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What Does Model-Driven Data Acquisition Really Achieve in Wireless Sensor Networks?
"... Abstract—Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to ..."
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Abstract—Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99 % of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime—a remarkable result per se, but a far cry from the aforementioned 99 % data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary. I.
Uncertain Time-Series Similarity: Return to the Basics
"... In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product qual ..."
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In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others. Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms. In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Articles from this volume were invited to present
TriopusNet: Automating Wireless Sensor Network Deployment and Replacement in Pipeline Monitoring
"... This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet ..."
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This study presents TriopusNet, a mobile wireless sensor network system for autonomous sensor deployment in pipeline monitoring. TriopusNet works by automatically releasing sensor nodes from a centralized repository located at the source of the water pipeline. During automated deployment, TriopusNet runs a sensor deployment algorithm to determine node placement. While a node is flowing inside the pipeline, it performs placement by extending its mechanical arms to latch itself onto the pipe’s inner surface. By continuously releasing nodes into pipes, the TriopusNet system builds a wireless network of interconnected sensor nodes. When a node runs at a low battery level or experiences a fault, the TriopusNet system releases a fresh node from the repository and performs a node replacement algorithm to replace the failed node with the fresh one. We have evaluated the TriopusNet system by creating and collecting real data from an experimental pipeline testbed. Comparing with the nonautomated static deployment, TriopusNet is able to use less sensor nodes to cover a sensing area in the pipes while maintaining network connectivity among nodes with high data collection rate. Experimental results also show that TriopusNet can recover from the network disconnection caused by a battery-depleted node and successfully replace the battery-depleted node with a fresh node.
Real-time data analytics in sensor networks
- in Managing and Mining Sensor Data
, 2012
"... Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the ..."
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Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series.
1Adaptive Rectifier Driven by Power Intake Predictors for Wind Energy Harvesting Sensor Networks
"... Abstract—This paper presents a power management technique for improving the efficiency of harvesting energy from air-flows in wireless sensor networks (WSNs) applications. The proposed architecture consists of a two-stage energy conversion circuit: an AC-DC converter followed by a DC-DC buck-boost r ..."
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Abstract—This paper presents a power management technique for improving the efficiency of harvesting energy from air-flows in wireless sensor networks (WSNs) applications. The proposed architecture consists of a two-stage energy conversion circuit: an AC-DC converter followed by a DC-DC buck-boost regulator with Maximum Power Point Tracking (MPPT) capability. The key feature of the proposed solution is the adaptive hybrid voltage rectifier which exploits both passive and active topologies combined with power prediction algorithms. The adaptive con-verter significantly outperforms other solutions, increasing the efficiency between 10 % and 30 % with respect to the only-passive and the only-active topologies. To assess the performance of this approach in a real-life scenario, air-flow data have been collected by deploying WSN nodes interfaced with a wind micro-turbine in an underground tunnel of the Metro B1 line in Rome. It is shown that, by using the adaptive AC-DC converter combined with power prediction algorithms, nodes deployed in the tunnel can harvest up to 22 % more energy with respect to previous methods. Finally, it is shown that using power management techniques optimized for the specific scenario, the overall system overhead, in terms of average number of sampling performed per day by a node, is reduced of up to 93%. Keywords—Energy harvesting, power management technique, prediction algorithm, voltage rectifier, wireless sensor networks. I.
Software Engineering for Mobility: Reflecting on the Past, Peering into the Future
, 2014
"... At the end of the second millennium, mobility was a hot research topic. Physical mobility of devices was becoming commonplace with the availability of cheap wireless cards, the first attempts to transform phones into personal do-it-all devices were beginning to appear, and mobile ad hoc networks wer ..."
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At the end of the second millennium, mobility was a hot research topic. Physical mobility of devices was becoming commonplace with the availability of cheap wireless cards, the first attempts to transform phones into personal do-it-all devices were beginning to appear, and mobile ad hoc networks were attracting a huge interest from many research communities. Logical mobility of code was still going strong as a design option for distributed systems, with the Java language providing some of the ready-to-use building blocks. In 2000, when we put forth a research “roadmap ” for software engineering for mobility, the challenges posed by this dynamic scenario were many. A decade and a half later, many things have changed. Mobility is no longer exotic: we juggle multiple personal devices every day while on the move, plus we grab and update applications on a whim from virtual stores. Indeed, some trends and visions we considered in our original paper materialized, while others faded, disappeared, or morphed into something else. Moreover, some players unexpected at the time (e.g., cloud computing and online social networks) appeared on the scene as game changers. In this paper we revisit critically our original vision, reflecting on the past and peering into the future of the lively and exciting research area of mobility. Further, we ask ourselves to what extent the software engineering community is still interested in taking up the challenges mobility bears.
Guaranteeing Communication Quality in Real World WSN Deployments
"... April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by: ..."
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April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by:
SAP AG
"... or classroom use provided that the copies are not made or distributed for profit or commercial advantage, the ACM copyright/server notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc. To copy otherwise, to republish, to post on ..."
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or classroom use provided that the copies are not made or distributed for profit or commercial advantage, the ACM copyright/server notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior specific permission and/or a fee. c ○ 20XX ACM 1529-3785/20XX/0700-0111 $5.00 ACM Transactions on Sensor Networks, Vol. X, No. X, XX 20XX, Pages 111–154. 112 · Tony O’Donovan et. al. Today’s industrial facilities such as oil refineries, chemical plants, and factories rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this paper we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software,
