-
Notifications
You must be signed in to change notification settings - Fork 0
/
wos_database.txt
3467 lines (3402 loc) · 181 KB
/
wos_database.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
FN Clarivate Analytics Web of Science
VR 1.0
PT C
AU Binsy, MS
Sampath, N
AF Binsy, M. S.
Sampath, Nalini
BE Smys, S
Bestak, R
Chen, JIZ
Kotuliak, I
TI User Configurable and Portable Air Pollution Monitoring System for Smart
Cities Using IoT
SO INTERNATIONAL CONFERENCE ON COMPUTER NETWORKS AND COMMUNICATION
TECHNOLOGIES (ICCNCT 2018)
SE Lecture Notes on Data Engineering and Communications Technologies
LA English
DT Proceedings Paper
CT International Conference on Computer Networks and Inventive
Communication Technologies (ICCNCT)
CY APR 26-27, 2018
CL Coimbatore, INDIA
DE Air pollution monitoring; Raspberry pi 3; GPS; User configurable;
Internet of things
AB Pollution occurs at an unprecedented scale around the globe. Among various types of pollution, air pollution is a major threat to life and ecosystem as a whole. This paper presents the application of the Internet of Things to model a smart environment by developing a user-configurable air pollution monitoring device. This user configurable device monitors air pollution, collects location coordinates and send the collected data along with the location to an online Internet of Things platform called ThingSpeak. The Public can make use of the information in this platform. This device consists of sensors, Raspberry Pi 3, and GPS module. User configurability is achieved by developing a Bluetooth-based Android application thereby making the device more flexible.
C1 [Binsy, M. S.; Sampath, Nalini] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Comp Sci & Engn, Bengaluru, India.
RP Sampath, N (corresponding author), Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Comp Sci & Engn, Bengaluru, India.
NR 9
TC 1
Z9 1
U1 1
U2 5
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-4512
BN 978-981-10-8681-6; 978-981-10-8680-9
J9 LECT NOTE DATA ENG
PY 2019
VL 15
BP 345
EP 359
DI 10.1007/978-981-10-8681-6_32
PG 15
WC Computer Science, Hardware & Architecture; Engineering, Electrical &
Electronic; Telecommunications
SC Computer Science; Engineering; Telecommunications
GA BM1YG
UT WOS:000460682500031
DA 2021-12-15
ER
PT J
AU Qian, XY
Wang, XN
AF Qian, Xinyan
Wang, Xiaonan
TI Content-Centric IoT-Based Air Pollution Monitoring
SO WIRELESS PERSONAL COMMUNICATIONS
LA English
DT Article; Early Access
DE Internet of Things; Content-centric; Names; Air pollution monitoring
ID NDN-BASED IOT; INTERNET; THINGS; NETWORKS
AB The Internet of Things (IoT) has been attracting a lot of attention due to its extensive applications such as air pollution monitoring. IoT is based on end-to-end communications where each device independently delivers collected data. This leads to a lot of redundant data especially in the air pollution monitoring case where the data collected in a specific area is highly correlated. To suppress data redundancy and alleviate data delivery costs, we propose a Content-centric IoT-based Air pollution Monitoring (CIAM) system. In CIAM, the content-centric mechanism is exploited to perform air pollution data aggregation and delivery. For each type of content, a content-centric backbone is constructed so that the devices involved in the backbone can aggregate the correlated data and lower the data delivery cost and latency. CIAM is quantitatively evaluated, and the results demonstrate that CIAM alleviates the data delivery costs and latency.
C1 [Qian, Xinyan; Wang, Xiaonan] Changshu Inst Technol, Suzhou, Peoples R China.
RP Wang, XN (corresponding author), Changshu Inst Technol, Suzhou, Peoples R China.
FU CERNET Innovation Project [NGII20170106]
FX This work is supported by the CERNET Innovation Project(NGII20170106).
NR 23
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0929-6212
EI 1572-834X
J9 WIRELESS PERS COMMUN
JI Wirel. Pers. Commun.
DI 10.1007/s11277-021-09284-4
EA OCT 2021
PG 10
WC Telecommunications
SC Telecommunications
GA WO5PI
UT WOS:000712505700002
DA 2021-12-15
ER
PT C
AU Kalajdjieski, J
Stojkoska, BR
Trivodaliev, K
AF Kalajdjieski, Jovan
Stojkoska, Biljana Risteska
Trivodaliev, Kire
GP IEEE
TI IoT Based Framework for Air Pollution Monitoring in Smart Cities
SO 2020 28TH TELECOMMUNICATIONS FORUM (TELFOR)
LA English
DT Proceedings Paper
CT 28th Telecommunications Forum (TELFOR)
CY NOV 24-25, 2020
CL Belgrade, SERBIA
SP Telecommunicat Soc, Univ Belgrade, IEEE Commun Soc, IEEE Reg 8, Telekom Srbija a d, VLATACOM d o o, Univ Belgrade, Sch Elect Engn, IEEE Serbia & Montenegro COM Chapter, Nokia, Roaming Networks, VIP Mobile, Cisco, Telenor, TERI Engn, ERICSSON, Huawei, SBB, IRITEL a d, Inst Mihajlo Pupin
DE Air Pollution monitoring; Big Data; Internet of Things; Smart City
ID INTERNET; THINGS
AB Awareness of air pollution is one of the key aspects of modern smart cities. Policy makers, and other key stakeholders, are often ignorant of pollution in their immediate surrounding and its correlation to local environment and micro-climate when making short- or long-term decisions. The Internet of Things (IoT) paradigm provides a suitable general framework for monitoring air pollution as it incorporates a sensor network containing static and/or mobile sensors for measuring the different pollutants. IoT architectures, although very powerful, have a lot of issues and challenges that need to be addressed and in turn solved. In this paper a comprehensive summary of current trends in air pollution monitoring is presented. Considering the advantages of the existing solutions, a novel holistic air pollution monitoring architecture is proposed. A detailed analysis of its components is provided, including their characteristics, objectives and mechanisms to overcome the major issues and challenges.
C1 [Kalajdjieski, Jovan; Stojkoska, Biljana Risteska; Trivodaliev, Kire] Fac Comp Sci & Engn, Rugjer Boshkovikj 16, Skopje 1000, North Macedonia.
RP Kalajdjieski, J (corresponding author), Fac Comp Sci & Engn, Rugjer Boshkovikj 16, Skopje 1000, North Macedonia.
FU Faculty of Computer Science and Engineering, USCM, Skopje, Macedonia
FX This was partially financed by Faculty of Computer Science and
Engineering, USCM, Skopje, Macedonia.
NR 16
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-0-7381-4243-2
PY 2020
BP 391
EP 394
PG 4
WC Telecommunications
SC Telecommunications
GA BR7DM
UT WOS:000666945500100
DA 2021-12-15
ER
PT J
AU Kaivonen, S
Ngai, ECH
AF Kaivonen, Sami
Ngai, Edith C-H
TI Real-time air pollution monitoring with sensors on city bus
SO DIGITAL COMMUNICATIONS AND NETWORKS
LA English
DT Article
DE Smart city; Internet-of-Things; Mobile sensor network; Air pollution
monitoring
ID INTERNET; THINGS
AB This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles. The study is part of the GreenIoT project in Sweden, which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala. Through deploying low-cost wireless sensors, it is possible to obtain more fine-grained and real-time air pollution levels at different locations. The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala. The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed. Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.
C1 [Kaivonen, Sami; Ngai, Edith C-H] Uppsala Univ, Dept Informat Technol, Uppsala, Sweden.
RP Ngai, ECH (corresponding author), Uppsala Univ, Dept Informat Technol, Uppsala, Sweden.
FU VINNOVA, SwedenVinnova [2015-00347]
FX This work was supported by the GreenIoT project grant (2015-00347) from
VINNOVA, Sweden. We would also like to thank Tommy Rydbeck and his
colleagues from Gamla Uppsala Buss AB for the technical support and
collaboration, which made the experiments in this work possible.
NR 16
TC 28
Z9 28
U1 7
U2 13
PU KEAI PUBLISHING LTD
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGHENG DISTRICT 100717,
PEOPLES R CHINA
SN 2468-5925
EI 2352-8648
J9 DIGIT COMMUN NETW
JI Digit. Commun. Netw.
PD FEB
PY 2020
VL 6
IS 1
BP 23
EP 30
DI 10.1016/j.dcan.2019.03.003
PG 8
WC Telecommunications
SC Telecommunications
GA KQ3LX
UT WOS:000516829300003
OA Green Published, gold
DA 2021-12-15
ER
PT C
AU Habibi, R
Alesheikh, AA
AF Habibi, R.
Alesheikh, A. A.
BE Arefi, H
Motagh, M
TI INCORPOARATION OF GEOSENSOR NETWORKS INTO INTERNET OF THINGS FOR
ENVIRONMENTAL MONITORING
SO INTERNATIONAL CONFERENCE ON SENSORS & MODELS IN REMOTE SENSING &
PHOTOGRAMMETRY
SE International Archives of the Photogrammetry Remote Sensing and Spatial
Information Sciences
LA English
DT Proceedings Paper
CT International Conference on Sensors and Models in Remote Sensing and
Photogrammetry
CY NOV 23-25, 2015
CL Kish Island, IRAN
DE geosensor networks; Internet of Things; SWE; sensor; environmental
monitoring; air pollution
AB Thanks to the recent advances of miniaturization and the falling costs for sensors and also communication technologies, Internet specially, the number of internet-connected things growth tremendously. Moreover, geosensors with capability of generating high spatial and temporal resolution data, measuring a vast diversity of environmental data and automated operations provide powerful abilities to environmental monitoring tasks. Geosensor nodes are intuitively heterogeneous in terms of the hardware capabilities and communication protocols to take part in the Internet of Things scenarios. Therefore, ensuring interoperability is an important step. With this respect, the focus of this paper is particularly on incorporation of geosensor networks into Internet of things through an architecture for monitoring real-time environmental data with use of OGC Sensor Web Enablement standards. This approach and its applicability is discussed in the context of an air pollution monitoring scenario.
C1 [Habibi, R.; Alesheikh, A. A.] KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Tehran, Iran.
RP Habibi, R (corresponding author), KN Toosi Univ Technol, Fac Geodesy & Geomat Engn, Tehran, Iran.
RI Alesheikh, Ali Asghar/Z-4780-2019; Alesheikh, Ali Asghar/AAR-5464-2021
OI Alesheikh, Ali Asghar/0000-0001-9537-9401
NR 11
TC 1
Z9 2
U1 0
U2 1
PU COPERNICUS GESELLSCHAFT MBH
PI GOTTINGEN
PA BAHNHOFSALLE 1E, GOTTINGEN, 37081, GERMANY
SN 2194-9034
J9 INT ARCH PHOTOGRAMM
PY 2015
VL 41
IS W5
BP 259
EP 261
DI 10.5194/isprsarchives-XL-1-W5-259-2015
PG 3
WC Geography, Physical; Remote Sensing; Imaging Science & Photographic
Technology
SC Physical Geography; Remote Sensing; Imaging Science & Photographic
Technology
GA BF4CQ
UT WOS:000380618200046
OA Green Submitted, gold
DA 2021-12-15
ER
PT J
AU Dhingra, S
Madda, RB
Gandomi, AH
Patan, R
Daneshmand, M
AF Dhingra, Swati
Madda, Rajasekhara Babu
Gandomi, Amir H.
Patan, Rizwan
Daneshmand, Mahmoud
TI Internet of Things Mobile-Air Pollution Monitoring System (IoT-Mobair)
SO IEEE INTERNET OF THINGS JOURNAL
LA English
DT Article
DE Air pollution monitoring system; air quality index (AQI); air-pollution
safe route; Android; cloud; distributed systems; global positioning
system (GPS); sensors
ID EXPOSURE
AB Internet of Things (IoT) is a worldwide system of "smart devices" that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.
C1 [Dhingra, Swati; Madda, Rajasekhara Babu] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore 632014, Tamil Nadu, India.
[Gandomi, Amir H.; Daneshmand, Mahmoud] Stevens Inst Technol, Business Intelligence & Analyt, Hoboken, NJ 07030 USA.
[Patan, Rizwan] Galgotias Univ, Sch Comp Sci & Engn, Greater Noida 201307, India.
RP Gandomi, AH (corresponding author), Stevens Inst Technol, Business Intelligence & Analyt, Hoboken, NJ 07030 USA.
RI Gandomi, Amir H/J-7595-2013; PATAN, RIZWAN/C-4451-2017
OI Gandomi, Amir H/0000-0002-2798-0104; PATAN, RIZWAN/0000-0003-4878-1988
NR 24
TC 54
Z9 56
U1 5
U2 26
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2327-4662
J9 IEEE INTERNET THINGS
JI IEEE Internet Things J.
PD JUN
PY 2019
VL 6
IS 3
BP 5577
EP 5584
DI 10.1109/JIOT.2019.2903821
PG 8
WC Computer Science, Information Systems; Engineering, Electrical &
Electronic; Telecommunications
SC Computer Science; Engineering; Telecommunications
GA IE8AL
UT WOS:000472596200135
DA 2021-12-15
ER
PT C
AU Korunoski, M
Stojkoska, BR
Trivodaliev, K
AF Korunoski, Mladen
Stojkoska, Biljana Risteska
Trivodaliev, Kire
BE Dumnic, B
Delimar, M
Stefanovic, C
TI Internet of Things Solution for Intelligent Air Pollution Prediction and
Visualization
SO PROCEEDINGS OF 18TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES (IEEE
EUROCON 2019)
LA English
DT Proceedings Paper
CT 18th IEEE International Conference on Smart Technologies (IEEE EUROCON)
CY JUL 01-04, 2019
CL Novi Sad, SERBIA
SP IEEE, IEEE Reg 8, IEEE Serbia & Montenegro Sect, Univ Neoplantensis
DE Intelligent System; Air Pollution Monitoring; Air Pollution Prediction;
Web System
AB Air pollution monitoring and control is becoming a key priority in urban areas due to its substantial effect on human morbidity and mortality. This paper presents a system architecture for intelligent pollution visualization and future pollution prediction by encompassing pollution measurements and meteorological parameters. First, a pollution model using spatial interpolation is built. By adding meteorological parameters this model is further used to identify the pollution field evolution and the position of potential sources of air pollution. Using deep learning techniques, the system provides predictions for future pollution levels as well as times to reaching alarming thresholds. The whole system is encompassed in a fast, easy to use web service and a client that visually renders the system responses. The system is built and tested on data for the city of Skopje. Although the spatial resolution of the system data is low, the results are satisfactory and promising. Since the system can be seamlessly deployed on an Internet of Things sensing architecture, the improved data spatial resolution will improve performance.
C1 [Korunoski, Mladen; Stojkoska, Biljana Risteska; Trivodaliev, Kire] Ss Cyril & Methodius Univ, Fac Comp Sci & Engn, Skopje 1000, North Macedonia.
RP Korunoski, M (corresponding author), Ss Cyril & Methodius Univ, Fac Comp Sci & Engn, Skopje 1000, North Macedonia.
FU Faculty of Computer Science and Engineering, University Ss. Cyril and
Methodius, Skopje
FX This work was partially financed by the Faculty of Computer Science and
Engineering, University Ss. Cyril and Methodius, Skopje.
NR 22
TC 1
Z9 1
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-9301-8
PY 2019
PG 6
WC Engineering, Electrical & Electronic
SC Engineering
GA BP5KJ
UT WOS:000556109600030
DA 2021-12-15
ER
PT C
AU Ayele, TW
Mehta, R
AF Ayele, Temesegan Walelign
Mehta, Rutvik
GP IEEE
TI Air pollution monitoring and prediction using IoT
SO PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE
COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT)
LA English
DT Proceedings Paper
CT International Conference on Inventive Communication and Computational
Technologies (ICICCT)
CY APR 20-21, 2018
CL Coimbatore, INDIA
SP IEEE, Ranganathan Engn Coll
DE Deep Learning; IoT; LSTM; Real time; RNN
AB The internet of Things (IoT) is a course of action interrelated computing devices, mechanical and advanced machines, objects, or people that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication In this work an IoT based air pollution monitoring and prediction system is proposed. This system can be utilized for monitoring air pollutants of a particular area and to air quality analysis as well as forecasting the air quality. The proposed system will focus on the monitoring of air pollutants focus with the combination of IoT with a machine learning algorithm called Recurrent Neural Network more specifically Long Short Term Memory (LSTM).
C1 [Ayele, Temesegan Walelign; Mehta, Rutvik] Parul Univ, PIET, Dept Informat Technol, Vadodara, India.
RP Ayele, TW (corresponding author), Parul Univ, PIET, Dept Informat Technol, Vadodara, India.
NR 14
TC 8
Z9 8
U1 0
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-1974-2
PY 2018
BP 1741
EP 1745
PG 5
WC Computer Science, Theory & Methods; Engineering, Electrical &
Electronic; Telecommunications
SC Computer Science; Engineering; Telecommunications
GA BL8FP
UT WOS:000456251700346
DA 2021-12-15
ER
PT C
AU Maurya, S
Sharma, S
Yadav, P
AF Maurya, Sweta
Sharma, Shilpi
Yadav, Pranay
GP IEEE
TI Internet of Things based Air Pollution Penetrating System using GSM and
GPRS
SO 2018 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATION AND
TELECOMMUNICATION (ICACAT)
LA English
DT Proceedings Paper
CT International Conference on Advanced Computation and Telecommunication
(ICACAT)
CY DEC 28-29, 2018
CL Bhopal, INDIA
SP IEEE MP Sub Sect, IEEE Bombay Sect, Laxmi Narain Coll Technol Grp Coll, Natl Board Accrediat
DE Real-time monitoring; Air pollution; RF; Sensor; Wireless Sensor network
and Cloud Servers
AB In the last decade, level of pollution is indomitable in urban area. Due to this, the quality degrades day by day. Due to this researchers are focused on air pollution monitoring unit with the help of wireless sensor network. These sensor networks give the information of pollution level of the centralized server using internet or telecom network. This paper proposed a reliable and low cost air pollution monitoring system for developing countries. Most of the developing nations where they don't have fourth generation high speed communication network but they require Air pollution monitoring system with the help of proposed monitoring system they could measure the level carbon mono oxide (Co) and other pollution gas level in the form PPM. The proposed system is based on internet of things, global position system (GPS) and general packet radio servers (GPRS). Sensor collection data in the analog form and send microcontroller unit that is converted into an analog information in digital form and send this digital data to cloud server using GPRS system and store the data on cloud servers and then process this data. After the processing of this collected sensor data represented on http link that is based IP address and also create an APK file App presentation of this data. The proposed pollution measurement system shows low cost and better reliability as compared to other measurement devices that is shown in simulation and result.
C1 [Maurya, Sweta; Sharma, Shilpi] RGPV, BIT, Dept Elect & Commun, Bhopal, MP, India.
[Yadav, Pranay] ULT, Dept Res & Dev, Bhopal, MP, India.
RP Maurya, S (corresponding author), RGPV, BIT, Dept Elect & Commun, Bhopal, MP, India.
RI Yadav, Pranay/R-6751-2017
OI Yadav, Pranay/0000-0002-9368-8398
NR 8
TC 0
Z9 0
U1 0
U2 0
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-5472-9
PY 2018
PG 5
WC Computer Science, Theory & Methods; Telecommunications
SC Computer Science; Telecommunications
GA BO7YH
UT WOS:000526067100105
DA 2021-12-15
ER
PT C
AU Cao, QH
Khan, I
Farahbakhsh, R
Madhusudan, G
Lee, GM
Crespi, N
AF Cao, Quyet H.
Khan, Imran
Farahbakhsh, Reza
Madhusudan, Giyyarpuram
Lee, Gyu Myoung
Crespi, Noel
GP IEEE
TI A Trust Model for Data Sharing in Smart Cities
SO 2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC)
SE IEEE International Conference on Communications
LA English
DT Proceedings Paper
CT IEEE International Conference on Communications (ICC)
CY MAY 23-27, 2016
CL Kuala Lumpur, MALAYSIA
SP IEEE, IEEE Commun Soc
DE Internet of Things; Smart Cities; Trust-based Data Sharing; Data Usage
Control; Defeasible Reasoning; and Air Pollution Monitoring
ID SEMANTIC SENSOR; INTERNET; THINGS; WEB
AB The data generated by the devices and existing infrastructure in the Internet of Things (IoT) should be shared among applications. However, data sharing in the IoT can only reach its full potential when multiple participants contribute their data, for example when people are able to use their smartphone sensors for this purpose. We believe that each step, from sensing the data to the actionable knowledge, requires trust-enabled mechanisms to facilitate data exchange, such as data perception trust, trustworthy data mining, and reasoning with trust related policies. The absence of trust could affect the acceptance of sharing data in smart cities. In this study, we focus on data usage transparency and accountability and propose a trust model for data sharing in smart cities, including system architecture for trust-based data sharing, data semantic and abstraction models, and a mechanism to enhance transparency and accountability for data usage. We apply semantic technology and defeasible reasoning with trust data usage policies. We built a prototype based on an air pollution monitoring use case and utilized it to evaluate the performance of our solution.
C1 [Cao, Quyet H.; Madhusudan, Giyyarpuram] Orange Labs, Paris, France.
[Khan, Imran] Schneider Elect Ind SAS, 38TEC, F-38050 Grenoble 9, France.
[Cao, Quyet H.; Farahbakhsh, Reza; Crespi, Noel] Inst Mines Telecom, Telecom SudParis, CNRS, UMR 5157, Paris, France.
[Lee, Gyu Myoung] Liverpool John Moores Univ, Dept Comp Sci, Liverpool L3 5UX, Merseyside, England.
RP Cao, QH (corresponding author), Orange Labs, Paris, France.; Cao, QH (corresponding author), Inst Mines Telecom, Telecom SudParis, CNRS, UMR 5157, Paris, France.
RI Crespi, Noel/ABE-7052-2020
OI Crespi, Noel/0000-0003-2962-192X
NR 21
TC 14
Z9 15
U1 1
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1550-3607
BN 978-1-4799-6664-6
J9 IEEE ICC
PY 2016
DI 10.1109/ICC.2016.7510834
PG 7
WC Engineering, Electrical & Electronic; Telecommunications
SC Engineering; Telecommunications
GA BG6YK
UT WOS:000390993201030
OA Green Submitted
DA 2021-12-15
ER
PT C
AU Binsy, MS
Sampath, N
AF Binsy, M. S.
Sampath, Nalini
BE Hemanth, J
Fernando, X
Lafata, P
Baig, Z
TI Self Configurable Air Pollution Monitoring System Using IoT and Data
Mining Techniques
SO INTERNATIONAL CONFERENCE ON INTELLIGENT DATA COMMUNICATION TECHNOLOGIES
AND INTERNET OF THINGS, ICICI 2018
SE Lecture Notes on Data Engineering and Communications Technologies
LA English
DT Proceedings Paper
CT International Conference on Intelligent Data Communication Technologies
and Internet of Things (ICICI)
CY AUG 07-08, 2018
CL Coimbatore, INDIA
DE Air pollution prediction; Data mining; Self-configurable; Regression;
Internet of things
AB Air pollution is a perilous threat to living organisms and the whole ecosystem. The purpose of this paper is to develop a self-configurable air pollution monitoring system which can monitor and predict air pollution by applying Internet of Things (IoT) and data mining technologies. Self-configurability of the device is the ability to regulate the frequency of monitoring based on pollutant predictions. Monitoring is done using the system developed in paper [1] which collects concentration of pollutants such as Carbon monoxide, harmful gases, dust level, meteorological parameters such as temperature along with GPS location. This paper deals with using the monitored data for prediction along with humidity information. Data mining technique, Regression is used to predict the level of pollutant. These predicted values in turn decides the mode of operation of the device. The monitored data send to ThingSpeak are further analyzed using MATLAB. Map of the location is updated using red and green markers based on the level of pollution. These data along with predicted pollutant levels in ThingSpeak can be viewed by the public.
C1 [Binsy, M. S.; Sampath, Nalini] Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Engn, Bengaluru, India.
RP Sampath, N (corresponding author), Amrita Vishwa Vidyapeetham, Dept Comp Sci & Engn, Amrita Sch Engn, Bengaluru, India.
NR 11
TC 0
Z9 0
U1 0
U2 0
PU SPRINGER INTERNATIONAL PUBLISHING AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 2367-4512
BN 978-3-030-03146-6; 978-3-030-03145-9
J9 LECT NOTE DATA ENG
PY 2019
VL 26
BP 786
EP 798
DI 10.1007/978-3-030-03146-6_90
PG 13
WC Telecommunications
SC Telecommunications
GA BR9CJ
UT WOS:000674929900090
DA 2021-12-15
ER
PT C
AU Zimos, E
Mota, JFC
Rodrigues, MRD
Deligiannis, N
AF Zimos, Evangelos
Mota, Joao F. C.
Rodrigues, Miguel R. D.
Deligiannis, Nikos
GP IEEE
TI Internet-of-Things Data Aggregation Using Compressed Sensing with Side
Information
SO 2016 23RD INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT)
LA English
DT Proceedings Paper
CT 23rd International Conference on Telecommunications (ICT)
CY MAY 16-18, 2016
CL Thessaloniki, GREECE
ID SIGNAL RECOVERY
AB The Internet-of-Things (IoT) is the key enabling technology for transforming current urban environments into so-called Smart Cities. One of the goals behind making cities smarter is to provide a healthy environment that improves the citizens' quality of life and wellbeing. In this work, we introduce a novel data aggregation mechanism tailored to the application of large-scale air pollution monitoring with IoT devices. Our design exploits the intra-and inter-source correlations among air-pollution data using the framework of compressed sensing with side information. The proposed method delivers significant improvements in the data reconstruction quality with respect to the state of the art, even in the presence of noise when measuring and transmitting the data.
C1 [Zimos, Evangelos; Deligiannis, Nikos] Vrije Univ Brussel, Dept Elect & Informat, Pl Laan 2, B-1050 Brussels, Belgium.
[Mota, Joao F. C.; Rodrigues, Miguel R. D.] UCL, Dept Elect & Elect Engn, London WC1E 7JE, England.
[Zimos, Evangelos; Deligiannis, Nikos] iMinds, Gaston Crommenlaan 8 b102, B-9050 Ghent, Belgium.
RP Zimos, E (corresponding author), Vrije Univ Brussel, Dept Elect & Informat, Pl Laan 2, B-1050 Brussels, Belgium.; Zimos, E (corresponding author), iMinds, Gaston Crommenlaan 8 b102, B-9050 Ghent, Belgium.
RI Mota, Joao F. C./AAK-8182-2021; Deligiannis, Nikolaos/ABH-2381-2020
OI Mota, Joao F. C./0000-0001-7263-8255; Deligiannis,
Nikolaos/0000-0001-9300-5860
NR 30
TC 1
Z9 1
U1 0
U2 4
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-1990-8
PY 2016
DI 10.1109/ICT.2016.7500418
PG 5
WC Engineering, Electrical & Electronic; Telecommunications
SC Engineering; Telecommunications
GA BG1KE
UT WOS:000386851600076
OA Green Submitted
DA 2021-12-15
ER
PT C
AU Parmar, G
Lakhani, S
Chattopadhyay, MK
AF Parmar, Gagan
Lakhani, Sagar
Chattopadhyay, Manju K.
GP IEEE
TI An IoT Based Low Cost Air Pollution Monitoring System
SO 2017 INTERNATIONAL CONFERENCE ON RECENT INNOVATIONS IN SIGNAL PROCESSING
AND EMBEDDED SYSTEMS (RISE)
LA English
DT Proceedings Paper
CT International Conference on Recent Innovations in Signal processing and
Embedded Systems (RISE)
CY NOV 27-29, 2017
CL Maulana Azad Nat Inst Technol, Dept Electron & Communicat Engn, Bhopal,
INDIA
SP IEEE, ESIEE
HO Maulana Azad Nat Inst Technol, Dept Electron & Communicat Engn
DE Internet of Things; Pollution Monitoring; ARM Microcontroller; Nucleo
F401RE; Raspberry pi-3; MEAN Stack
AB A prototype for an Environmental Air Pollution Monitoring System for monitoring the concentrations of major air pollutant gases has been developed. The system uses low cost air-quality monitoring nodes comprises of low cost semiconductor gas sensor with Wi-Fi modules. This system measures concentrations of gases such as CO, CO2, SO2 and NO2 using semiconductor sensors. The sensors will gather the data of various environmental parameters and provide it to raspberry pi which act as a base station. Realization of data gathered by sensors is displayed on Raspberry pi 3 based Webserver. A MEAN stack is developed to display data over website. The fundamental aspect of proposed work is to provide low cost infrastructure to enable the data collection and dissemination to all stakeholders.
C1 [Parmar, Gagan; Lakhani, Sagar; Chattopadhyay, Manju K.] Devi Ahilya Univ, Sch Elect, Indore, Madhya Pradesh, India.
RP Parmar, G (corresponding author), Devi Ahilya Univ, Sch Elect, Indore, Madhya Pradesh, India.
NR 16
TC 10
Z9 10
U1 4
U2 8
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5090-4760-4
PY 2017
BP 524
EP 528
PG 5
WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic
SC Computer Science; Engineering
GA BL7GU
UT WOS:000455016600097
DA 2021-12-15
ER
PT J
AU Metia, S
Nguyen, HAD
Ha, QP
AF Metia, Santanu
Nguyen, Huynh A. D.
Ha, Quang Phuc
TI IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with
Extended Fractional-Order Kalman Filtering
SO SENSORS
LA English
DT Article
DE extended fractional-order kalman filter; internet of things; air quality
ID IDENTIFICATION
AB This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019-2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.
C1 [Metia, Santanu; Nguyen, Huynh A. D.; Ha, Quang Phuc] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia.
[Nguyen, Huynh A. D.] Can Tho Univ, Coll Engn Technol, Can Tho 900000, Vietnam.
RP Ha, QP (corresponding author), Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia.
RI Liu, Hai-Ying/P-5557-2014; Metia, Santanu/AAY-7588-2021; Ha,
Quang/F-8077-2017
OI Liu, Hai-Ying/0000-0001-8667-3465; Ha, Quang/0000-0003-0978-1758;
Nguyen, Huynh Anh Duy/0000-0002-9315-0176; Metia,
Santanu/0000-0002-5676-1146
FU Institute for Sustainable Futures (ISF) [PRO19-7375]
FX This research was funded, in part, by the Institute for Sustainable
Futures (ISF) grant number PRO19-7375.
NR 36
TC 0
Z9 0
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 1424-8220
J9 SENSORS-BASEL
JI Sensors
PD AUG
PY 2021
VL 21
IS 16
AR 5313
DI 10.3390/s21165313
PG 16
WC Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments
& Instrumentation
SC Chemistry; Engineering; Instruments & Instrumentation
GA UH9GE
UT WOS:000690228800001
PM 34450755
OA Green Published, gold
DA 2021-12-15
ER
PT C
AU Yang, F
He, JY
An, WP
Flanagan, C
MacNamee, C
McGrath, S
AF Yang Feng
He Junyi
An WeiPeng
Flanagan, Colin
MacNamee, Ciaran
McGrath, Sean
GP IEEE
TI API Monitor based on Internet of Things technology
SO 2018 12TH INTERNATIONAL CONFERENCE ON SENSING TECHNOLOGY (ICST)
SE International Conference on Sensing Technology
LA English
DT Proceedings Paper
CT 12th International Conference on Sensing Technology (ICST)
CY DEC 04-06, 2018
CL Univ Limerick, Limerick, IRELAND
SP Univ Limerick, Dept Elect & Comp Engn
HO Univ Limerick
DE IoT; Air Pollution; LPWAN; Sigfox
AB In many countries, due to the exhaustion of industrial production and the exhaust emissions of a large number of fuel vehicles, air pollution problems are also increasing, which has a negative impact on the ecological environment and people's health. Reducing the impact of air pollution on people and improving air quality are inseparable from the monitoring of air. The following problems are common in previous air monitoring systems: large energy consumption, short-life, limited communication distance, and high maintenance cost. In this paper, an air pollution index (API) monitoring system was built using the Internet of Things technology and Sigfox low-power wide area network (LPWAN) communication technology to solve the above problems, and this air pollution monitoring system was tested at the University of Limerick.
C1 [Yang Feng; He Junyi; An WeiPeng] Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo, Peoples R China.
[Flanagan, Colin; MacNamee, Ciaran; McGrath, Sean] Univ Limerick, Dept Elect & Comp Engn, Limerick, Ireland.
RP Yang, F (corresponding author), Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo, Peoples R China.
FU University Key Applied Research Program Fund [13150051]; UL(University
of Limerick) IoT 2018 summer project; [6609007023]
FX This work supported by University Key Applied Research Program Fund
(13150051), University doctoral fund (6609007023) and UL(University of
Limerick) IoT 2018 summer project. The authors with to thank the
reviewers for the careful review and credible suggestions, as well as
others who have helped to complete this paper.
NR 6
TC 1
Z9 1
U1 2
U2 2
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2156-8065
BN 978-1-5386-5147-6
J9 I CONF SENS TECHNOL
PY 2018
BP 213
EP 216
PG 4
WC Engineering, Electrical & Electronic; Remote Sensing
SC Engineering; Remote Sensing
GA BM0LU
UT WOS:000458872800039
DA 2021-12-15
ER
PT C
AU Chen, XJ
Liu, XP
Xu, P
AF Chen Xiaojun
Liu Xianpeng
Xu Peng
GP IEEE
TI IOT- Based Air Pollution Monitoring and Forecasting System
SO 2015 INTERNATIONAL CONFERENCE ON COMPUTER AND COMPUTATIONAL SCIENCES
(ICCCS)
LA English
DT Proceedings Paper
CT Computer and Computational Sciences (ICCCS)
CY JAN 27-29, 2015
CL Noida, INDIA
SP IEEE, ICCCS, ITS Engn Coll, ITS Engn Coll, Comp Sci & Engn Dept
DE Neural Network; Air Quality Monitoring; Air Pollution Forecast
AB Using empirical analysis, conventional air automatic monitoring system has high precision, but large bulk, high cost, and single datum class make it impossible for large-scale installation. Based on intriducing Internet of Things(IOT) into the field of environmental protection, this paper puts forward a kind of real-time air pollution monitoring and forecasting system. By using IOT, this system can reduce the hardware cost into 1/10 as before. The system can be laid out in a large number in monitoring area to form monitoring sensor network. Besides the functions of conventional air automatic monitoring system, it also exhibits the function of forecasting development trend of air pollution within a certain time range by analyzing the data obtained by front-end perception system according to neural network technology. Targeted emergency disposal measures can be taken to minimize losses in practical application.
C1 [Chen Xiaojun; Liu Xianpeng; Xu Peng] Jiangsu City Vocat Coll, Electromech Dept, Nantong Campus,169 Zhongyuan Rd, Nantong, Jiangsu, Peoples R China.
RP Chen, XJ (corresponding author), Jiangsu City Vocat Coll, Electromech Dept, Nantong Campus,169 Zhongyuan Rd, Nantong, Jiangsu, Peoples R China.
NR 16
TC 31
Z9 31
U1 2
U2 6
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-4799-1819-5
PY 2015
BP 257
EP 260
PG 4
WC Computer Science, Interdisciplinary Applications; Engineering,
Electrical & Electronic
SC Computer Science; Engineering
GA BF3CY
UT WOS:000380528400045
DA 2021-12-15
ER
PT C
AU Gupta, K
Rakesh, N
AF Gupta, Karan
Rakesh, Nitin
GP IEEE
TI IoT Based Automobile Air Pollution Monitoring System
SO PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE CONFLUENCE 2018 ON CLOUD
COMPUTING, DATA SCIENCE AND ENGINEERING
LA English
DT Proceedings Paper
CT 8th IEEE International Conference on Cloud Computing, Data Science and
Engineering (Confluence) / Global Technology, Innovation and
Enterpreneurship Summit
CY JAN 11-12, 2018
CL Amity Sch Engn & Technol, Noida, INDIA
SP IEEE UP Sect, IEEE, Amity Univ, Amity Sch Engn & Technol, Dept Comp Sci & Engn, Amity Univ, Dell EMC
HO Amity Sch Engn & Technol
DE Clean Automobile model; IoT (Internet of Things); ADC (Analog to Digital
Converter); Raspberry pi; Arduino
AB The pollution due to the vehicles has sky-rocketed several risks not only for the organisms but has also disturbed the balance of the environment. In the past decade, the number of vehicles has increased enormously due to which vehicular pollution has only increased. hi most of the cases, the driver does not have the information related to the emission of the vehicle thus making it difficult for the driver to take the particular actions to get the vehicle repaired. So, in order to control the vehicular pollution, the 'Automobile Air Pollution Monitoring' model can be used. The model will help in detecting the vehicles which emit the pollution greater than the standard limit. Moreover, the data will help in analyzing what sort of vehicle type are the major contributor of the pollution and thus making proper strategies to tackle such vehicle problems.
C1 [Gupta, Karan; Rakesh, Nitin] Amity Univ, ASET, Dept Comp Sci & Engn, Noida, Uttar Pradesh, India.
RP Gupta, K (corresponding author), Amity Univ, ASET, Dept Comp Sci & Engn, Noida, Uttar Pradesh, India.
NR 24
TC 1
Z9 1
U1 1
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
BN 978-1-5386-1719-9
PY 2018
BP 503
EP 508
PG 6
WC Computer Science, Information Systems; Computer Science, Theory &
Methods
SC Computer Science
GA BP9ZB
UT WOS:000571162600089
DA 2021-12-15
ER
PT J
AU Idrees, Z
Zheng, LR
AF Idrees, Zeba
Zheng, Lirong
TI Low cost air pollution monitoring systems: A review of protocols and
enabling technologies
SO JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
LA English
DT Review
DE Pollution monitoring; Electrochemical gas sensors; IoT; Low cost ambient
sensors; Particle matter sensors; Sensor calibration
ID GAS SENSORS; PERFORMANCE; PLATFORM; NETWORK; DESIGN
AB Air pollution is the foremost concern especially in modern cities, because of its noteworthy influences on the community health, and the global economy. The significance of the air quality statistics makes the highly accurate real-time monitoring systems vital. The partial data access, high cost and the non-scalability of conventional air monitoring system enforce the researchers to develop future air pollution monitoring system employing advance technologies such as internet of things (IoT), wireless sensor network (WSN) and the low-cost ambient sensors. This paper presents a short but comprehensive review of these air pollution monitoring systems (APMS), their enabling technologies and protocols. We categorize the recent work into two main classes as static and mobile air monitoring systems based on the carriers of the sensing node and the deployment strategies. Sub categories include, portable monitoring devices, community supported approaches, WSN based systems, and IoT supported approaches. Broad performance assessments and the comparison among these categories were performed with respect to their architecture and the incorporated tools. Furthermore, the present study explored the issues, groundwork, and the methodology of designing a real-time air pollution monitoring system. Finally, the review deliberated the limits of the existing works and summaries the objectives that need to attain in the future air monitoring systems to make them more accurate and realistic.
C1 [Idrees, Zeba; Zheng, Lirong] Fudan Univ, Sch Informat Sci & Engn, Shanghai, Peoples R China.
[Idrees, Zeba] Univ Engn & Technol Lahore, Lahore, Pakistan.
RP Idrees, Z (corresponding author), Fudan Univ, Sch Informat Sci & Engn, Shanghai, Peoples R China.
RI Idrees, Zeba/ABE-2251-2020; ZHENG, Lirong/ABF-9274-2020
OI ZHENG, Lirong/0000-0001-9588-0239
NR 78
TC 19
Z9 19
U1 13
U2 30
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 2467-964X
EI 2452-414X
J9 J IND INF INTEGR
JI J. Ind. Inf. Integr.
PD MAR
PY 2020
VL 17
AR 100123
DI 10.1016/j.jii.2019.100123
PG 10
WC Computer Science, Interdisciplinary Applications; Engineering,
Industrial
SC Computer Science; Engineering
GA KU2YL
UT WOS:000519573700005
DA 2021-12-15
ER
PT C
AU Zimos, E
Mota, JFC
Tsiligianni, E
Rodrigues, MRD
Deligiannis, N
AF Zimos, Evangelos
Mota, Joao F. C.
Tsiligianni, Evaggelia
Rodrigues, Miguel R. D.
Deligiannis, Nikos
GP IEEE
TI Data Aggregation and Recovery for the Internet of Things: A Compressive
Demixing Approach
SO 2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
SE IEEE Wireless Communications and Networking Conference
LA English
DT Proceedings Paper
CT IEEE Wireless Communications and Networking Conference (WCNC)
CY APR 15-18, 2018
CL Barcelona, SPAIN
SP IEEE, IEEE Commun Soc, Natl Instruments, Rohde & Schwarz, Huawei, InterDigital, NEC
DE Compressive demixing; wireless sensor networks; Internet of things;
air-pollution monitoring; smart cities
ID WIRELESS SENSOR NETWORKS; GEOMETRY; SEPARATION
AB Large-scale wireless sensor networks (WSNs) and Internet-of-Things (IoT) applications involve diverse sensing devices collecting and transmitting massive amounts of heterogeneous data. In this paper, we propose a novel compressive data aggregation and recovery mechanism that reduces the global communication cost without introducing computational overhead at the network nodes. Following the principles of compressive demixing, each node of the network collects measurement readings from multiple sources and mixes them with readings from other nodes into a single low-dimensional measurement vector, which is then relayed to other nodes; the constituent signals are recovered at the sink using convex optimization. Our design achieves significant reduction in the overall network data rates compared to prior schemes based on (distributed) compressed sensing or compressed sensing with (multiple) side information. Experiments using real large-scale air-quality data demonstrate the superior performance of the proposed framework against state-of-the-art solutions, with and without the presence of measurement and transmission noise.
C1 [Zimos, Evangelos; Tsiligianni, Evaggelia; Deligiannis, Nikos] Vrije Univ Brussel, Dept Elect & Informat, Brussels, Belgium.
[Zimos, Evangelos; Tsiligianni, Evaggelia; Deligiannis, Nikos] IMEC, Kapeldreef 75, B-3001 Leuven, Belgium.
[Mota, Joao F. C.] Heriot Watt Univ, Inst Sensors Signals & Syst, Edinburgh, Midlothian, Scotland.
[Rodrigues, Miguel R. D.] UCL, Dept Elect & Elect Engn, London, England.
RP Zimos, E (corresponding author), Vrije Univ Brussel, Dept Elect & Informat, Brussels, Belgium.; Zimos, E (corresponding author), IMEC, Kapeldreef 75, B-3001 Leuven, Belgium.
RI Deligiannis, Nikolaos/ABH-2381-2020; Mota, Joao F. C./AAK-8182-2021
OI Deligiannis, Nikolaos/0000-0001-9300-5860; Mota, Joao F.
C./0000-0001-7263-8255
FU Fonds Wetenschappelijk OnderzoekFWO [G0A2617]; VUB-Duke-UCL-UGent
International Joint Research Group on Big Data
FX We acknowledge the support of the Fonds Wetenschappelijk Onderzoek
(project no. G0A2617) and the VUB-Duke-UCL-UGent International Joint
Research Group on Big Data.
NR 27
TC 0
Z9 0
U1 0
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 1525-3511
BN 978-1-5386-1734-2
J9 IEEE WCNC
PY 2018
PG 6
WC Computer Science, Hardware & Architecture; Computer Science, Theory &
Methods; Engineering, Electrical & Electronic; Telecommunications
SC Computer Science; Engineering; Telecommunications
GA BK3SU
UT WOS:000435542401084
DA 2021-12-15
ER
PT C
AU Jovanovska, EM
Davcev, D
AF Jovanovska, Elena Mitreska
Davcev, Danco
BE Urien, P
Piramuthu, S
TI No pollution Smart City Sightseeing Based on WSN Monitoring System
SO 2020 SIXTH INTERNATIONAL CONFERENCE ON MOBILE AND SECURE SERVICES
(MOBISECSERV))
SE Proceedings of the Conference on Mobile and Secure Services
LA English
DT Proceedings Paper
CT 6th International Conference on Mobile And Secure Services (MobiSecServ)
CY FEB 22-23, 2020
CL Miami Beach, FL
SP IEEE, Univ Florida, Telecom Paris, IP Paris
DE Air Pollution; Air Quality; Internet of Things (IoT); Cloud Computing;
Mobile Application; Smart Monitoring System; Smart City; Wireless Sensor
Network (WSN)
AB The 'Smart city' is an emerging concept in the last decade that covers many aspects of progressive city life. Many aspects of the Smart cities are inevitably related to ICT or more precisely to the Internet of Things (IoT). In this paper, we cover a smart environment and smart mobility in cities through a mobile application, sensors and web services that cover these aspects in smart cities. Having this in mind, we propose our IoT/Cloud-based 'Smart city' air pollution monitoring system. As the main contribution of this paper, we have designed and implemented a mobile application for air pollution indicators control and corresponding visualization. Additional novelty is the real time recommendations provided by our mobile application on the basis of air pollution parameters value.
C1 [Jovanovska, Elena Mitreska; Davcev, Danco] Univ Ss Cyril & Methodius, Fac Comp Sci & Engn, Skopje, North Macedonia.
RP Jovanovska, EM (corresponding author), Univ Ss Cyril & Methodius, Fac Comp Sci & Engn, Skopje, North Macedonia.
NR 17
TC 0
Z9 0
U1 1
U2 1
PU IEEE
PI NEW YORK
PA 345 E 47TH ST, NEW YORK, NY 10017 USA
SN 2640-5598
BN 978-1-7281-5797-9
J9 PROC CONF MOBILE SEC
PY 2020
PG 6
WC Engineering, Electrical & Electronic
SC Engineering
GA BP8NR
UT WOS:000566184800013
DA 2021-12-15
ER
PT C
AU Spandana, G
Shanmughasundram, R
AF Spandana, G.
Shanmughasundram, R.
GP IEEE