Robust Planarization of Unlocalized Wireless Sensor Networks

Zhang, Fenghui; Jiang, Anxiao; Chen, Jianer

doi:10.1109/infocom.2008.131

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…The sensors form a network. We suppose that the sensors collaboratively apply boundary detection algorithms to identify the nodes adjacent to holes of the domain [10, 11, 14-17, 22, 31, 31, 39, 44], and extract a connected planar subgraph G [18,20,38,46]. This extracted graph G stays on an (unknown) surface Σ where the faces corresponding to holes (i.e., boundaries) are locally marked.…”

Section: Network Setupmentioning

confidence: 99%

Decentralized human trajectories tracking using hodge decomposition in sensor networks

Yin

Ding

et al. 2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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With the recent development of localization and tracking systems for both indoor and outdoor settings, we consider the problem of sensing, representing and analyzing human movement trajectories that we expect to gather in the near future. In this paper, we propose to use the topological representation, which records how a target moves around the natural obstacles in the underlying environment. We demonstrate that the topological information can be sufficiently descriptive for many applications and efficient enough for storing, comparing and classifying these natural human trajectories. We pre-process the sensor network with a purely decentralized algorithm such that certain edges are given numerical weights. Then we can perform trajectory classification by simply summing up the edge weights along the trajectory. Our method supports real-time classification of trajectories with minimum communication cost. We test the effectiveness of our approach by showing how to classify randomly generated trajectories in a multi-level arts museum layout as well as how to distinguish real world taxi trajectories in a large city.

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Section: Network Setupmentioning

confidence: 99%

Decentralized human trajectories tracking using hodge decomposition in sensor networks

Yin

Ding

et al. 2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…We can apply planarization techniques to extract a planar graph from the network connectivity graph. Such methods have been developed in the past [8], [10], [23], [27]. Any such algorithm can be used for our purpose.…”

Section: A Planar Graph For Trackingmentioning

confidence: 99%

“…Therefore, this minimal scheme is applicable without the use of locations. It is possible to obtain a planar graph without using node locations [27]. After that we can determine a consistent orientation and create a tracking form abstractly.…”

Section: B Effects Of Target Detection Errorsmentioning

confidence: 99%

Differential Forms for Target Tracking and Aggregate Queries in Distributed Networks

Sarkar

Gao

2013

IEEE/ACM Trans. Networking

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Consider mobile targets in a plane and their movements being monitored by a network such as a field of sensors. We develop distributed algorithms for in-network tracking and range queries for aggregated data (for example returning the number of targets within any user given region). Our scheme stores the target detection information locally in the network, and answers a query by examining the perimeter of the given range. The cost of updating data about mobile targets is proportional to the target displacement. The key insight is to maintain in the sensor network a function with respect to the target detection data on the graph edges that is a differential form such that the integral of this form along any closed curve C gives the integral within the region bounded by C. The differential form has great flexibility making it appropriate for tracking mobile targets. The basic range query can be used to find a nearby target or any given identifiable target with cost O(d) where d is the distance to the target in question. Dynamic insertion, deletion, coverage holes and mobility of sensor nodes can be handled with only local operations, making the scheme suitable for a highly dynamic network. It is extremely robust and capable of tolerating errors in sensing and target localization. Targets do not need to be identified for the tracking, thus user privacy can be preserved. In this article, we only elaborate the advantages of differential forms in tracking of mobile targets. Similar routines can be applied for organizing many other types of informations, for example streaming scalar sensor data (such as temperature data field), to support efficient range queries. We demonstrate through analysis and simulations that this scheme compares favorably with existing schemes that use location services for answering aggregate range queries of target detection data.

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“…There have been several papers on how to obtain a planarized network efficiently for unlocalized wireless sensor networks [7], [8]. We skip the first step here.…”

Section: A Constructing the Path Pmentioning

confidence: 99%

Sorting Based Data Centric Storage

Zhang

Jiang

Chen

2008

2008 Seventh IEEE International Symposium on Network Computing and Applications

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Data-centric storage [6], which supports efficient innetwork data query and processing, is an important concept for sensor networks. Previous approaches mostly use hash functions to store data, where data with the same key value are stored in sensors at or near the same geographic location.We propose a new data-centric storage method based on sorting. Our method is robust for different network models and works for unlocalized homogeneous sensor networks, i.e., it requires no location information. The idea is to sort the data in the network based on their key values, so that queries -including range queries -can be easily answered. The sorting method balances the storage load well. We present a sorting algorithm that is both decentralized and efficient.

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Robust Planarization of Unlocalized Wireless Sensor Networks

Cited by 12 publications

References 13 publications

Decentralized human trajectories tracking using hodge decomposition in sensor networks

Decentralized human trajectories tracking using hodge decomposition in sensor networks

Differential Forms for Target Tracking and Aggregate Queries in Distributed Networks

Sorting Based Data Centric Storage

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