Minimization of delay and collision with cross cube spanning tree in wireless sensor networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-05

AUTHORS

Jing Zhang, Li Xu, Pei-Wei Tsai, Zhiwei Lin

ABSTRACT

The wireless sensor network (WSN) is a system containing the event detection and the data gathering abilities. The data gathering mechanism is the fundamental but important procedure in the WSN environment. The way of the data gathering majorly affects the efficiency of WSNs on retrieving data at the sink node. It is generally known that the clustering techniques are effective to reduce the energy consumption in the WSNs. However, the research on the packet collision and the transmission delay in the Cluster based routing algorithm still remains limited. The packet loss and the transmission delay will happen more often due to collision and as such it will have negative impact on the WSN performance. In addition, the transmission delay phenomenon in the WSN may cause the inefficient result in the data gathering process. Unfortunately, it is usually neglected in the existing literature. To overcome the drawback of transmission delay and collision, a cluster-based converge cast tree (CCCT) protocol is proposed in this paper. The core of this protocol is to construct a cross cube spanning tree topology control algorithm. The proposed protocol performance is analyzed theoretically, which demonstrate that the protocol is efficient in avoiding packet collision and reducing the transmission delay. Finally, the protocol is examined by the simulations. The simulation results indicate that the proposed CCCT structure and algorithms outperform the existing approaches significantly in the realistic WSN environment. More... »

PAGES

1875-1893

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11276-017-1653-4

DOI

http://dx.doi.org/10.1007/s11276-017-1653-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100163320


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1005", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Communications Technologies", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/10", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Technology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Fujian University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.440712.4", 
          "name": [
            "School of Information Science and Engineering, Fujian University of Technology, and Fujian Provincial Key Laboratory of Big Data Mining and Applications, 350118, Fuzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Jing", 
        "id": "sg:person.015044633253.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015044633253.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fujian Normal University", 
          "id": "https://www.grid.ac/institutes/grid.411503.2", 
          "name": [
            "School of Mathematics and Computer Science, Fujian Normal University, 350007, Fuzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xu", 
        "givenName": "Li", 
        "id": "sg:person.014317676055.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014317676055.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Swinburne University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.1027.4", 
          "name": [
            "Department of Computer Science and Software Engineering, Swinburne University of Technology, 3122, Hawthorn, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tsai", 
        "givenName": "Pei-Wei", 
        "id": "sg:person.015331106167.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015331106167.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Ulster", 
          "id": "https://www.grid.ac/institutes/grid.12641.30", 
          "name": [
            "School of Computing, Ulster University, BT370QB, Jordanstown, Northern Ireland, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lin", 
        "givenName": "Zhiwei", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11276-016-1414-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001479749", 
          "https://doi.org/10.1007/s11276-016-1414-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11276-016-1414-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001479749", 
          "https://doi.org/10.1007/s11276-016-1414-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.adhoc.2011.12.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002557080"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.asoc.2014.11.063", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009645024"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1515/amcs-2015-0023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016634457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1089733.1089736", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028242823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1687-1499-2012-83", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029406646", 
          "https://doi.org/10.1186/1687-1499-2012-83"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11276-015-1132-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029854017", 
          "https://doi.org/10.1007/s11276-015-1132-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/2629658", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034574535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11277-016-3725-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043305361", 
          "https://doi.org/10.1007/s11277-016-3725-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11277-016-3725-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043305361", 
          "https://doi.org/10.1007/s11277-016-3725-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11276-015-1063-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049189192", 
          "https://doi.org/10.1007/s11276-015-1063-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/18.850663", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061101337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jsen.2010.2063020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061321407"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jsen.2013.2257023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061322594"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/jsen.2014.2377200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061323708"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/lcomm.2012.091212.121454", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061349709"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2016.2544344", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061655823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2008.74", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061753408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2010.80", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061753673"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2011.305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061753865"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2013.160", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061754262"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2014.2307871", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061754535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpds.2015.2388482", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061754802"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jnca.2017.05.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085118049"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1091770295", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-61869-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091770295", 
          "https://doi.org/10.1007/978-3-319-61869-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wcnc.2014.6952904", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093583205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wmnc.2015.29", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094232592"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wiopt.2014.6850332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094323036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/nsitnsw.2015.7176383", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095291458"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-05", 
    "datePublishedReg": "2019-05-01", 
    "description": "The wireless sensor network (WSN) is a system containing the event detection and the data gathering abilities. The data gathering mechanism is the fundamental but important procedure in the WSN environment. The way of the data gathering majorly affects the efficiency of WSNs on retrieving data at the sink node. It is generally known that the clustering techniques are effective to reduce the energy consumption in the WSNs. However, the research on the packet collision and the transmission delay in the Cluster based routing algorithm still remains limited. The packet loss and the transmission delay will happen more often due to collision and as such it will have negative impact on the WSN performance. In addition, the transmission delay phenomenon in the WSN may cause the inefficient result in the data gathering process. Unfortunately, it is usually neglected in the existing literature. To overcome the drawback of transmission delay and collision, a cluster-based converge cast tree (CCCT) protocol is proposed in this paper. The core of this protocol is to construct a cross cube spanning tree topology control algorithm. The proposed protocol performance is analyzed theoretically, which demonstrate that the protocol is efficient in avoiding packet collision and reducing the transmission delay. Finally, the protocol is examined by the simulations. The simulation results indicate that the proposed CCCT structure and algorithms outperform the existing approaches significantly in the realistic WSN environment.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11276-017-1653-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4997639", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1327893", 
        "issn": [
          "1022-0038", 
          "1572-8196"
        ], 
        "name": "Wireless Networks", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "25"
      }
    ], 
    "name": "Minimization of delay and collision with cross cube spanning tree in wireless sensor networks", 
    "pagination": "1875-1893", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "692ac9d95b0cc61adda6d8d09978b53e0b963a37a0597ecf4f7f6b82206a4fef"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11276-017-1653-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1100163320"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11276-017-1653-4", 
      "https://app.dimensions.ai/details/publication/pub.1100163320"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:18", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000368_0000000368/records_78950_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11276-017-1653-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11276-017-1653-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11276-017-1653-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11276-017-1653-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11276-017-1653-4'


 

This table displays all metadata directly associated to this object as RDF triples.

184 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11276-017-1653-4 schema:about anzsrc-for:10
2 anzsrc-for:1005
3 schema:author Nd2fbe4342a0a4103afaddd891c6063a4
4 schema:citation sg:pub.10.1007/978-3-319-61869-2
5 sg:pub.10.1007/s11276-015-1063-4
6 sg:pub.10.1007/s11276-015-1132-8
7 sg:pub.10.1007/s11276-016-1414-9
8 sg:pub.10.1007/s11277-016-3725-7
9 sg:pub.10.1186/1687-1499-2012-83
10 https://app.dimensions.ai/details/publication/pub.1091770295
11 https://doi.org/10.1016/j.adhoc.2011.12.004
12 https://doi.org/10.1016/j.asoc.2014.11.063
13 https://doi.org/10.1016/j.jnca.2017.05.001
14 https://doi.org/10.1109/18.850663
15 https://doi.org/10.1109/jsen.2010.2063020
16 https://doi.org/10.1109/jsen.2013.2257023
17 https://doi.org/10.1109/jsen.2014.2377200
18 https://doi.org/10.1109/lcomm.2012.091212.121454
19 https://doi.org/10.1109/nsitnsw.2015.7176383
20 https://doi.org/10.1109/tit.2016.2544344
21 https://doi.org/10.1109/tpds.2008.74
22 https://doi.org/10.1109/tpds.2010.80
23 https://doi.org/10.1109/tpds.2011.305
24 https://doi.org/10.1109/tpds.2013.160
25 https://doi.org/10.1109/tpds.2014.2307871
26 https://doi.org/10.1109/tpds.2015.2388482
27 https://doi.org/10.1109/wcnc.2014.6952904
28 https://doi.org/10.1109/wiopt.2014.6850332
29 https://doi.org/10.1109/wmnc.2015.29
30 https://doi.org/10.1145/1089733.1089736
31 https://doi.org/10.1145/2629658
32 https://doi.org/10.1515/amcs-2015-0023
33 schema:datePublished 2019-05
34 schema:datePublishedReg 2019-05-01
35 schema:description The wireless sensor network (WSN) is a system containing the event detection and the data gathering abilities. The data gathering mechanism is the fundamental but important procedure in the WSN environment. The way of the data gathering majorly affects the efficiency of WSNs on retrieving data at the sink node. It is generally known that the clustering techniques are effective to reduce the energy consumption in the WSNs. However, the research on the packet collision and the transmission delay in the Cluster based routing algorithm still remains limited. The packet loss and the transmission delay will happen more often due to collision and as such it will have negative impact on the WSN performance. In addition, the transmission delay phenomenon in the WSN may cause the inefficient result in the data gathering process. Unfortunately, it is usually neglected in the existing literature. To overcome the drawback of transmission delay and collision, a cluster-based converge cast tree (CCCT) protocol is proposed in this paper. The core of this protocol is to construct a cross cube spanning tree topology control algorithm. The proposed protocol performance is analyzed theoretically, which demonstrate that the protocol is efficient in avoiding packet collision and reducing the transmission delay. Finally, the protocol is examined by the simulations. The simulation results indicate that the proposed CCCT structure and algorithms outperform the existing approaches significantly in the realistic WSN environment.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree false
39 schema:isPartOf N56844fb4b6f34ed9ae3ba485815ac0c4
40 N84a8ca9bcd3048c9be94636c51f6dbd5
41 sg:journal.1327893
42 schema:name Minimization of delay and collision with cross cube spanning tree in wireless sensor networks
43 schema:pagination 1875-1893
44 schema:productId N9d6c9817af764d3fb6560af39c84fafd
45 Na98578fa9ee546d29e80647e269b4b0e
46 Ne4819860e9d54009a172253d1e29328b
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100163320
48 https://doi.org/10.1007/s11276-017-1653-4
49 schema:sdDatePublished 2019-04-11T13:18
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N3722a64969a6426b90e2a89beb7616d0
52 schema:url https://link.springer.com/10.1007%2Fs11276-017-1653-4
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N3722a64969a6426b90e2a89beb7616d0 schema:name Springer Nature - SN SciGraph project
57 rdf:type schema:Organization
58 N436140fcf7ed4c1783e4c87779a5d9a6 rdf:first sg:person.015331106167.77
59 rdf:rest Nb74a24aa23d64078ad4214754f1084f2
60 N56844fb4b6f34ed9ae3ba485815ac0c4 schema:volumeNumber 25
61 rdf:type schema:PublicationVolume
62 N84a8ca9bcd3048c9be94636c51f6dbd5 schema:issueNumber 4
63 rdf:type schema:PublicationIssue
64 N918cea8fc5584d07a59002ff65a9b19a schema:affiliation https://www.grid.ac/institutes/grid.12641.30
65 schema:familyName Lin
66 schema:givenName Zhiwei
67 rdf:type schema:Person
68 N9d6c9817af764d3fb6560af39c84fafd schema:name readcube_id
69 schema:value 692ac9d95b0cc61adda6d8d09978b53e0b963a37a0597ecf4f7f6b82206a4fef
70 rdf:type schema:PropertyValue
71 Na98578fa9ee546d29e80647e269b4b0e schema:name dimensions_id
72 schema:value pub.1100163320
73 rdf:type schema:PropertyValue
74 Nb74a24aa23d64078ad4214754f1084f2 rdf:first N918cea8fc5584d07a59002ff65a9b19a
75 rdf:rest rdf:nil
76 Nd2fbe4342a0a4103afaddd891c6063a4 rdf:first sg:person.015044633253.10
77 rdf:rest Nf9ca1eb4dbdc4ec1aeb3fb603aece6b1
78 Ne4819860e9d54009a172253d1e29328b schema:name doi
79 schema:value 10.1007/s11276-017-1653-4
80 rdf:type schema:PropertyValue
81 Nf9ca1eb4dbdc4ec1aeb3fb603aece6b1 rdf:first sg:person.014317676055.22
82 rdf:rest N436140fcf7ed4c1783e4c87779a5d9a6
83 anzsrc-for:10 schema:inDefinedTermSet anzsrc-for:
84 schema:name Technology
85 rdf:type schema:DefinedTerm
86 anzsrc-for:1005 schema:inDefinedTermSet anzsrc-for:
87 schema:name Communications Technologies
88 rdf:type schema:DefinedTerm
89 sg:grant.4997639 http://pending.schema.org/fundedItem sg:pub.10.1007/s11276-017-1653-4
90 rdf:type schema:MonetaryGrant
91 sg:journal.1327893 schema:issn 1022-0038
92 1572-8196
93 schema:name Wireless Networks
94 rdf:type schema:Periodical
95 sg:person.014317676055.22 schema:affiliation https://www.grid.ac/institutes/grid.411503.2
96 schema:familyName Xu
97 schema:givenName Li
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014317676055.22
99 rdf:type schema:Person
100 sg:person.015044633253.10 schema:affiliation https://www.grid.ac/institutes/grid.440712.4
101 schema:familyName Zhang
102 schema:givenName Jing
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015044633253.10
104 rdf:type schema:Person
105 sg:person.015331106167.77 schema:affiliation https://www.grid.ac/institutes/grid.1027.4
106 schema:familyName Tsai
107 schema:givenName Pei-Wei
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015331106167.77
109 rdf:type schema:Person
110 sg:pub.10.1007/978-3-319-61869-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091770295
111 https://doi.org/10.1007/978-3-319-61869-2
112 rdf:type schema:CreativeWork
113 sg:pub.10.1007/s11276-015-1063-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049189192
114 https://doi.org/10.1007/s11276-015-1063-4
115 rdf:type schema:CreativeWork
116 sg:pub.10.1007/s11276-015-1132-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029854017
117 https://doi.org/10.1007/s11276-015-1132-8
118 rdf:type schema:CreativeWork
119 sg:pub.10.1007/s11276-016-1414-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001479749
120 https://doi.org/10.1007/s11276-016-1414-9
121 rdf:type schema:CreativeWork
122 sg:pub.10.1007/s11277-016-3725-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043305361
123 https://doi.org/10.1007/s11277-016-3725-7
124 rdf:type schema:CreativeWork
125 sg:pub.10.1186/1687-1499-2012-83 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029406646
126 https://doi.org/10.1186/1687-1499-2012-83
127 rdf:type schema:CreativeWork
128 https://app.dimensions.ai/details/publication/pub.1091770295 schema:CreativeWork
129 https://doi.org/10.1016/j.adhoc.2011.12.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002557080
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/j.asoc.2014.11.063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009645024
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/j.jnca.2017.05.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085118049
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/18.850663 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061101337
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/jsen.2010.2063020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061321407
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/jsen.2013.2257023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061322594
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/jsen.2014.2377200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061323708
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/lcomm.2012.091212.121454 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061349709
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/nsitnsw.2015.7176383 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095291458
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1109/tit.2016.2544344 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061655823
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1109/tpds.2008.74 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061753408
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/tpds.2010.80 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061753673
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/tpds.2011.305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061753865
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/tpds.2013.160 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061754262
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/tpds.2014.2307871 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061754535
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1109/tpds.2015.2388482 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061754802
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/wcnc.2014.6952904 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093583205
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1109/wiopt.2014.6850332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094323036
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1109/wmnc.2015.29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094232592
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1145/1089733.1089736 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028242823
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1145/2629658 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034574535
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1515/amcs-2015-0023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016634457
172 rdf:type schema:CreativeWork
173 https://www.grid.ac/institutes/grid.1027.4 schema:alternateName Swinburne University of Technology
174 schema:name Department of Computer Science and Software Engineering, Swinburne University of Technology, 3122, Hawthorn, Australia
175 rdf:type schema:Organization
176 https://www.grid.ac/institutes/grid.12641.30 schema:alternateName University of Ulster
177 schema:name School of Computing, Ulster University, BT370QB, Jordanstown, Northern Ireland, UK
178 rdf:type schema:Organization
179 https://www.grid.ac/institutes/grid.411503.2 schema:alternateName Fujian Normal University
180 schema:name School of Mathematics and Computer Science, Fujian Normal University, 350007, Fuzhou, China
181 rdf:type schema:Organization
182 https://www.grid.ac/institutes/grid.440712.4 schema:alternateName Fujian University of Technology
183 schema:name School of Information Science and Engineering, Fujian University of Technology, and Fujian Provincial Key Laboratory of Big Data Mining and Applications, 350118, Fuzhou, China
184 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...