Data-driven modeling of phenomena in wireless sensor networks

Wireless sensor networks (WSNs) is an emerging field with applications that span scientific, engineering, medical and other disciplines. Significant human and capital infrastructure is needed for testing behavior of WSNs in real-world deployments. Sensor network simulations have the advantage of facilitating testing without going through the rigors of a deployment, but require good simulation models. However, high-quality communication and phenomena models are extremely hard to come by and suffer from need of large quantity of training data to capture the time-varying nature of the underlying phenomenon. In my dissertation, I address issues in the modeling of wireless links (communication-related) and occupancy monitoring (application phenomenon-related). For wireless link modeling, I advocate a novel machine learning-based, data-driven approach involving Hidden Markov Models (HMMs) and Mixtures of Multivariate Bernoullis (MMBs) for modeling the long and short time scale behavior of links. For occupancy modeling, I propose SCOPES, a wireless camera sensor network for gathering building occupancy traces for aiding model creation. However, both problems require large quantity of training data to model the time varying nature of the underlying phenomenon. For each instance, I solve the training data problem by using parameter-tying based model adaptation techniques that constrain the new model parameters through a nonlinear transformation of a pre-existing reference model. Using model adaptation, I showed that we can achieve significant reduction in training data requirement, thereby improving the simulation quality by enabling the construction of high-quality models

Stochastic Approach to Scheduling Multiple Divisible Tasks on a Heterogeneous Distributed Computing System

Heterogeneity has been considered in scheduling, but without taking into account the temporal variation of completion times of the sub-tasks for a divisible, independent task. In this paper, the problem of scheduling multiple, divisible independent tasks on a heterogeneous distributed computing system is addressed. The “stochastic ” approach, which was previously applied to DAG scheduling, is employed for scheduling a group of multiple divisible as well as whole independent tasks. It explicitly considers the standard deviations (temporal heterogeneity) in addition to the mean execution times in deriving a schedule, in order to model more closely what would actually happen “on average” on a temporally heterogeneous system (instead of approximating the random weights by their means only as in other approaches). Through an extensive computer simulation, it has been shown that the proposed approach can improve schedules significantly over those by a scheme which uses the average weights only.

Scopes: Smart cameras object position estimation system.

Abstract. Wireless camera sensor networks have to balance the conflicting challenges imposed by the detection performance, latency and lifetime requirements in surveillance applications. While previous studies for camera sensor networks have addressed these issues separately, they have not quantified the trade-offs between these requirements. In this paper, we discuss the design and implementation of SCOPES, a distributed Smart Camera Object Position Estimation System that balances the trade-offs associated with camera sensor networks. The main contribution of the paper is the extensive evaluation of parameters affecting the performance of the system through analysis, simulation and experimentation in real-life conditions. Our results demonstrates the effectiveness of SCOPES, which achieves detection probabilities ranging from 84% to 98% and detection latencies from 10 seconds to 18 seconds. Moreover, by using coordination schemes, the detection performance of SCOPES was improved with increased system lifetime. SCOPES highlights that intelligent system design can compensate for resource-constrained hardware and computationally simple data processing algorithms