Processing KNN Queries in Grid-Based Sensor Networks

Huang, Yuan Ko; Su, Chun Hsing

doi:10.1109/is3c.2014.253

Cited by 3 publications

(5 citation statements)

References 22 publications

(9 reference statements)

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“…In [2], beacon based mechanism is proposed to reduce the high energy consumption evoked by heavy traffic. Huang et al [3] present net grid based algorithm to process the k-NN query. The PCIKNN [4] algorithm allows a k-NN query to propagate on an optimal number of concurrent itineraries.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 1 shows the directional storage for the VLVs in Table 1. Sensor data with the value distance in [0, 3) i.e. VLV-A store in the nearest management node to sink.…”

Section: ) Value Based Linguistic Variables (Vlvs)mentioning

confidence: 99%

“…For example, in Figure 2(c), the set X = {x 1 , x 2 } is with the Bloom filter code array 0010010110010010. Test element y 1 hashed to position (3,8,12) is determined in the set, but actually it is a false positive. Test element y 2 hashed to position (8,13,15) is definitely not in the set X.…”

Section: B Privacy-preserving K-nn Query Schemementioning

confidence: 99%

See 2 more Smart Citations

Enable privacy preservation for k-NN query in two-tiered wireless sensor networks

Peng¹,

Zhang²,

Chen³

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. To address this problem, we propose PPKN, an efficient and privacy-preserving k-NN query protocol in two-tiered wireless sensor networks. Our proposal prevents adversaries from gaining sensitive information of both queries issued by users and data collected by sensor nodes while allows the sink to verify whether results are valid. It offers confidentiality of queries and data by constructing a special code, provides integrity verification by the correlation among data. Moreover, by the implementation of KNQ query framework, PPKN also achieves high efficient in query response and energy consumption. Finally, the theoretical analysis and experiment results show the high performance of PPKN in terms of privacy preservation and query efficiency.IEEE ICC 2015 -Ad-hoc and Sensor Networking Symposium 978-1-4673-6432-4/15/$31.00 ©2015 IEEE

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Section: Related Workmentioning

confidence: 99%

“…Figure 1 shows the directional storage for the VLVs in Table 1. Sensor data with the value distance in [0, 3) i.e. VLV-A store in the nearest management node to sink.…”

Section: ) Value Based Linguistic Variables (Vlvs)mentioning

confidence: 99%

See 1 more Smart Citation

Enable privacy preservation for k-NN query in two-tiered wireless sensor networks

Peng¹,

Zhang²,

Chen³

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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“…Many studies have proposed ways to optimize the performance of the

NN query in various environments or conditions [ 17 , 18 ]. Xie et al [ 17 ] proposed

NN query processing method in wireless and robot networks (WSRNs).…”

Section: Related Workmentioning

confidence: 99%

“…Huang at al. [ 18 ] proposed an efficient algorithm to process the

NN query for moving objects in a grid-based sensor network.…”

Section: Related Workmentioning

confidence: 99%

Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks

Han

Park

Hong

et al. 2015

Sensors

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The k-Nearest Neighbors (kNN) query is an important spatial query in mobile sensor networks. In this work we extend kNN to include a distance constraint, calling it a l-distant k-nearest-neighbors (l-kNN) query, which finds the k sensor nodes nearest to a query point that are also at l or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l- kNN query can be used in most kNN applications for the case of well distributed query results. To process an l-kNN query, we must discover all sets of kNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-kNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l- kNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting k sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l- kNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the kNN query in terms of energy efficiency, query latency, and accuracy.

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