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WorkshopPostersEnergy Efficient Transmission Scheme for Data Gathering in Mobile Sensor NetworksChao Wang and Parameswaran Ramanathan Mobile sensor networks are being envisionedfor certain applications like habitat monitoring and environmentalsensing. For instance, mobile sensor nodes areattached to selected animals to gather data about theirbehavior. These data are uploaded to stationary units fordetailed analysis over wireless ad-hoc networks. Since themobile sensor nodes are likely to operate on batteries,reducing energy consumption for such data gathering isan important issue. The authors use a threshold-based transmissionscheme for power-adjustable radio to optimize transmitenergy subject to given buffer overflow and delay constraints.An analytical model is developedto estimate the transmit energy, buffer overflow and delay fora sensor node in the single-hop case. Simulation resultsshow that the model achieves very good accuracy. The transmission scheme is then adapted to the multi-hopscenario. Simulations based on radio parameters froma sensor board demonstrate that high energy saving canbe achieved by the transmission scheme in both single-hopand multi-hop cases. Sparse Multipath Wireless Channels: Modeling and ImplicationsGautham Hariharan and Akbar Sayeed In contrast to the prevalent assumption of rich multipath ininformation theoretic analysis of wireless channels, physicalchannels exhibit sparse multipath, especially at large bandwidths.We propose a model for sparse multipath fading channels andpresent results on the impact of sparsity on non-coherent capacityand reliability in the wideband regime. A key implication ofsparsity is that the statistically independent degrees of freedomin the channel, that represent the delay-Doppler diversityafforded by multipath, scale at a sub-linear rate with thesignal space dimensions (time-bandwidth product). Our analysis isbased on a training-based communication scheme that usesshort-time Fourier (STF) signaling waveforms. Sparsity indelay-Doppler manifests itself as time-frequency coherence in theSTF domain. From a capacity perspective, sparse channels areasymptotically coherent: the gap between coherent and non-coherentextremes vanishes in the limit of large signal space dimensionwithout the need for peaky signaling. From a reliabilityviewpoint, there is a fundamental tradeoff between channeldiversity and learnability that can be optimized to maximize theerror exponent at any rate by appropriately choosing the signalingduration as a function of bandwidth. Robust Location Authentication using Wireless CongruityArunesh Mishra, Shravan Rayanchu, Ashutosh Shukla, Suman Banerjee Traditional methods for localization in wireless networks rely on thecorrelation of the received signal strength with physical distance. It isalso well known, that these mechanisms fail in an adversarial setting dueto the lack of robustness of the signal strength property to maliciousintent. We present a property of the wireless medium, which we callwireless congruity, that captures the relative similarities in wirelessmedia characteristics (such as packet receptions, idle channel time, etc.)as observed by two receivers that are in physical proximity of each other.We show that wireless congruity holds promise for robust localization bypresenting an initial yet encouraging set of results obtained throughextensive experimentation in a rich indoor wireless environment. Active Wireless Sensing - An Information Retrieval Framework for Wireless Sensor NetworksThiagarajan Sivanadyan and Akbar Sayeed Active Wireless Sensing (AWS) is motivated by emerging advances in wireless technology and offers an alternative and complementary approach to in-network processing techniques for rapid and energy-efficient information retrieval in wireless sensor networks. The basic architecture in AWS consists of: i) a wireless information retriever (WIR), equipped with an antenna array, that interrogates a select ensemble of wireless sensors with space-time waveforms, ii) the sensors playing the role of active scatterers - modulating the acquired waveforms with their (possibly encoded) measured data - to generate a multipath response to the WIR's interrogation signal, and iii) the WIR retrieving the sensor data by exploiting the space-time characteristics of the resulting multipath sensing channel. The process of information retrieval has been analytically characterized using insights from wideband multi-antenna wireless channels in multipath propagation environments. A distinctive feature of AWS is the flexibility in tailoring the space-time interrogation waveforms, sensor encoding strategies, and associated processing of the received multipath signal at the WIR for energy-efficient information retrieval. One such key mechanism for energy efficiency is distributed source-channel matching: generating a coherent response from sub-ensembles of sensors with highly correlated data, based on the spatial smoothness or correlation in the signal field or on the spatial scale of local cooperation in the network. Orientation Optimization of Video Surveillance Nodes in Presence of ObstaclesYen-Ting Lin, Tai-Hsuan Wu, and Seapahn Megerian Video surveillance networks (VSNs) have a large number of important applications ranging from homeland security to monitoring and preservation of environments. We address the camera node orientation problem in VSN deployment where the goal is to maximize the detection of targets in the field, in presence of obstacles. We start by experimentally characterizing and then deriving a detection model for video camera sensors. Next, the main enabling step is the determination of the regions where obstacles may be present which is accomplished by collaborative observation of targets in the field. Once the obstacle regions have been identified, orientation of surveillance nodes are optimized to maximize detection area. Extensive experimental and simulation studies illustrate the performance of the algorithm. Sensing Driven Clustering and Sensor Prediction for Monitoring and Control ApplicationsYen-Ting Lin, and Seapahn Megerian Clustering the nodes in a wireless sensor network is a fundamental step in many distributed self organization, resource management, communication, and processing algorithms. Although in many system management tasks a purely network topology-based clustering may be sufficient, for many distributed sensing, control, and actuation applications, a more sensor-centric approach to clustering is required. In this project we investigate the sensing-driven node clustering problem by first formulating it as an instance of weighted bi-partite matching between sensors and phenomena of interest. In order to make the clustering algorithm implementation practical in larger networks, we present two distributed approaches: (i) a simple, low-cost, deterministic approach and (ii) a probabilistic balanced clustering approach. Depending on the number of sensors in the system and cluster capacities, our simulation studies indicate that switching from one approach to the other, under specific conditions, can achieve near optimal results while keeping the communication costs low. Inter-sensor data modeling and prediction have recently proven to be very promising techniques in drastically reducing the number of active sensors and thus the overall energy consumption in wireless sensor networks. Using existing and recently proposed inter-sensor data modeling techniques as the enablers, we propose an on-line distributed active prediction algorithm to use the available prediction models to put redundant sensor nodes to sleep. The proposed distributed algorithm selects a subset of the sensors that form a connected network. After completion, each sensor is either in the active set or its measurements can be directly predicted by a designated predictor sensor in the active set, within specified tolerance levels. We also show that performing the prediction modeling locally, as opposed to at a fusion center, is better suited for dynamic sensor networks. Multirate Media Streaming Using Network CodingNiveditha Sundaram, Parmesh Ramanathan, and Suman Banerjee Multimedia data transfers typically involve large volumes of data. Multirate multicast transmissions using layered source coding are generally used to deliver data streams to heterogeneous receivers. Network coding has been envisioned to increase throughput and deliver higher data rates than conventional source coding or no coding. The paper proposes a polynomial time algorithm formulticast to heterogeneous receivers using network coding. The overall goal of the algorithm is to maximize the aggregate rate to all the receivers. The problem is formulated as a linear programming optimization and solution from this optimization is used to assign linear network codes to all nodes using the Linear Information Flow (LIF) algorithm described by Sanders et. al. Empirical evaluation of theproposed solution shows that all receivers can be given a rate equal to their max-flows in all of the simulated instances. Location Estimation Scheme Using Connectivity Constraints for Wireless Adhoc Sensor NetworksNiveditha Sundaram and Parmesh Ramanathan The poster proposes a method to estimate the location of nodes in a sensor network. The network has a few nodeswhose locations are accurately known. The other nodes estimate their location based on neighborhood relationships gathered by them through message exchanges over a wireless ad hoc network. The solution shows that by incorporating non-neighbor constraints,one can substantially improve the accuracy of location estimation as compared to only utilizing neighbor relationships. The expected location estimation error is analytically derived for a certain regular deployment. For other deployments, empiricalevaluation of the proposed scheme shows that the node locations can be estimated accurately without the need for any specialized hardware. Joint Source-Channel Communication for Distributed Estimation in Sensor Networks''Waheed U. Bajwa, Jarvis D. Haupt, Akbar M. Sayeed and Robert D. Nowak Power and bandwidth are scarce resources in dense wireless sensor networks and it is widely recognized that joint optimization of the operations of sensing, processing and communication can result in significant savings in the use of network resources. Recently, we have proposed a distributed joint source-channel communication architecture for energy-efficient estimation of sensor field data at a distant destination and analyzed the corresponding relationships between power, distortion, and latency as a function of number of sensor nodes. The approach is applicable to a broad class of sensed signal fields and is based on distributed computation of appropriately chosen projections of sensor data at the destination -- phase-coherent transmissions from the sensor nodes enable exploitation of the distributed beamforming gain for energy efficiency. Random projections are used when little or no prior knowledge is available about the signal field. Distinct features of the proposed scheme include: 1) processing and communication are combined into one distributed projection operation; 2) it virtually eliminates the need for in-network processing and communication; 3) given sufficient prior knowledge about the sensed data, consistent estimation is possible with increasing sensor density even with vanishing total network power; and 4) consistent signal estimation is possible with power and latency requirements growing at most sub-linearly with the number of sensor nodes when little or no prior knowledge about the sensed data is assumed. On the (In)Feasibility of Fine Grained Power ControlVivek Shrivastava, Dheeraj Agrawal, Arunesh Mishra, Suman Banerjee A wide range of transmit power conrol (TPC) algorithms have been proposed in recent literature to reduce interference and increase capacity in 802.11 wireless networks. However, few of them have made it to practice. In many cases this gap is attributed to lack of suitable hardware support in wireless cards to implement these algorithms. In particular, many research efforts have indicated that wireless card vendors need to support power control mechanisms in a fine-grained manner, both in the number of possible power levels and the time granularity at which the controls can be applied. In this work, we claim that even if fine-grained power control mechanisms were to be made available by wireless card vendors, algorithms would not be able to properly leverage such degrees of control in typical indoor environments. We prove this claim through rigorous empirical analysis and then build a tunable empirical model that can determine the granularity of power control that is actually useful. We believe that the results from this study can serve as the right set of assumptions to build practically realizable TPC algorithms in the future. Optimal Worst-Case Coverage of Directional Field-of-View Sensor NetworksJake Adriaens, Seapahn Megerian, and Miodrag Potkonjak (UCLA) Sensor coverage is a fundamental sensor networking design and use issue that in general tries to answer the questions about the quality of sensing (surveillance) that a particular sensor network provides. Although isotropic sensor models and coverage formulations have been studied and analyzed in great depth recently, the obtained results do not easily extend to, and address the coverage of directional and field-of-view sensors such as imagers and video cameras. Here, we present an optimal polynomial time algorithm for computing the worst-case breach coverage in sensor networks that are comprised of directional ``field-of-view (FOV) sensors. Given a region covered by video cameras, a direct application of the presented algorithm is to compute ``breach, which is defined as the maximal distance that any hostile target can maintain from the sensors while traversing through the region. Breach translates to ``worst-case coverage'' by assuming that in general, targets are more likely to be detected and observed when they are closer to the sensors (while in the field of view). The approach is amenable to the inclusion of any sensor detection model that is either independent of, or inversely proportional to distance from the targets. Although for the sake of discussion we mainly focus on square fields and model the sensor FOV as an isosceles triangle, we also discuss how the algorithm can trivially be extended to deal with arbitrary polygonal field boundaries and sensor FOVs, even in the presence of rigid obstacles. We also present several simulation-based studies of the scaling issues in such coverage problems and analyze the statistical properties of breach and its sensitivity to node density, locations, and orientations. A simple grid-based approximation approach is also analyzed for comparison and validation of the implementation Exploiting Mobility for Information Exchange and Collaborative Decision-making in Sensor NetworksAarti Sing, Tai-Lin Chin, Parmesh Ramanathan, Robert D. Nowak Mobile sensors offer a new set of capabilities and challenges over static sensor networks. Applications like environmental monitoring, ecosystem studies, and battlefield surveillance involve sensing over a vast region of space. Mobile sensors can trade off the large number of static sensors with latency, and offer flexible sampling strategies that can be tuned to a particular task at hand. This project focuses on two types of mobility - adaptive and uncoordinated. Based on recent advances in active learning research, we propose adaptive path planning solutions for estimation of a spatially varying field and characterize the fundamental accuracy-latency-pathlength tradeoffs possible with adaptive mobility. Uncoordinated mobility, on the other hand, is derived by placing sensors on objects (people, animals or vehicles) whose mobility is governed by tasks other than sensing. In this context, we develop collaborative sensing and information exchange strategies for target detection, and assuring quality of service differentiation. |