Collection of the sensed data in a wireless sensor network at one or more sink(s) is a well studied problem and there are a lot of efficient solutions for a variety of wireless sensor network configurations and applic...
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ISBN:
(纸本)9781479946181
Collection of the sensed data in a wireless sensor network at one or more sink(s) is a well studied problem and there are a lot of efficient solutions for a variety of wireless sensor network configurations and application requirements. These methods are often optimized towards collection of the sensed data at a predetermined base station or sink. This inherently reduces the agility of the wireless sensor network as the flow of information is not easily changeable after the establishment of the routing and data collection algorithms. This paper presents an efficient data dissemination method based on the compressed sensing theory that allows each sensor node to take the role of a sink. Agile sink selection is especially advantageous in scenarios where the sink or the end user of the wireless sensor network is mobile. The proposed method allows availing the global state of the environment by fetching a small set of data from any arbitrary node. Our evaluations prove the better performance of our technique over existing methods. Also a comparison with an oracle-based approach gives sufficient experimental evidences of a nearly optimal performance of our method.
Wireless sensor networks (WSNs) are currently employed in a vast number of different applications ranging from home automation and health care to military systems. Although their application may vary greatly, WSNs sha...
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ISBN:
(纸本)9781479946181
Wireless sensor networks (WSNs) are currently employed in a vast number of different applications ranging from home automation and health care to military systems. Although their application may vary greatly, WSNs share a common set of characteristics such as a limited energy supply and simple hardware. A common issue related with the application of WSNs is sensor localization, for some types of applications it is important that the sensors know the relative or absolute position of other sensors in the network, such as surveillance of monitoring networks. If sensors are randomly placed they may resort a wide range of methods such as Global Navigation Satellite systems (GNSS) or received signal strength indicators (RSSI). In this work we present an alternative to relative sensor localization by employed a crossed dipole antenna in the reception and a known polarization in the transmission. The accuracy of the proposed methods is measured trough numerical simulations and results are presented.
Executing analytics functionalities over data from highly distributed data sources and data streams is at the very core of the vast majority of Industrial Internet of Things (IIoT) applications. State of the art strea...
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ISBN:
(纸本)9781728105703
Executing analytics functionalities over data from highly distributed data sources and data streams is at the very core of the vast majority of Industrial Internet of Things (IIoT) applications. State of the art streaming engines provide the means for high performance analytics over high velocity IIoT streams, yet they still need significant programming and customization efforts when deployed in heterogeneous industrial environments. This paper introduces a configurable engine for distributed data analytics for IIoT applications. The engine leverages the performance of state of the art data streaming middleware platforms, which it augments with a set of digital models for configuring DDA operations. As such the introduced engine reduces the effort needed to implement and deploy distributed data analytics in IIoT environments. The engine is available as open source software and has been validated in a various real-life IIoT applications in different environments.
A wireless sensor network (WSN) is usually deployed in a field of interest (FoI) for detecting or monitoring some special events and then forwarding the aggregated data to the designated data center through sink nodes...
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ISBN:
(纸本)9781538639917
A wireless sensor network (WSN) is usually deployed in a field of interest (FoI) for detecting or monitoring some special events and then forwarding the aggregated data to the designated data center through sink nodes or gateways. Traditionally, the WSN requires the intensive deployment in which the extra sensor nodes are deployed to achieve the required coverage level. Fortunately, depending on the developments of the unmanned aerial vehicle (UAV) techniques, the UAV has been widely adopted in both military and civilian applications. Comparing with the traditional mobile sensor nodes, the UAV has much faster moving speed, longer deployment range and relatively longer serving time. Consequently, the UAV can be considered as a perfect carrier for the existing sensing equipment and used to form a UAV-based WSN (UWSN). In this paper, we theoretically analyse the coverage problem in the UWSN. Based on the integral geometry, we solve the aforementioned question. The experimental results further verifies our theoretical results.
In this work, we propose Traffic Data Enrichment sensor (TraDES), towards a low-cost traffic sensor for Intelligent Transportation System (ITS) based on heterogeneous data fusion. TraDES aims at fusing data from vehic...
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ISBN:
(纸本)9781728105703
In this work, we propose Traffic Data Enrichment sensor (TraDES), towards a low-cost traffic sensor for Intelligent Transportation System (ITS) based on heterogeneous data fusion. TraDES aims at fusing data from vehicular traces with road traffic data to enrich current spatiotemporal traffic data. In that direction, we propose a robust methodology to group spatially and temporally these different data sources, producing a vehicular trace with its respective traffic conditions, which is given as input to a learning-based model based on Artificial Neural Networks (ANN). Hence, TraDES is an enriched traffic sensor that is able to sense (detect) traffic conditions using a scalable and low-cost approach and to increase the spatiotemporal traffic data coverage.
Network simulation is an important tool for testing and evaluating wireless sensor network applications. Parallel simulation strategies improve the scalability of these tools. However, achieving high performance depen...
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ISBN:
(纸本)9781457705137
Network simulation is an important tool for testing and evaluating wireless sensor network applications. Parallel simulation strategies improve the scalability of these tools. However, achieving high performance depends on reducing the synchronization overhead among simulation processes. In this paper we present an optimistic simulation algorithm with support for backtracking and re-execution. The algorithm reduces the number of synchronization cycles to the number of transmissions in the network under test. We implement SnapSim, an extension to the popular Avrora simulator, based on this algorithm. The experimental results show that our prototype system improves the performance of Avrora by 2 to 10 times for typical network-centric sensor network applications, and up to three orders of magnitude for applications that use the radio infrequently.
In this paper, we study properties of distributed consensus in layered sensor networks of the multi-layer multi-group (MLMG) structure. We show that properly designed MLMG networks maintain decentralized communication...
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ISBN:
(纸本)9781479946181
In this paper, we study properties of distributed consensus in layered sensor networks of the multi-layer multi-group (MLMG) structure. We show that properly designed MLMG networks maintain decentralized communication, whereas show the advantage of centralized structures. In particular, they require less number of transmissions required to reach consensus. This feature is critical for efficient distributedcomputing in large-scale sensor network applications. For typical classes of MLMG networks, we mathematically characterize the reduced number of transmissions compared to equivalent egalitarian decentralized structures of the same consensus dynamics. This explicit characterization based on simple graphical characteristics of MLMG structures permits an efficient design of large-scale network structures to meet desired performance requirements. In addition, we characterize the asymptotic and transient properties of consensus in MLMG networks of limited channel rates, using the probabilistic quantization schemes.
In this poster, we present a new lightweight multi-threaded software model for target location detection system. Our localization algorithm is based on trilateration using RSSI for ranging. This work can be applied fo...
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ISBN:
(纸本)9780769550411
In this poster, we present a new lightweight multi-threaded software model for target location detection system. Our localization algorithm is based on trilateration using RSSI for ranging. This work can be applied for collecting the location information of mobile sensor nodes in the network. Our software runs on the sensor nodes and on the base station. To support our clam we performed experimental analysis on telosb motes.
This paper focuses on improving the geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) and proposes a geographic routing oriented sleep s...
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ISBN:
(纸本)9781457705137
This paper focuses on improving the geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) and proposes a geographic routing oriented sleep scheduling (GSS) algorithm. The algorithm analysis and simulation results show that GSS can shorten the length of the first explored transmission path of TPGF, compared with the connected-k neighborhood (CKN) algorithm.
We study how knowledge of a moving object's path can be used to select sensors in a network that maximize the coverage of its path. We propose a mobility model that combines the shortest path between two points wi...
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ISBN:
(纸本)9781538654705
We study how knowledge of a moving object's path can be used to select sensors in a network that maximize the coverage of its path. We propose a mobility model that combines the shortest path between two points with random movement. Given the mobility model, we have different knowledge levels in terms of knowing nothing, the start, destination, movement model, and the whole path. We present a framework to assign weights to points on the movement grid based on the knowledge level and to greedily select sensors to maximize weighted coverage of the grid. We show in simulations of random movement that knowing more information generally has better performance, but for certain levels of knowledge, this decreases as the randomness increases. We also find that it is possible to obtain the maximum coverage by assuming the target follows the shortest path when the randomness is below a certain threshold. We verified these results on real human mobility traces.
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