A Message Processing Method for Top-k Query for Traffic Reduction in Ad Hoc Networks

Ryo, Hagihara; Shinohara, Masako; Hara, Takahiro; Nishio, Shojiro

doi:10.1109/mdm.2009.12

Cited by 24 publications

(36 citation statements)

References 8 publications

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“…Top-query processing methods have also been proposed for mobile ad hoc networks [13][14][15]27]. The method proposed in [27] assumes that the scores of data objects follow some kind of distribution such as Gaussian distribution.…”

Section: Related Workmentioning

confidence: 99%

“…However, because such scores are dependent of query conditions in multidimensional top-queries, this assumption is impractical. To suppress unnecessary data replies, the method proposed in [14] estimates the th score of data objects in the network by attaching standard scores to query messages. The aim of the method proposed in [15] is to prune more data objects which are not included in the top-result.…”

Section: Related Workmentioning

confidence: 99%

“…, } and then constructs the filter, . After that, stores all identifiers of cluster-heads (CH ) and their corresponding next nodes (next node ) in its routing AT ← 0 (10) for ∀ ← RoutingTable.entry do (11) if .CH is not included in CL and (12) .next node is within from then (13) AT ← AT ∪ { .CH, .next node} (14) if ̸ = 0 then (15) ← ⟨org, org position, , , , , , , ⟩ (16) Send to nodes included in AT as next node (17) else (18) LeafFlag ← 1 (19) Perform Algorithm 5 (20) if then (21) Reply data whose score is not more than threshold to parent the identifiers of cluster-heads to which the query has been transmitted, so initially CL = 0 in 's query message. (If is a cluster-head, = {org}.)…”

Section: Top-query Processingmentioning

confidence: 99%

“…Perform Algorithm 5 (8) else (9) ← {∀data ∈ | data.score ≤ threshold} (10) ← ⟨ , , threshold⟩ (11) Send to parent (12) else (13) receives reply from all next nodes inlcluded in (14) ← {∀data ∈ | data.score ≤ threshold} (15) ← ⟨ , , threshold⟩ (16) Send to parent Algorithm 6: Handling reply message, .…”

Section: In Algorithm 4)mentioning

confidence: 99%

“…Top-queries retrieve only the data objects that best match the user's specified query condition (scoring function) [2][3][4]. A wide range of applications, such as multimedia retrieval in P2P networks [5][6][7][8][9], environment monitoring in wireless sensor networks [10][11][12], and, in ad hoc networks, military situations and rescue operation in disaster sites [13][14][15], realize considerable benefits from top-query processing.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Multidimensional Top-kQuery Processing in Wireless Multihop Networks

Amagata

Sasaki

Hara

et al. 2015

Mobile Information Systems

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Top-queries, which retrieve the k most preferable data objects, have been receiving much attention. An emerging challenge is to support efficient top-query processing in a wireless distributed network. In this study, we investigated how to process multidimensional top-queries efficiently in a wireless multihop network. A major challenge for multidimensional top-queries is that answers for different users are typically different, because each user has a unique preference and search range. Meanwhile, it is desirable for wireless networks to reduce unnecessary traffic even if users issue top-queries with their own unique preferences. Therefore, we address the above problem and propose a top-query processing method in wireless multihop networks, called ClusTo. ClusTo performs a novel clustering scheme for multidimensional top-query processing and routes queries based on the cluster while guaranteeing the user's specified search range. Moreover, ClusTo takes a dynamic threshold approach to suppress unnecessary query transmissions to nodes which do not contribute to top-data retrieval. Extensive experiments on both real and synthetic data have demonstrated that ClusTo outperforms existing methods in terms of traffic and delay.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Top-query Processingmentioning

confidence: 99%

Section: In Algorithm 4)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Multidimensional Top-kQuery Processing in Wireless Multihop Networks

Amagata

Sasaki

Hara

et al. 2015

Mobile Information Systems

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Towards Adaptive Distributed Top-k Query Processing

Dabringer

Eder

2016

Communications in Computer and Information Science

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ADiT is an adaptive approach for processing distributed topk queries over peer-to-peer networks optimizing both system load and query response time. This approach considers the size of the peer to peer network, the amount k of searched objects, the network capabilities of a connected peer, i.e. the transmission rate, the amount of objects stored on each peer, and the speed of a peer in processing a local topk query. In extensive experiments with a variety of scenarios we could show that ADiT outperforms state of the art distributed query processing techniques.

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