scholarly journals Energy-efficient non-linear k-barrier coverage in mobile sensor network

2020 ◽  
Vol 17 (3) ◽  
pp. 737-758
Author(s):  
Zijing Ma ◽  
Shuangjuan Li ◽  
Longkun Guo ◽  
Guohua Wang
Keyword(s):  
Sensor Network ◽  
Mobile Sensors ◽  
Barrier Coverage ◽  
Force Model ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Virtual Force ◽  
Straight Line ◽  
Non Linear ◽  
Single Sensor

K-barrier coverage is an important coverage model for achieving robust barrier coverage in wireless sensor networks. After initial random sensor deployment, k-barrier coverage can be achieved by moving mobile sensors to form k barriers consisting of k sensor chains crossing the region. In mobile sensor network, it is challenging to reduce the moving distances of mobile sensors to prolong the network lifetime. Existing work mostly focused on forming linear barriers, that is the final positions of sensors are on a straight line, which resulted in large redundant movements. However, the moving cost of sensors can be further reduced if nonlinear barriers are allowed, which means that sensors? final positions need not be on a straight line. In this paper, we propose two algorithms of forming non-linear k barriers energy-efficiently. The algorithms use a novel model, called horizontal virtual force model, which considers both the euclidean distance and horizontal angle between two sensors. Then we propose two barrier forming algorithms. To construct a barrier, one algorithm always chooses the mobile sensor chain with the largest horizontal virtual force and then flattens it, called sensor chain algorithm. The other chooses the mobile sensor with the largest horizontal virtual force to construct the barrier, other than the mobile sensor chain, called single sensor algorithm. Simulation results show that the algorithms significantly reduce the movements of mobile sensors compared to a linear k-barrier coverage algorithm. Besides, the sensor chain algorithm outperforms the single sensor algorithm when the sensor density becomes higher.

scholarly journals Optimizing the Sensor Movement for Barrier Coverage in a Sink-Based Deployed Mobile Sensor Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 156301-156314 ◽  
Author(s):  
Shuangjuan Li ◽  
Hong Shen ◽  
Qiong Huang ◽  
Longkun Guo
Keyword(s):  
Sensor Network ◽  
Barrier Coverage ◽  
Mobile Sensor ◽  
Mobile Sensor Network

scholarly journals A mobile sensor network to map carbon dioxide emissions in urban environments

2016 ◽  
Author(s):  
Joseph K. Lee ◽  
Andreas Christen ◽  
Rick Ketler ◽  
Zoran Nesic
Keyword(s):  
Carbon Dioxide ◽  
Sensor Network ◽  
Spatial Resolution ◽  
Aerodynamic Resistance ◽  
Mobile Sensors ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Emissions Inventory ◽  
Mixing Ratios ◽  
Resistance Approach

Abstract. A method for directly measuring carbon dioxide (CO2) emissions using a mobile sensor network in cities at fine spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined with open-source hardware to control, georeference and log measurements of CO2 mixing ratios on vehicles (car, bikes). Second, two measurement campaigns, one in summer and one in winter (heating-season) were carried out. Five mobile sensors were deployed within a 1 x 12.7 km transect across the City of Vancouver, BC, Canada. The sensors were operated for 3.5 hours on pre-defined routes to map CO2 mixing ratios at street level, which was then averaged to 100 x 100 m grids. The grid-averaged CO2 mixing ratios were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios were highest in the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO2 emissions from the gridded CO2 mixing ratio measurements in conjunction with mixing ratios and fluxes collected from a 28-m tall eddy-covariance tower located within the study area. These measured emissions showed a range of −12 to 226 kg CO2 ha−1 hr−1 in summer and of −14 to 163 kg CO2 ha−1 hr−1 in winter, with an average of 35.1 kg CO2 ha−1 hr−1 (summer) and 25.9 kg CO2 ha−1 hr−1 (winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06 kg CO2 ha−1 hr−1 (summer) and 28.76 kg CO2 ha−1 hr−1 (winter) and was used to compare against the measured emissions from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO2 mixing ratios and the emissions inventory (R2 = 0.53 in summer and R2 = 0.47 in winter). 87 % (summer) and 94 % (winter) of measured grid cells show a difference within ±1 order, and 49 % (summer) and 69 % (winter) show an error of less than a factor 2. Although associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.

scholarly journals A mobile sensor network to map carbon dioxide emissions in urban environments

2017 ◽  
Vol 10 (2) ◽  
pp. 645-665 ◽  
Author(s):  
Joseph K. Lee ◽  
Andreas Christen ◽  
Rick Ketler ◽  
Zoran Nesic
Keyword(s):  
Carbon Dioxide ◽  
Sensor Network ◽  
Aerodynamic Resistance ◽  
Mobile Sensors ◽  
Grid Cells ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Emissions Inventory ◽  
Mixing Ratios ◽  
Resistance Approach

Abstract. A method for directly measuring carbon dioxide (CO2) emissions using a mobile sensor network in cities at fine spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined with open-source hardware to control, georeference, and log measurements of CO2 mixing ratios on vehicles (car, bicycles). Second, two measurement campaigns, one in summer and one in winter (heating season) were carried out. Five mobile sensors were deployed within a 1 × 12. 7 km transect across the city of Vancouver, BC, Canada. The sensors were operated for 3.5 h on pre-defined routes to map CO2 mixing ratios at street level, which were then averaged to 100  ×  100 m grid cells. The averaged CO2 mixing ratios of all grids in the study area were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios were highest in the grid cells of the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO2 emissions from the gridded CO2 mixing ratio measurements in conjunction with mixing ratios and fluxes collected from a 28 m tall eddy-covariance tower located within the study area. These measured emissions showed a range of −12 to 226 CO2 ha−1 h−1 in summer and of −14 to 163 kg CO2 ha−1 h−1 in winter, with an average of 35.1 kg CO2 ha−1 h−1 (summer) and 25.9 kg CO2 ha−1 h−1 (winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06 kg CO2 ha−1 h−1 (summer) and 28.76 kg CO2 ha−1 h−1 (winter) and was used to compare against the measured emissions from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO2 mixing ratios and the emissions inventory (R2 = 0. 53 in summer and R2 = 0. 47 in winter). Also, 87 % (summer) and 94 % (winter) of measured grid cells show a difference within ±1 order of magnitude, and 49 % (summer) and 69 % (winter) show an error of less than a factor 2. Although associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.

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