5 research outputs found

    Consistent Sensor, Relay, and Link Selection in Wireless Sensor Networks

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    In wireless sensor networks, where energy is scarce, it is inefficient to have all nodes active because they consume a non-negligible amount of battery. In this paper we consider the problem of jointly selecting sensors, relays and links in a wireless sensor network where the active sensors need to communicate their measurements to one or multiple access points. Information messages are routed stochastically in order to capture the inherent reliability of the broadcast links via multiple hops, where the nodes may be acting as sensors or as relays. We aim at finding optimal sparse solutions where both, the consistency between the selected subset of sensors, relays and links, and the graph connectivity in the selected subnetwork are guaranteed. Furthermore, active nodes should ensure a network performance in a parameter estimation scenario. Two problems are studied: sensor and link selection; and sensor, relay and link selection. To solve such problems, we present tractable optimization formulations and propose two algorithms that satisfy the previous network requirements. We also explore an extension scenario: only link selection. Simulation results show the performance of the algorithms and illustrate how they provide a sparse solution, which not only saves energy but also guarantees the network requirements.Comment: 27 pages, 17 figure

    A censoring strategy for decentralized estimation in energy-constrained adaptive diffusion networks

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    This paper presents a censoring strategy for distributed estimation over adaptive networks in scenarios where energy resources are limited. Sensors apply selective communication policies in order to save energy for being able to transmit more important information later. Simulation results show an enhancement in network lifetime, by reducing communication processes among nodes with a slightly degraded result, compared with energy unconstrained schemes

    Cost Savings Associated with Filling a 3-Month Supply of Prescription Medicines

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    Background: Many patients are burdened by prescription costs, and patients, providers and policy makers may attempt to reduce these costs by substituting 3-month for 1-month supplies of medicines. Objectives: To measure the difference in out-of-pocket and total costs among patients receiving different quantities of the same prescription drug used to treat a chronic condition, and to examine patient and health system characteristics associated with the use of a 3-month supply. Methods: Data were pooled from the 2000-5 Medical Expenditure Panel Survey, a nationally representative survey of the US non-institutionalized civilian population, to compare prescription drug expenditures for medicines dispensed as both 3-month and 1-month supplies. Logistic regression was used to model correlates associated with 3-month use. The main outcome measures were the mean monthly out-of-pocket and total costs expressed in year 2005 values. Results: Forty-four percent of prescriptions examined were dispensed as 3-month supplies. The average (95% CI) monthly total and out-of-pocket costs for a 1-month supply were $US42.72 (42.01, 43.42) and $US20.44 (19.99, 20.89), respectively, while the corresponding monthly costs for a 3-month supply were $US37.95 (37.26, 38.64) and $US15.10 (14.68, 15.53). After adjustment for potential confounders, this represented a 29% decrease in out-of-pocket costs and an 18% decrease in total prescription costs through the use of a 3-month rather than a 1-month supply. Eighty percent of patients achieved some cost savings from a 3-month supply and there was considerable variation in the amount saved. There were no marked differences in the characteristics of individuals using 3-month versus 1-month supplies. Conclusions: Although such opportunities are not universally available, these findings quantify the cost savings that patients in the US can achieve through filling larger quantities of a prescription for a chronic condition.

    A Bayesian Decision Model for Intelligent Routing in Sensor Networks

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    Abstract- In this paper we propose an efficient energy-aware routing algorithm based on learning patterns. Energy and mes-sage importance are considered in a Bayesian model in order to establish intelligent decision rules that make the network economize in crucial resources. I