مدیریت کیفیت داده ی سازگاری محور سیستمهای حسگر شبکه

With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications.

As new fabrication and integration technologies reduce the cost and size of wireless micro-sensors, we are witnessing another revolution that facilitates the observation and control of our physical world [1], [6], [5] and [22], just as networking technologies have changed the way individuals and organizations exchange information. Micro-sensors such as Motes from Intel and Crossbow [4] have been developed to make WSN applications possible; TinyOS [10] and [12] has been designed to provide adequate system support to facilitate sensor node programming; Several applications, such as habitat monitoring [35], ZebraNet [25], Counter-sniper system [36], environment sampling [2], target tracking [34], and structure monitoring [42], have been launched, showing the promising future of wide range of applications of wireless sensor networks (WSNs). With the main function of collecting interesting and meaningful data, the success of WSN applications is nonetheless determined by whether they can provide a high quality stream of data over a long period. The inherent feature of unattended and untethered deployment of WSN in a malicious environment, however, imposes challenges to the underlying systems. These challenges are further complicated by the fact that WSNs are usually seriously energy and storage constrained. However, most previous efforts focus on devising techniques to save the sensor node energy and thus extend the lifetime of the whole WSN. We envision that data quality management has been becoming a more and more important issue in the design of WSNs. In principle, the quality of data should reflect the timeliness and accuracy of collected data that are presented to interested recipients who make the final decision based on these data. Complementing to the work on the sensor design that improves the accuracy of sensing, in this paper, we intend to study the relationship between data quality and energy-efficient design of WSNs. To integrate and manage data quality in WSNs, we propose a framework named Orchis, which includes a set of data consistency models customized to WSNs, a set of APIs to management the quality of collected data, an adaptive protocol for data sampling, a set of consistency-driven cross layer protocols to support achieving the goals of data consistency and energy efficiency. The novelty of this work is that we propose to use consistency models, including temporal, numerical, and frequency three perspectives, as metrics to measure the quality of the collected data in wireless sensor networks, and based on these models, we propose a framework to manage data quality of WSNs. To the best of our knowledge, we are the first to propose consistency models in wireless sensor networks and to try to manage the data quality from the viewpoint of data consistency. Among these components, we address two of them, consistency models and the Alep protocol in detail in this paper. First, we formally define a new metric, data consistency model, to evaluate the data quality. Intuitively, most people think that the higher requirements of data quality, the more energy will be consumed. However, we find that this intuition is not necessarily held, and that the energy can be saved if we consider data consistency and data dynamics together. This fact in turn inspires us to attack the problem from the perspective of data consistency and data dynamics, and exploit the data consistency in system protocol design. Thus, an adaptive, lazy, and energy-efficiency data collection protocol called Alep is proposed. Finally, our comprehensive performance evaluation based on both simulation and prototype implementation shows that Alep improves the quality of data, saves energy, and extends the lifetime of WSNs. The contributions of this paper are four-fold. First, to the best of knowledge, we are the first to propose a general framework that integrates data quality management in the design of WSNs. Second, we formally define data consistency models as metrics to evaluate data quality. Third, we propose an adaptive, lazy, energy-efficiency protocol to improve data quality and save energy. Finally, a comprehensive performance evaluation has been undertaken based on both TOSSIM [12] and a prototype implementation using 13 MICA2 Motes. The rest of the paper is organized as follows. We first analyze the importance of data quality to WSN applications, then, abstract the specific consistency-related features of WSNs and their applications in Section 2. Section 3 depicts a consistency-driven data management framework. In Section 4, we present the formal definition of data consistency and data dynamics. An adaptive lazy energy-efficient protocol for data collection is described in Section 5. And Sections 6 and 7 report a comprehensive performance evaluation based on TOSSIM simulator and a prototype implementation of 13 MICA2 motes respectively. Finally, related work and conclusion remarks are discussed in Sections 8 and 9 respectively.

In this paper, we propose a consistency-driven data quality management framework Orchis and depict its two important components: consistency models and an adaptive protocol. To the best of our knowledge, we are the first that formally define a set of consistency models for WSNs. We also design and implement an adaptive, lazy, energy efficient data collection protocol to improve data quality and save energy. The comprehensive evaluation using both TOSSIM-based simulation and a prototype implementation shows that the proposed Alep protocol indeed reduces the number of delivered messages, improves the quality of the collected data and saves energy.