Near-optimal sensor placements: maximizing information while minimizing communication cost

Abstract

When monitoring spatial phenomena with wireless sensor networks, selecting the best sensor placements is a fundamental task. Not only should the sensors be informative, but they should also be able to communicate efficiently. In this paper, we present a data-driven approach that addresses the three central aspects of this problem: measuring the predictive quality of a set of sensor locations (regardless of whether sensors were ever placed at these locations), predicting the communication cost involved with these placements, and designing an algorithm with provable quality guarantees that optimizes the NP-hard tradeoff. Specifically, we use data from a pilot deployment to build non-parametric probabilistic models called Gaussian Processes (GPs) both for the spatial phenomena of interest and for the spatial variability of link qualities, which allows us to estimate predictive power and communication cost of unsensed locations. Surprisingly, uncertainty in the representation of link qualities plays an important role in estimating communication costs. Using these models, we present a novel, polynomial-time, data-driven algorithm, pSPIEL, which selects Sensor Placements at Informative and cost-Effective Locations. Our approach exploits two important properties of this problem: submodularity, formalizing the intuition that adding a node to a small deployment can help more than adding a node to a large deployment; and locality, under which nodes that are far from each other provide almost independent information. Exploiting these properties, we prove strong approximation guarantees for our pSPIEL approach. We also provide extensive experimental validation of this practical approach on several real-world placement problems, and built a complete system implementation on 46 Tmote Sky motes, demonstrating significant advantages over existing methods.

High Quality Sensor Placement for SHM Systems: Refocusing on Application Demands

“…There is a near optimal sensor placement algorithm where the sensor placement is chosen to be informative and energy efficient [10]. Entropy is used to measure the quality of the data collected and the placement is gradually refined by a nonparametric Gaussian Process.…”

Investigating Coverage and Connectivity Trade-offs in Wireless Sensor Networks: The Benefits of MOEAs

“…The authors present a polynomial-time, data-driven algorithm using non-parametric probabilistic models called Gaussian Processes. Since their work requires sensor and link quality data collected at an initial deployment, the work of Krause et al (2006) complements the present study, as we can determine an optimized deployment without any preceding data collection.…”

“…To the best of our knowledge, only Krause et al (2006) consider both coverage and communication in a realistic scenario. The authors present a polynomial-time, data-driven algorithm using non-parametric probabilistic models called Gaussian Processes.…”

Coordinated sampling sans Origin-Destination identifiers: Algorithms and analysis

“…cSamp-T provides more fine-grained flow coverage objectives and reduces duplicated flow reports. Sensor network monitoring: There has been recent work applying the theory of maximizing submodular functions in sensor networks [34,35]. The problem of placing sensors robust to adversarial objectives [11] is conceptually similar to maximizing the minimum fractional coverage.…”