An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks

Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose...
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Autor*in:	Yanlong Sun [verfasserIn] Yazhou Yuan [verfasserIn] Xiaolei Li [verfasserIn] Qimin Xu [verfasserIn] Xinping Guan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 6(2018), Seite 68324-68336
Übergeordnetes Werk:	volume:6 ; year:2018 ; pages:68324-68336

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2018.2879536

Katalog-ID:	DOAJ015277860

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520			\|a Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.
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allfields	10.1109/ACCESS.2018.2879536 doi (DE-627)DOAJ015277860 (DE-599)DOAJ184038a18c9b41f6851caaaa88188097 DE-627 ger DE-627 rakwb eng TK1-9971 Yanlong Sun verfasserin aut An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area. Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller Electrical engineering. Electronics. Nuclear engineering Yazhou Yuan verfasserin aut Xiaolei Li verfasserin aut Qimin Xu verfasserin aut Xinping Guan verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 68324-68336 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:68324-68336 https://doi.org/10.1109/ACCESS.2018.2879536 kostenfrei https://doaj.org/article/184038a18c9b41f6851caaaa88188097 kostenfrei https://ieeexplore.ieee.org/document/8528403/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 68324-68336
spelling	10.1109/ACCESS.2018.2879536 doi (DE-627)DOAJ015277860 (DE-599)DOAJ184038a18c9b41f6851caaaa88188097 DE-627 ger DE-627 rakwb eng TK1-9971 Yanlong Sun verfasserin aut An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area. Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller Electrical engineering. Electronics. Nuclear engineering Yazhou Yuan verfasserin aut Xiaolei Li verfasserin aut Qimin Xu verfasserin aut Xinping Guan verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 68324-68336 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:68324-68336 https://doi.org/10.1109/ACCESS.2018.2879536 kostenfrei https://doaj.org/article/184038a18c9b41f6851caaaa88188097 kostenfrei https://ieeexplore.ieee.org/document/8528403/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 68324-68336
allfields_unstemmed	10.1109/ACCESS.2018.2879536 doi (DE-627)DOAJ015277860 (DE-599)DOAJ184038a18c9b41f6851caaaa88188097 DE-627 ger DE-627 rakwb eng TK1-9971 Yanlong Sun verfasserin aut An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area. Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller Electrical engineering. Electronics. Nuclear engineering Yazhou Yuan verfasserin aut Xiaolei Li verfasserin aut Qimin Xu verfasserin aut Xinping Guan verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 68324-68336 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:68324-68336 https://doi.org/10.1109/ACCESS.2018.2879536 kostenfrei https://doaj.org/article/184038a18c9b41f6851caaaa88188097 kostenfrei https://ieeexplore.ieee.org/document/8528403/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 68324-68336
allfieldsGer	10.1109/ACCESS.2018.2879536 doi (DE-627)DOAJ015277860 (DE-599)DOAJ184038a18c9b41f6851caaaa88188097 DE-627 ger DE-627 rakwb eng TK1-9971 Yanlong Sun verfasserin aut An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area. Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller Electrical engineering. Electronics. Nuclear engineering Yazhou Yuan verfasserin aut Xiaolei Li verfasserin aut Qimin Xu verfasserin aut Xinping Guan verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 68324-68336 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:68324-68336 https://doi.org/10.1109/ACCESS.2018.2879536 kostenfrei https://doaj.org/article/184038a18c9b41f6851caaaa88188097 kostenfrei https://ieeexplore.ieee.org/document/8528403/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 68324-68336
allfieldsSound	10.1109/ACCESS.2018.2879536 doi (DE-627)DOAJ015277860 (DE-599)DOAJ184038a18c9b41f6851caaaa88188097 DE-627 ger DE-627 rakwb eng TK1-9971 Yanlong Sun verfasserin aut An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area. Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller Electrical engineering. Electronics. Nuclear engineering Yazhou Yuan verfasserin aut Xiaolei Li verfasserin aut Qimin Xu verfasserin aut Xinping Guan verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 68324-68336 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:68324-68336 https://doi.org/10.1109/ACCESS.2018.2879536 kostenfrei https://doaj.org/article/184038a18c9b41f6851caaaa88188097 kostenfrei https://ieeexplore.ieee.org/document/8528403/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 68324-68336
language	English
source	In IEEE Access 6(2018), Seite 68324-68336 volume:6 year:2018 pages:68324-68336
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topic_facet	Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Yanlong Sun @@aut@@ Yazhou Yuan @@aut@@ Xiaolei Li @@aut@@ Qimin Xu @@aut@@ Xinping Guan @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Yanlong Sun
spellingShingle	Yanlong Sun misc TK1-9971 misc Underwater wireless sensor networks (UWSNs) misc target tracking misc adaptive sampling misc fuzzy logic controller misc Electrical engineering. Electronics. Nuclear engineering An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks
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topic_title	TK1-9971 An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks Underwater wireless sensor networks (UWSNs) target tracking adaptive sampling fuzzy logic controller
topic	misc TK1-9971 misc Underwater wireless sensor networks (UWSNs) misc target tracking misc adaptive sampling misc fuzzy logic controller misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Underwater wireless sensor networks (UWSNs) misc target tracking misc adaptive sampling misc fuzzy logic controller misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Underwater wireless sensor networks (UWSNs) misc target tracking misc adaptive sampling misc fuzzy logic controller misc Electrical engineering. Electronics. Nuclear engineering
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title	An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks
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title_full	An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks
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title_sort	adaptive sampling algorithm for target tracking in underwater wireless sensor networks
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title_auth	An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks
abstract	Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.
abstractGer	Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.
abstract_unstemmed	Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.
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title_short	An Adaptive Sampling Algorithm for Target Tracking in Underwater Wireless Sensor Networks
url	https://doi.org/10.1109/ACCESS.2018.2879536 https://doaj.org/article/184038a18c9b41f6851caaaa88188097 https://ieeexplore.ieee.org/document/8528403/ https://doaj.org/toc/2169-3536
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author2	Yazhou Yuan Xiaolei Li Qimin Xu Xinping Guan
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