|
|
A target group tracking algorithm based on a hybrid sensor network |
Chun Zhang(张淳) |
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China |
|
|
Abstract Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.
|
Received: 12 November 2017
Revised: 30 April 2018
Accepted manuscript online:
|
PACS:
|
01.20.+x
|
(Communication forms and techniques (written, oral, electronic, etc.))
|
|
Fund: Project supported by the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20140875), the Scientific Research Foundation of Nanjing University of Posts and Telecommunications, China (Grant No. NY213084), and the National Natural Science Foundation of China (Grant No. 61502243). |
Corresponding Authors:
Chun Zhang
E-mail: zhc1088@njupt.edu.cn
|
Cite this article:
Chun Zhang(张淳) A target group tracking algorithm based on a hybrid sensor network 2018 Chin. Phys. B 27 080101
|
[1] |
Akyildiz I F, Su W, Sankarasubramaniam Y and Cayirci E 2002 Comput. Networks 38 393
|
[2] |
Fadel E Gungor V C Nassef L, Akkari N, Maik M G A Almasri S and Akyildiz I F 2015 Comput Commun. 71 22
|
[3] |
Chen Z, Cao J C 2013 Chin. Phys. B 22 059201
|
[4] |
Wang J R, Wang J P, He Z and Xu H T 2015 Chin. Phys. B 24 060101
|
[5] |
Liu H R Dong M R Yin R R and Li H 2015 Chin. Phys. B 24 050506
|
[6] |
Zheng X J Cui B T Lou X Y and Zhuang B 2017 Chin. Phys. B 26 040201
|
[7] |
Kim K 2013 Sensors 13 11314
|
[8] |
Zorbas D and Razafindralambo T 2013 Comput. Commun. 36 1039
|
[9] |
Halder S and Ghosal A 2016 Wireless Networks 22 2317
|
[10] |
Ren T, Wang Y F, Liu M M and Xu Y J 2016 Chin. Phys. B 25 020101
|
[11] |
Yang Y, Ding S and Wang B Z 2016 Chin. Phys. B 25 050101
|
[12] |
Mahapatra A, Anand K and Agrand D P 2006 Comput. Commun. 29 437
|
[13] |
Atia G K, Veeravalli V V and Fuemmeler J 2011 IEEE Trans. Signal Process. 59 4923
|
[14] |
Chen W P, Hou J C and Sha L 2004 IEEE Trans. Mob. Comput. 3 258
|
[15] |
Lai Y X Xie J S Lin Z YWang T and LiaoMH 2015 Sensors 15 23218
|
[16] |
Samarah S, Al-Hajri M and Boukerche A 2011 IEEE Trans. Veh. Technol. 60 656
|
[17] |
Xue L, Liu Z X and Guan X P 2011 J. Syst. Eng. Electron. 22 347
|
[18] |
BhuiyanMZ AWang G J Zhang L and Peng Y 2010 Journal of Central South University of Technology 17 340
|
[19] |
Rostami A S Mohana F and Keshavarz H 2017 Wireless Personal Communications 95 3585
|
[20] |
Zhao S and Yu L 2017 China Commun. 14 44
|
[21] |
Cao D L, Jin B H, Das S K and Cao J N 2010 J. Parallel Distrib. Comput. 70 825
|
[22] |
Wang T, Peng Z, Xu W Z, Liang J B, Wang G J, Tian H, Cai Y Q and Chen Y H 2017 Asian J. Control. 19 1350
|
[23] |
Wang T, Peng Z, Liang J B, Wen S, Bhuiyan M Z A, Cai Y Q and Cao J N 2016 ACM Trans. Sens. Netw. 12 1
|
[24] |
Mahboubi H, Momeni A, Aghdam A G, Sayrafian-Pour K and Marbukh V 2017 IEEE Trans. Control Syst. Technol. 20 1522
|
[25] |
Mahboubi H, MasoudimansourW, Aghdam A G and Sayrafian-Pour K 2017 IEEE Trans. Cybern. 47 511
|
[26] |
Hu F J and Tu C 2017 J. Intell. Fuzzy Syst. 32 3509
|
[27] |
Qi Y F, Cheng P, Bai J, Chen J M, Guenard A, Song Y Q and Shi Z G 2016 IEEE Trans. Ind. Electron. 63 6949
|
[28] |
Wang X B, Fu M Y and Zhang H S 2012 IEEE Trans. Mob. Comput. 11 567
|
[29] |
Wang Y C, Hu C C and Tseng Y C 2008 IEEE Trans. Mob. Comput. 7 262
|
[30] |
Khedr A M and Osamy W 2011 J. Parallel Distrib. Comput. 71 1318
|
[31] |
Nighot M, Ghatol A and Thakare V 2018 Int. J. Commun. Syst. 31 1
|
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|