An energy-efficient quality adaptive framework for multi-modal sensor context recognition.
ABSTRACT In pervasive computing environments, under- standing the context of an entity is essential for adapting the application behavior to changing situations. In our view, context is a high-level representation of a user or entity's state and can capture location, activities, social relationships, capabilities, etc. Inherently, however, these high-level context metrics are difficult to capture using uni-modal sensors only, and must therefore be inferred with the help of multi-modal sensors. However a key challenge in supporting context-aware pervasive computing environments, is how to determine in an energy-efficient manner multiple (potentially competing) high- level context metrics simultaneously using low-level sensor data streams about the environment and the entities present therein. In this paper, we first highlight the intricacies of determining multiple context metrics as compared to a single context, and then develop a novel framework and practical implementa- tion for this problem. The proposed framework captures the tradeoff between the accuracy of estimating multiple context metrics and the overhead incurred in acquiring the necessary sensor data stream. In particular, we develop a multi-context search heuristic algorithm that computes the optimal set of sensors contributing to the multi-context determination as well as the associated parameters of the sensing tasks. Our goal is to satisfy the application requirements for a specified accuracy at a minimum cost. We compare the performance of our heuristic based framework with a brute-forced approach for multi- context determination. Experimental results with SunSPOT sensors demonstrate the potential impact of the proposed framework.
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ABSTRACT: This paper examines the problem of target detection by a wireless sensor network. Sensors acquire measurements emitted from the target that are corrupted by noise, and initially make individual decisions about the presence/absence of the target. We propose the local vote decision fusion algorithm, in which sensors first correct their decisions using decisions of neighboring sensors, and then make a collective decision as a network. An explicit formula that approximates the system's decision threshold for a given false alarm rate is derived using limit theorems for random fields, which provides a theoretical performance guarantee for the algorithm. We examine both distance- and nearest-neighbor-based versions of the local vote algorithm for grid and random sensor deployments and show that, in many situations, for a fixed-system false alarm, the local vote correction achieves significantly higher target detection rate than decision fusion based on uncorrected decisions. The algorithm does not depend on the signal model and is shown to be robust to different types of signal decay. We also extend this framework to temporal fusion, where information becomes available over time.IEEE Transactions on Signal Processing 02/2008; · 3.20 Impact Factor
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ABSTRACT: An mission planner's point of view of sensor- enabled detection systems is considered and a hypothesis-testing- based computational framework for evaluating the quality of information (QoI) supported by a sensor network deployment is explored. Through a common, modular analysis framework, that decomposes the computational burden of QoI evaluation, the QoI properties of various fusion/decision architectures are investigated and trade-offs explored at the sensor, cluster, and system-level. Both finite and infinite-sized sensor networks are considered and extensions of the analysis framework to faulty sensor and the impact of calibration are also investigated.1
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ABSTRACT: Declarative queries are proving to be an attractive paradigm for inter- acting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this ar- ticle, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the com- bination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identi- fying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach pro- vides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.The VLDB Journal 01/2005; 14:417-443. · 1.70 Impact Factor