multipath routing decreases the probability of energy depletion by orders of magnitude versus that of a shortest path routing scheme when the initial energy reserve is high

Spatial Model for Energy Burden Balancing and Data Fusion in Sensor Networks Detecting Bursty Events

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 inhomogeneities in the energy burden may in turn lead to nodes with depleted energy reserves. To remedy this problem, we consider how one might achieve a more balanced energy burden across the network by spreading traffic, i.e., using a multiplicity of paths between AGNs and sinks. The proposed model reveals, how various aspects of the task at hand impact the characteristics of energy burdens on the network and in turn the lifetime for the system. We show that the scale of aggregation and degree of spreading can be optimized. Additionally, if the sensing activity involves large amounts of data flowing to sinks, then inhomogeneities in the energy burdens seen by nodes around the sinks will be hard to overcome, and indeed the network appears to scale poorly. By contrast, if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.

Sufficient Conditions on Stable Recovery of Sparse Signals With Partial Support Information

In this letter, we study signal reconstruction from compressed sensing measurements. We propose new sufficient conditions for stable recovery when partial support information is available. Weighted l1-minimization is adopted to recover the original signal under three noise models. The proposed approach is to use Ozekis inequality and shifting inequality in order to bound the errors in the associated weighted l1 -minimization. Our result offers generalized performance bounds on recovery capturing known support information. Improved sufficient conditions for recovery are derived based on our results, even for the cases where the accuracy of prior support information is arbitrarily low.

A queuing model with random interruptions for electric vehicle charging systems

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 optimally adjust the indicator signal so as to minimize the mean number of charging EVs during the actual on-peak periods. Next we consider the case where EVs respond to the signal based on the individual loads. Simulation results show that performance is improved if the EVs carrying higher loads are less sensitive to the on-peak indicator signal.

A Scalable Model for Energy Load Balancing in Large-scale Sensor Networks

In this paper we propose a stochastic geometric model to study the energy burdens seen in a large scale hirarchical sensor network. The network makes a natural use of aggregation nodes, for compression, filtering or data fusion of local sensed data. Aggregation nodes (AGN) then relay the traffic to mobile sinks. While aggregation may substantially reduce the overall traffic on the network it may have a deleterious effect of concentrating loads on paths between AGNs and the sinks— such inhomogeneities in energy burdens may in turn lead to nodes with depleted energy reserves. To remedy this problem we consider how one might achieve more balanced energy burdens across the network by spreading traffic, i.e., using a multiplicity of paths between AGNs and sinks. The proposed model reveals, how various aspects of the task at hand impact the characteristics of energy burdens on the network and in turn the likely lifetime for the system. We show that the scale of aggregation and degree of spreading might need and can be optimized. Additionally if the sensing activity involves large amounts of data flowing to sinks, then inhomogeneities in the energy burdens seen by nodes around the sinks will be hard to overcome, and indeed the network appears to scale poorly. By contrast if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.

A max-min strategy for QoS improvement in MIMO ad-hoc networks

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 a modified ns2.

Minimum Cost Data Aggregation for Wireless Sensor Networks Computing Functions of Sensed Data

We consider a problem of minimum cost (energy) data aggregation in wireless sensor networks computing certain functions of sensed data. We use in-network aggregation such that data can be combined at the intermediate nodes en route to the sink. We consider two types of functions: firstly the summation-type which includes sum, mean, and weighted sum, and secondly the extreme-type which includes max and min. However for both types of functions the problem turns out to be NP-hard. We first show that, for sum and mean, there exist algorithms which can approximate the optimal cost by a factor logarithmic in the number of sources. For weighted sum we obtain a similar result for Gaussian sources. Next we reveal that the problem for extreme-type functions is intrinsically different from that for summation-type functions. We then propose a novel algorithm based on the crucial tradeoff in reducing costs between local aggregation of flows and finding a low cost path to the sink: the algorithm is shown to empirically find the best tradeoff point. We argue that the algorithm is applicable to many other similar types of problems. Simulation results show that significant cost savings can be achieved by the proposed algorithm.

Application of precise indoor position tracking to immersive virtual reality with translational movement support

In this study, we propose an application for immersive virtual reality experiences, which integrates three-dimensional (3D) head-mounted displays (HMDs) with a precise indoor position tracking algorithm based on ultrasound. Our method provides a natural virtual reality experience with interaction by precisely matching the physical movements in the real world with those in the virtual environment, unlike other methods that require external input devices to move around in the virtual environment. Users can move within the assigned indoor space while carrying a wireless client device with the HMD, without the risk of colliding with obstacles or structures. The system is designed to provide the accurate 3D X, Y, and Z coordinate values of translational movements in real-time as well as the pitch, roll, and yaw values of rotational movements supported by the HMD, resulting in the six degrees of freedom required by immersive virtual reality. In addition, the system utilizes ultrasonic transducers in a grid format, which makes it simple to expand the position tracking coverage area, and supports simultaneous tracking of multiple users. Through experiments and a user study we show that the system obtains the accurate position of the moving objects and delivers a highly immersive virtual reality experience.

Energy-Efficient Collection of Sparse Data in Wireless Sensor Networks Using Sparse Random Matrices

We consider the energy efficiency of collecting sparse data in wireless sensor networks using compressive sensing (CS). We use a sparse random matrix as the sensing matrix, which we call Sparse Random Sampling (SRS). In SRS, only a randomly selected subset of nodes, called the source nodes, are required to report data to the sink. Given the source nodes, we intend to construct a data gathering tree such that (1) it is rooted at the sink and spans every source node and (2) the minimum residual energy of the tree nodes after the data collection is maximized. We first show that this problem is NP-complete and then develop a polynomial time algorithm to approximately solve the problem. We greedily construct a sequence of data gathering trees over multiple rounds and propose a polynomial-time algorithm to collect linearly combined measurements at each round. We show that the proposed algorithm is provably near-optimal. Simulation and experimental results show that the proposed algorithm excels not only in increasing the minimum residual energy, but also in extending the network lifetime.