Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, i.e., the density of the measure points in a field. By properly scheduling, we want to retain the high fidelity of data collection. In this paper, we propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, we dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. Furthermore, during a short time period, a sensor may report similar readings. Such a correlation in the data reported from the same sensor is called temporal correlation, which can be explored to further save energy. We develop a generic framework to address several important technical challenges, including how to partition the sensors into clusters, how to dynamically maintain the clusters in response to environmental changes, how to schedule the sensors in a cluster, how to explore temporal correlation, and how to restore the data in the sink with high fidelity. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic dataset.

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Abstract-Sensor scheduling plays a critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we deal with a challenging task: Without accurate location information, how do we schedule sensor nodes to save energy and meet both constraints of sensing coverage and network connectivity? Our approach utilizes an integrated method that provides statistical sensing coverage and guaranteed network connectivity. We use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Our method is totally distributed, is able to dynamically adjust sensing coverage with guaranteed network connectivity, and is resilient to time asynchrony. We present analytical results to disclose the relationship among node density, scheduling parameters, coverage quality, detection probability, and detection delay. Analytical and simulation results demonstrate the effectiveness of our joint scheduling method.

Abstract-Wireless sensor networks consist of a large number of tiny sensors that have only limited energy supply. One of the major challenges in constructing such networks is to maintain long network lifetime as well as sufficient sensing areas. To achieve this goal, a broadly-used method is to turn off redundant sensors. In this paper, the problem of estimating redundant sensing areas among neighbouring wireless sensors is analysed. We present simple methods to estimate the degree of redundancy without the knowledge of location or directional information. We also provide tight upper and lower bounds on the probability of complete redundancy and on the average partial redundancy. With random sensor deployment, our analysis shows that partial redundancy is more realistic for real applications, as complete redundancy is expensive, requiring up to 11 neighbouring sensors to provide a 90 percent chance of complete redundancy. Based on the analysis, we propose a scalable Lightweight Deployment-Aware Scheduling (LDAS) algorithm, which turns off redundant sensors without using accurate location information. Simulation study demonstrates that the LDAS algorithm can reduce network energy consumption and provide desired QoS requirement effectively.

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The Material Point Method (MPM) has been shown to facilitate effective simulations of physically complex and topologically challenging materials, with a wealth of emerging applications in computational engineering and visual computing. Borne out of the extreme importance of regularity, MPM is given attractive parallelization opportunities on high-performance modern multiprocessors. Parallelization of MPM that fully leverages computing resources presents challenges that require exploring an extensive design-space for favorable data structures and algorithms. Unlike the conceptually simple CPU parallelization, where the coarse partition of tasks can be easily applied, it takes greater effort to reach the GPU hardware saturation due to its many-core SIMT architecture. In this paper we introduce methods for addressing the computational challenges of MPM and extending the capabilities of general simulation systems based on MPM, particularly concentrating on GPU optimization. In addition to our open-source high-performance framework, we also conduct performance analyses and benchmark experiments to compare against alternative design choices which may superficially appear to be reasonable, but can suffer from suboptimal performance in practice. Our explicit and fully implicit GPU MPM solvers are further equipped with a Moving Least Squares MPM heat solver and a novel sand constitutive model to enable fast simulations of a wide range of materials. We demonstrate that more than an order of magnitude performance improvement can be achieved with our GPU solvers. Practical high-resolution examples with up to ten million particles run in less than one minute per frame.

Industrial knitting machines are commonly used to manufacture complicated shapes from yarns; however, designing patterns for these machines requires extensive training. We present the first general visual programming interface for creating 3D objects with complex surface finishes on industrial knitting machines. At the core of our interface is a new, augmented, version of the stitch mesh data structure. The augmented stitch mesh stores low-level knitting operations per-face and encodes the dependencies between faces using directed edge labels. Our system can generate knittable augmented stitch meshes from 3D models, allows users to edit these meshes in a way that preserves their knittability, and can schedule the execution order and location of each face for production on a knitting machine. Our system is general, in that its knittability-preserving editing operations are sufficient to transform between any two machine-knittable stitch patterns with the same orientation on the same surface. We demonstrate the power and flexibility of our pipeline by using it to create and knit objects featuring a wide range of patterns and textures, including intarsia and Fair Isle colorwork; knit and purl textures; cable patterns; and laces.

-Secure in-network aggregation in Wireless Sensor Networks (WSNs) is a necessary and challenging task. In this paper, we first propose integration of system monitoring modules and intrusion detection modules in the context of WSNs. We propose an Extended Kalman Filter (EKF) based mechanism to detect false injected data. Specifically, by monitoring behaviors of its neighbors and using EKF to predict their future states (actual in-network aggregated values), each node aims at setting up a normal range of the neighbors' future transmitted aggregated values. This task is challenging because of potentially high packet loss rate, harsh environment, sensing uncertainty, etc. We illustrate how to use EKF to address this challenge to create effective local detection mechanisms. Using different aggregation functions (average, sum, max, and min), we present how to obtain a theoretical threshold. We further apply an algorithm of combining Cumulative Summation (CUSUM) and Generalized Likelihood Ratio (GLR) to increase detection sensitivity. To overcome the limitations of local detection mechanisms, we illustrate how our proposed local detection approaches work together with the system monitoring module to differentiate between malicious events and emergency events. We conduct experiments and simulations to evaluate local detection mechanisms under different aggregation functions.