Ubiquitous data collection for mobile users in wireless sensor networks

Li, Zhenjiang; Li, Mo; Wang, Jiliang; Cao, Zhichao

doi:10.1109/infcom.2011.5935040

Cited by 80 publications

(70 citation statements)

References 24 publications

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“…We therefore introduce the concept of Omnidirectional Passive Human Detection (Omni-PHD), in referring to the problem of realizing passive human detection with a coverage of disk-like boundary, by employing linkcentric detecting unit architectures. Besides the applicationdriven motivations for Omni-PHD, we also envision our work as an effort towards bridging the gap between the theoretical analysis of coverage problems in wireless sensor networks (WSN) and the practical hardware performances [7] [8] [9], as well as guidelines for applications such as monitoring [10] localization [11], data collection [12], topology analysis [13], etc.…”

Section: Introductionmentioning

confidence: 99%

Towards omnidirectional passive human detection

Zhou

Yang

et al. 2013

2013 Proceedings IEEE INFOCOM

102

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Abstract-Passive human detection and localization serve as key enablers for various pervasive applications such as smart space, human-computer interaction and asset security. The primary concern in devising scenario-tailored detecting systems is the coverage of their monitoring units. In conventional radiobased schemes, the basic unit tends to demonstrate a directional coverage, even if the underlying devices are all equipped with omnidirectional antennas. Such an inconsistency stems from the link-centric architecture, creating an anisotropic wireless propagating environment. To achieve an omnidirectional coverage while retaining the link-centric architecture, we propose the concept of Omnidirectional Passive Human Detection, and investigate to harness the PHY layer features to virtually tune the shape of the unit coverage by fingerprinting approaches, which is previously prohibited with mere MAC layer RSSI. We design the scheme with ubiquitously deployed WiFi infrastructure and evaluate it in typical multipath-rich indoor scenarios. Experimental results show that our scheme achieves an average false positive of 8% and an average false negative of 7% in detecting human presence in 4 directions.

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Section: Introductionmentioning

confidence: 99%

Towards omnidirectional passive human detection

Zhou

Yang

et al. 2013

2013 Proceedings IEEE INFOCOM

102

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“…In Li et al [2011], Shah et al [2003], Pasztor et al [2007], Somasundara et al [2006], Wang et al [2005], and Zhao and Yang [2009], the use of mobile nodes was proposed to move around in the deployed area and collect data from sensor nodes. Depending on the applications, their mobility can be either controlled or not, and these mobile nodes may collect data from sensor nodes within the range of one or multiple hops.…”

Section: Data Collectionmentioning

confidence: 99%

Data pre-forwarding for opportunistic data collection in wireless sensor networks

Brown

Sreenan

2012

2012 Ninth International Conference on Networked Sensing (INSS)

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Opportunistic data collection in wireless sensor networks uses passing smartphones to collect data from sensor nodes, thus avoiding the cost of multiple static sink nodes. Based on the observed mobility patterns of smartphone users, sensor data should be preforwarded to the nodes that are visited more frequently with the aim of improving network throughput. In this article, we construct a formal network model and an associated theoretical optimization problem to maximize the throughput subject to energy constraints of sensor nodes. Since a centralized controller is not available in opportunistic data collection, data pre-forwarding (DPF) must operate as a distributed mechanism in which each node decides when and where to forward data based on local information. Hence, we develop a simple distributed DPF mechanism with two heuristic algorithms, implement this proposal in Contiki-OS, and evaluate it thoroughly. We demonstrate empirically, in simulations, that our approach is close to the optimal solution obtained by a centralized algorithm. We also demonstrate that this approach performs well in scenarios based on real mobility traces of smartphone users. Finally, we evaluate our proposal on a small laboratory testbed, demonstrating that the distributed DPF mechanism with heuristic algorithms performs as predicted by simulations, and thus that it is a viable technique for opportunistic data collection through smartphones.

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“…The network manager can require BS to collect data. In practice, sensor nodes are often organized as one or multiple long routes originated from BS and extended hop by hop to the boundary of the physical area [15]. In this study, we assume that all the nodes are organized as a collection tree which consists of a root BS and Q sub trees.…”

Section: Network An Adversary Models a System Modelmentioning

confidence: 99%

“…Wireless sensor nodes are deployed in various kinds of physical areas such as factories, office areas, forests or rivers to sense the physical space [15], [18], [19]. Sensor nodes convert physical parameters such as heat, light intensity or temperature to data items and send them to base station (BS) through multiple intermediate nodes.…”

Section: Introductionmentioning

confidence: 99%

Achieving Private, Scalable, and Precise Data Collection in Wireless Sensor Networks

2012

2012 IEEE 18th International Conference on Parallel and Distributed Systems

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Abstract-Wireless Sensor Networks (WSN) become increasingly popular to collect data over a large area. Given the collected data set, the network manager can extract various kinds of aggregate statistics from the set to characterize the physical space. On the collection of the data, three requirements should be imposed: (1) Privacy: as sensor nodes are source limited and often deployed in an open environment, the sensed data suffer from privacy vulnerabilities. Secure mechanism should be provided to protect data privacy; (2) Communication efficiency: collecting data from large-scale sensor networks often involves large-volume data generation and transmission, which may quickly consume the energy of the WSN. To prolong the lifetimes of the sensor nodes, the sensed data should be transmitted in lightweight manner; (3) Accuracy: the sensed data should be recovered accurately at the base station (BS) so that the manager can manipulate them freely to achieve any precise aggregate statistic he prefers. To satisfy these requirements, we propose two novel privacy-preserving data collection schemes based on compressive sensing techniques. Our schemes address the privacy, communication efficiency and accuracy issues simultaneously. Detailed theoretical analysis and simulation results confirm the high performance of the proposed schemes.

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Ubiquitous data collection for mobile users in wireless sensor networks

Cited by 80 publications

References 24 publications

Towards omnidirectional passive human detection

Towards omnidirectional passive human detection

Data pre-forwarding for opportunistic data collection in wireless sensor networks

Achieving Private, Scalable, and Precise Data Collection in Wireless Sensor Networks

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