A New WSN Deployment Approach for Air Pollution Monitoring

被引:0
作者
Boubrima, Ahmed [1 ]
Bechkit, Walid [1 ]
Rivano, Herve [1 ]
机构
[1] Univ Lyon, INRIA, INSA Lyon, CITI INRIA, F-69621 Villeurbanne, France
来源
2017 14TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) | 2017年
关键词
Smart city; Air pollution monitoring; Detection of threshold crossings of pollutants; Spatial data clustering; Wireless sensor networks (WSN); Deployment; Coverage; Connectivity; WIRELESS SENSOR NETWORKS; COVERAGE; LOCATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the increasing industrialization and the massive urbanization, air pollution monitoring is being considered as one of the major challenges of smart cities. Many air pollution monitoring systems have been proposed in the literature, among which wireless sensor networks seem to be a leading solution thanks to sensors' low cost and autonomy as well as their finegrained deployment. A careful deployment of sensors is therefore necessary to get better performances while ensuring a minimal financial cost. In this paper, we consider citywide wireless sensor networks and tackle the minimum- cost node positioning issue for air pollution monitoring. We propose an efficient approach that aims to find optimal sensors and sinks locations while ensuring air pollution coverage and network connectivity. Unlike most of the existing methods, which rely on simple and generic detection models, our approach is based on the spatial analysis of pollution data, allowing to take into account the nature of the pollution phenomenon. As proof of concept, we apply our approach on real world data, namely the Paris pollution data, which was recorded in March 2014. We also perform extensive simulations in order to study the performance of our approach in comparison to the existing methods.
引用
收藏
页码:455 / 460
页数:6
相关论文
共 15 条
[1]   Binary integer programming formulation and heuristics for differentiated coverage in heterogeneous sensor networks [J].
Altinel, I. Kuban ;
Aras, Necati ;
Guney, Evren ;
Ersoy, Cem .
COMPUTER NETWORKS, 2008, 52 (12) :2419-2431
[2]  
[Anonymous], 2009, ENCY DATABASE SYSTEM
[3]  
[Anonymous], 2006 INT C WIR MOB C
[4]   ST-DBSCAN: An algorithm for clustering spatial-temp oral data [J].
Birant, Derya ;
Kut, Alp .
DATA & KNOWLEDGE ENGINEERING, 2007, 60 (01) :208-221
[5]   Grid coverage for surveillance and target location in distributed sensor networks [J].
Chakrabarty, K ;
Iyengar, SS ;
Qi, HR ;
Cho, EC .
IEEE TRANSACTIONS ON COMPUTERS, 2002, 51 (12) :1448-1453
[6]  
Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining, P226
[7]  
Guney E., 2008, ISCIS 08 23 INT S, P1
[8]   Efficient integer programming formulations for optimum sink location and routing in heterogeneous wireless sensor networks [J].
Guney, Evren ;
Aras, Necati ;
Altinel, I. Kuban ;
Ersoy, Cem .
COMPUTER NETWORKS, 2010, 54 (11) :1805-1822
[9]  
Kama A. A. L., TECH REP
[10]   Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility [J].
Keskin, M. Emre ;
Altinel, I. Kuban ;
Aras, Necati ;
Ersoy, Cem .
AD HOC NETWORKS, 2014, 17 :18-36