Journal of Chinese Computer Systems 2015 3 3 Vol 36 No 3 2015 1 2 1 1 1 1 1 361021 2 E-mailwsnman@ gmail com 0 /1 NS2 TP393 A 1000-1220201503-0503-05 Heterogeneous Wireless Sensor Networks for Continuously Tracking Mobile Targets WANG Tian 1 2 PENG Zhen 1 CHEN Yong-hong 1 TIAN Hui 1 CAI Yi-qiao 1 1 College of Computer Science & Technology Huaqiao University Xiamen 361021 China 2 Department of Computing Hong Kong Polytechnic University Hong Kong AbstractTraditional wireless sensor networks consist of fixed sensor nodes so there are high energy consumption and coverage holes problems while tracking targets To this end this paper introduces a few of mobile sensors into traditional sensor networks for tracking targets continuously Based on this heterogeneous networks and a realistic detection model atrackingalgorithm is designed to cooperatively scheduling mobile sensors and fixed sensors Mobile sensors take advantage of their mobility to follow targets and detect them in short distances As a result the number of active fixed sensors is decreased and the energy can be saved Moreover mobilesensors can move into coverage holes which exist in traditional fixed networks to track targets Simulation results conducted on NS2 validate the effectiveness of the proposed tracking solution which can reduce the energy consumption of fixed sensors and improve the tracking quality Key wordstarget trackingheterogeneous wireless sensor networksmobile trackingeffective detectionenergy efficiency 1 WSN 1 2 1 3 2014-08-18 2014-10-16 61202468 61370007 61302094 61305085 2014J01240 12BS218 1982 1990 CCF 1974 1982 1984
504 2015 2 6 7 7 i e i e s d i e n e i = e s d i + e n 1 e 4 s d i d i i e 1 s d i d i s 5 0 if d e s d i = d i /d 0 k i > d 0 2 { s 0 if d i d0 d 0 s 0 d 0 k 2 5 e n μ σ 2 i e i 8-10 Kumar 8 e i ~ Nμ + e s d i σ 2 3 4 2 10 WSN Tan 9 GPS 11 12 4 1 λ λ 13 4 3 2 3 i e i ( ) 3 1 pe i = 槡 2πσ exp - e 2 i - μ - e s d i 2 4 2σ L L s i λ m sm λ 1 0 v m v t v m < v t i P i D P D α P D α P i + D = pe i de i 5 n 4 P D P 4 1 D = 1 - n 1 - P i D 6 i = 1 9 6 λ
3 505 2 d 0 4 5 r j L 0 L 0 P D < α 5 m Omn 5 1 Omn 1 1 1 2 L 2 0 3 L 0 m m < n S P D > α L 0 1 ~ 2 L 0 4 L 0 S m m < n S 5 FOR i SDO P D P D > α 6 IF 2 μ + 3σ L 0 L 0 L 0 2d 0 THEN 3 ~ 4 7 i S S L 0 2d 0 8 END IF 2 μ + 3σ 9 END FOR 5 ~ 9 10 IF L 0 > 2d 0 THEN L 0 11 L 0 L 0 2d 0 12 END IF L 0 L 0 13 L 0 d 1 d 1 > d 0 L 0 10 ~ 12 r k r k α P D Om 14 WHILE DO 15 IF r j P D > αthen 16 r j S 17 IF r j L 0 THEN 18 L 0 19 END IF 20 END IF 1 L 0 P 21 D IF P D < αthen Fig 1 L 0 predicts the location of mobile nodes and calculates P D 22 S r k S 23 END IF L 0 d 1 d 1 > d 0 24 END WHILE 13 1 2 L 0 5 2 i L 0 L 0 2d 0 3 T 1 T 2 i T i i = d 2 i T 2 4d 0 T 2 i T 1 i + 4d 0 T 1 T 2 T 1 T 2 > 4d 0 L 0 T 1 + L 0 T 2 > T 1 T 2 L 0 T 2 > T 1 T 2 - L 0 T 1 L 0 T 1 < 2d 0 L 0 2 T 2 L 0 T 2 > 2d 0 Fig 2 Some fixed node switches to sleeping state 6 1 P D 14 ~ 6 1 24 P D > α r j αr j r j NS - 2 35
506 2015 1 100 10 1 5 3 i L 0 2d 0 Fig 3 Node i determine whether the distance between L 0 and the target is greater than 2d 0 Mobile Tracking MT Fixed Tracking FT Cluster Tracking 7 CT CT m m 100 100 s 100 m 10 v m m /s 1 v t m /s 3 α% 80% 100 4 MT FT CT MT FT CT 50% FT 5 1 Fig 5 Number of holes vs energy consumption Table 1 Experiment parameters and their values 5 MT FT CT CT FT MT FT 60% 6 7 8 100 0 20 100 Fig 4 4 6 Number of holes vs effective monitoring ratio α 0 α 80% MT FT 50% MT 5 10 20 MT FT 35% 52% 73% CT 1mJ /s 0mJ /s 6 2 Fig 6 Number of mobile nodes vs effective monitoring ratio 4 5 6 MT FT CT 47% 65% 89% MT
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