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19
Near-optimal sensor placements: Maximizing information while minimizing communication cost
- In IPSN
, 2006
"... 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 ..."
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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.
Robust Sensor Placements at Informative and Communication-Efficient Locations
, 2010
"... 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 ..."
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Cited by 10 (0 self)
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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 hypothetical sensor 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 communication-Efficient Locations. Our approach exploit two important properties of this problem: submodularity, formalizing the intuition that adding a node to a small deployment can help more than adding it 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 approach. We also show how our placements can be made robust against changes in the environment,
Spatial-temporal coverage optimization in wireless sensor networks
- IEEE TRANS. MOB. COMPUT
, 2011
"... Mission-driven sensor networks usually have special lifetime requirements. However, the density of the sensors may not be large enough to satisfy the coverage requirement while meeting the lifetime constraint at the same time. Sometimes, coverage has to be traded for network lifetime. In this paper ..."
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Cited by 9 (3 self)
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Mission-driven sensor networks usually have special lifetime requirements. However, the density of the sensors may not be large enough to satisfy the coverage requirement while meeting the lifetime constraint at the same time. Sometimes, coverage has to be traded for network lifetime. In this paper, we study how to schedule sensors to maximize their coverage during a specified network lifetime. Unlike sensor deployment, where the goal is to maximize the spatial coverage, our objective is to maximize the spatialtemporal coverage by scheduling sensors ’ activity after they have been deployed. Since the optimization problem is NP-hard, we first present a centralized heuristic whose approximation factor is proved to be 1 2, and then, propose a distributed parallel optimization protocol (POP). In POP, nodes optimize their schedules on their own but converge to local optimality without conflict with one another. Theoretical and simulation results show that POP substantially outperforms other schemes in terms of network lifetime, coverage redundancy, convergence time, and event detection probability.
Minimalist multiple target tracking using directional sensor beams
- In Proceedings IEEE International Conference on Intelligent Robots and Systems
"... Abstract — We consider the problem of determining the paths of multiple, unpredictable moving bodies in a cluttered environment using weak detection sensors that provide simple crossing information. Each sensor is a beam that, when broken, provides the direction of the crossing (one bit) and nothing ..."
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Abstract — We consider the problem of determining the paths of multiple, unpredictable moving bodies in a cluttered environment using weak detection sensors that provide simple crossing information. Each sensor is a beam that, when broken, provides the direction of the crossing (one bit) and nothing else. Using a simple network of beams, the individual paths are separated and reconstructed as well as possible, up to combinatorial information about the route taken. In this setup, simple filtering algorithms are introduced, and a low-cost hardware implementation that demonstrates the practicality of the approach is shown. The results may apply in settings such as verification of multirobot system execution, surveillance and security, and unobtrusive behavioral monitoring for wildlife and the elderly. I.
Distributed Critical Location Coverage in Wireless Sensor Networks with Lifetime Constraint
"... Abstract—In many surveillance scenarios, there are some known critical locations where the events of concern are expected to occur. A common goal in such applications is to use sensors to monitor these critical locations with sufficient quality of surveillance within a designated period. However, wi ..."
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Abstract—In many surveillance scenarios, there are some known critical locations where the events of concern are expected to occur. A common goal in such applications is to use sensors to monitor these critical locations with sufficient quality of surveillance within a designated period. However, with limited sensing resources, the coverage and lifetime requirement may not be satisfied at the same time. Thus, sometimes the sensor needs to reduce its duty cycle in order to satisfy the stringent lifetime constraint. In this paper, we model the critical location coverage problemusingapointcoverage model withtheobjective of scheduling sensors to maximize the event detection probability while meeting the network lifetime requirement. We show that this problem is NP-hard and propose a distributed algorithm with a provable approximation ratio of 0.5. Extensive simulations show that the proposed distributed algorithm outperforms the extensions of several state-of-the-art schemes with a significant margin while preserving the network lifetime requirement. I.
Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks
"... In order to prolong the lifetime of a wireless sensor network (WSN) devoted to monitoring an area of interest, a useful means is to exploit network redundancy activating only the sensors that are strictly necessary for coverage and making them work with the minimum necessary sensing radius. In this ..."
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In order to prolong the lifetime of a wireless sensor network (WSN) devoted to monitoring an area of interest, a useful means is to exploit network redundancy activating only the sensors that are strictly necessary for coverage and making them work with the minimum necessary sensing radius. In this paper we introduce the first algorithm that reduces sensor coverage redundancy through joint Sensor Activation and sensing Radius Adaptation (SARA) in general application scenarios comprising two classes of devices: Sensors with variable sensing radius and sensors with fixed sensing radius. This device heterogeneity is explicitly addressed by modeling the coverage problem through Voronoi-Laguerre diagrams that, differently from Voronoi diagrams, allow to correctly identify each sensor coverage region depending on the sensor current radius and the radii of its neighboring nodes. SARA executes quickly with guaranteed termination and, given the currently available nodes, it always guarantees maximum coverage. By means of extensive simulations we show that SARA obtains remarkable improvements with respect to previous solutions, ensuring, in
Maximizing Submodular Set Function with Connectivity Constraint: Theory and Application to Networks
"... Abstract—In this paper, we investigate the wireless network deployment problem, which seeks the best deployment of a given limited number of wireless routers. We found that many goals for network deployment, such as maximizing the number of covered users or areas, or the total throughput of the netw ..."
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Abstract—In this paper, we investigate the wireless network deployment problem, which seeks the best deployment of a given limited number of wireless routers. We found that many goals for network deployment, such as maximizing the number of covered users or areas, or the total throughput of the network, can be modelled with the submodular set function. Specifically, given a set of routers, the goal is to find a set of locations S, each of which is equipped with a router, such that S maximizes a predefined submodular set function. However, this deployment problem is more difficult than the traditional maximum submodular set function problem, e.g., the maximum coverage problem, because it requires all the deployed routers to form a connected network. In addition, deploying a router in different locations might consume different costs. To address these challenges, this paper introduces two approximation algorithms, one for homogeneous deployment cost scenarios and the other for heterogeneous deployment cost scenarios. Our simulations, using synthetic data and real traces of census in Taipei, show that the proposed algorithms achieve a better performance than other heuristics. I.
4. TITLE AND SUBTITLE Robust Sensor Placements at Informative and Communication-efficient Locations
, 2010
"... Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing an ..."
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Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information,
Complete Abstract:
, 2010
"... Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, we envision a two-tiered early warning system designed to identify the signs of clinical deterioration and provide early warning of se ..."
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Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. In this paper, we envision a two-tiered early warning system designed to identify the signs of clinical deterioration and provide early warning of serious clinical events. The first tier of the system automatically identifies patients at risk of clinical deterioration from existing electronic medical record databases. The second tier performs real-time clinical event detection based on real-time vital sign data collected from on-body wireless sensors attached to those high-risk patients. We employ machine-learning techniques to analyze data from both tiers, assigning scores to patients in real time. The assigned scores can then be used to trigger early-intervention alerts. Preliminary study of an early warning system component and a wireless clinical monitoring system component demonstrate the feasibility of this two-tiered approach.... Read complete abstract on page 2.
