A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks
This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on cybernetics Jg. 49; H. 1; S. 171 - 183 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
IEEE
01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor's local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system's true state always resides in each sensor's bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach. |
|---|---|
| AbstractList | This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor’s local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the syste’s true state always resides in each sensor’s bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach. This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor's local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system's true state always resides in each sensor's bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach. This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor's local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system's true state always resides in each sensor's bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach.This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor's local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system's true state always resides in each sensor's bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach. |
| Author | Han, Qing-Long Wang, Zidong Ge, Xiaohua |
| Author_xml | – sequence: 1 givenname: Xiaohua orcidid: 0000-0003-0180-0897 surname: Ge fullname: Ge, Xiaohua organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 2 givenname: Qing-Long orcidid: 0000-0002-7207-0716 surname: Han fullname: Han, Qing-Long email: qhan@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia – sequence: 3 givenname: Zidong orcidid: 0000-0002-9576-7401 surname: Wang fullname: Wang, Zidong organization: Department of Computer Science, Brunel University London, Uxbridge, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29990117$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kUtP3DAURq2KqlDgB6BKVSQ23WTwI_FjCcO0VOKxYKqKleV4bsCQOIPtUPHv62EGFizwxpZ1ztW99_uKtvzgAaEDgieEYHU0n96cTCgmYkIFV4LST2iHEi5LSkW99fbmYhvtx3iP85H5S8kvaJsqpTAhYgeNx8Xpsze9s8XsCXwq58Hd3kKARTEPxsfexegGX1zbO-ihaIdQnLqYgmvGlJlrSOUF9A2EeOeWxSwm15u0Eq6eIBR_XYAOYsycj1m9hPRvCA9xD31uTRdhf3Pvoj8_Z_PpWXl-9ev39Pi8tFSoVFpcgTUcbFNjs5DYtqxhqgHVsFpyUhticFXxtsasaamqFTBStRWjBNNKtortoh_russwPI4Qk87zWOg642EYo6aYS1ZxzEVGD9-h98MYfO5OU1JLhSlRPFPfN9TY9LDQy5DnDc_6daEZEGvAhiHGAK22Lr1sJAXjOk2wXqWnV-npVXp6k142yTvztfhHzre14wDgjZeEVIxh9h_HM6St |
| CODEN | ITCEB8 |
| CitedBy_id | crossref_primary_10_1109_JSYST_2024_3359427 crossref_primary_10_1002_rnc_4963 crossref_primary_10_1007_s11432_019_2680_1 crossref_primary_10_1080_00207721_2021_1897707 crossref_primary_10_1002_rnc_5139 crossref_primary_10_1088_1402_4896_ad0330 crossref_primary_10_1109_TII_2022_3187747 crossref_primary_10_1109_TSIPN_2022_3174966 crossref_primary_10_1016_j_neucom_2021_08_081 crossref_primary_10_1109_TIM_2022_3205655 crossref_primary_10_1109_TCYB_2021_3066639 crossref_primary_10_1016_j_ins_2019_05_073 crossref_primary_10_1109_TCYB_2019_2912622 crossref_primary_10_1155_2019_1648576 crossref_primary_10_1016_j_ejcon_2023_100802 crossref_primary_10_1016_j_neucom_2019_03_088 crossref_primary_10_1109_ACCESS_2020_3032717 crossref_primary_10_1016_j_neucom_2019_03_089 crossref_primary_10_1109_TFUZZ_2020_2965877 crossref_primary_10_1016_j_jfranklin_2022_12_021 crossref_primary_10_1109_TSMC_2022_3214732 crossref_primary_10_1177_01423312221126233 crossref_primary_10_3390_math13081248 crossref_primary_10_1109_TNNLS_2023_3302190 crossref_primary_10_1007_s10586_024_04693_z crossref_primary_10_1002_rnc_6352 crossref_primary_10_1109_TCSI_2022_3197846 crossref_primary_10_1109_TNSE_2023_3235008 crossref_primary_10_1007_s11432_019_2691_4 crossref_primary_10_1016_j_nahs_2021_101022 crossref_primary_10_1109_TAC_2022_3232988 crossref_primary_10_1109_JSYST_2022_3142183 crossref_primary_10_1109_TCSII_2022_3229412 crossref_primary_10_1109_TCYB_2021_3079149 crossref_primary_10_1007_s00521_023_08419_x crossref_primary_10_1109_JSEN_2023_3279399 crossref_primary_10_1080_00207179_2021_1974095 crossref_primary_10_1109_TCYB_2022_3215015 crossref_primary_10_1109_TSMC_2019_2963411 crossref_primary_10_1109_TNSE_2024_3355226 crossref_primary_10_1109_TCYB_2019_2963087 crossref_primary_10_1007_s12555_022_0598_2 crossref_primary_10_1016_j_ins_2019_08_052 crossref_primary_10_1016_j_neucom_2022_06_074 crossref_primary_10_1109_TAC_2023_3311709 crossref_primary_10_1109_TNSE_2022_3216572 crossref_primary_10_1007_s11071_025_11718_8 crossref_primary_10_1109_TNNLS_2022_3183447 crossref_primary_10_1016_j_neucom_2019_03_090 crossref_primary_10_1016_j_neucom_2019_03_096 crossref_primary_10_1109_TCYB_2018_2868778 crossref_primary_10_1177_01423312211051202 crossref_primary_10_1002_rnc_6462 crossref_primary_10_1049_cth2_12194 crossref_primary_10_1080_00207721_2022_2055192 crossref_primary_10_1109_TNSE_2020_3017493 crossref_primary_10_1038_s41598_024_54761_y crossref_primary_10_1371_journal_pone_0299535 crossref_primary_10_1016_j_ins_2023_119869 crossref_primary_10_1016_j_isatra_2022_04_040 crossref_primary_10_1109_TCYB_2019_2912403 crossref_primary_10_1109_TCYB_2021_3070356 crossref_primary_10_1109_TSMC_2020_3035037 crossref_primary_10_1016_j_ins_2019_08_063 crossref_primary_10_1109_TIE_2024_3357884 crossref_primary_10_1109_TCYB_2020_3016093 crossref_primary_10_3389_fenrg_2022_888585 crossref_primary_10_1007_s11432_019_2654_y crossref_primary_10_1080_00207721_2022_2031338 crossref_primary_10_1109_TASE_2023_3284448 crossref_primary_10_1109_LSP_2022_3183494 crossref_primary_10_1109_TCYB_2017_2789212 crossref_primary_10_1109_TCYB_2019_2901631 crossref_primary_10_1109_TSMC_2019_2958529 crossref_primary_10_1007_s11071_021_06200_0 crossref_primary_10_1109_TNSE_2025_3571001 crossref_primary_10_1016_j_automatica_2025_112347 crossref_primary_10_1016_j_isatra_2024_10_008 crossref_primary_10_3389_frai_2021_573731 crossref_primary_10_1049_iet_cta_2019_1478 crossref_primary_10_1109_TNNLS_2021_3053652 crossref_primary_10_1109_TNSE_2023_3327591 crossref_primary_10_1080_21642583_2020_1737846 crossref_primary_10_32604_cmes_2022_020127 crossref_primary_10_1109_TCSII_2023_3236148 crossref_primary_10_1016_j_ins_2021_10_062 crossref_primary_10_1109_ACCESS_2023_3338446 crossref_primary_10_1109_ACCESS_2023_3342015 crossref_primary_10_1109_TCYB_2021_3051963 crossref_primary_10_1109_TNNLS_2022_3160645 crossref_primary_10_1016_j_ins_2022_01_069 crossref_primary_10_1109_TCSII_2022_3203824 crossref_primary_10_1109_TIE_2025_3536560 crossref_primary_10_1109_TAC_2023_3234453 crossref_primary_10_1016_j_chaos_2023_113535 crossref_primary_10_1109_TCYB_2019_2922740 crossref_primary_10_1002_acs_3209 crossref_primary_10_1016_j_automatica_2019_108557 crossref_primary_10_1109_TSIPN_2022_3182273 crossref_primary_10_1109_TNSE_2024_3383280 crossref_primary_10_1007_s12555_022_0942_6 crossref_primary_10_1007_s11432_019_2637_9 crossref_primary_10_1016_j_sysconle_2019_104533 crossref_primary_10_1080_00207721_2021_2005178 crossref_primary_10_1080_00207179_2021_2005828 crossref_primary_10_1016_j_neucom_2020_04_156 crossref_primary_10_1109_LCSYS_2022_3172791 crossref_primary_10_1016_j_ins_2019_08_080 crossref_primary_10_1016_j_automatica_2025_112278 crossref_primary_10_1109_TSMC_2023_3348290 crossref_primary_10_1002_rnc_6798 crossref_primary_10_1109_TCYB_2020_3001187 crossref_primary_10_1007_s13042_024_02204_5 crossref_primary_10_1109_TCYB_2018_2885567 crossref_primary_10_1016_j_neucom_2024_128067 crossref_primary_10_1080_00207721_2022_2056772 crossref_primary_10_1080_21642583_2021_1919935 crossref_primary_10_1109_TCSI_2023_3242974 crossref_primary_10_1080_00207721_2020_1868615 crossref_primary_10_1109_TCYB_2018_2869418 crossref_primary_10_1016_j_eswa_2023_121085 crossref_primary_10_1016_j_jfranklin_2023_09_045 crossref_primary_10_1109_TCYB_2021_3088636 crossref_primary_10_3390_en16135146 crossref_primary_10_1109_TNNLS_2021_3085001 crossref_primary_10_1109_TSMC_2020_3041121 crossref_primary_10_1016_j_neucom_2019_04_080 crossref_primary_10_1007_s00034_024_02689_z crossref_primary_10_1016_j_neucom_2019_04_081 crossref_primary_10_1109_TSMC_2022_3146182 crossref_primary_10_1016_j_neucom_2019_04_082 crossref_primary_10_1016_j_neucom_2019_04_083 crossref_primary_10_1109_TIM_2022_3150889 crossref_primary_10_3390_app14104030 crossref_primary_10_1016_j_neucom_2019_04_084 crossref_primary_10_1109_ACCESS_2021_3107324 crossref_primary_10_1109_TII_2022_3179409 crossref_primary_10_1109_JAS_2021_1004015 crossref_primary_10_1109_TSIPN_2020_3039395 crossref_primary_10_1002_rnc_7632 crossref_primary_10_1109_TCYB_2023_3293010 crossref_primary_10_1002_rnc_7994 crossref_primary_10_1109_TCYB_2021_3125851 crossref_primary_10_1109_JAS_2021_1003967 crossref_primary_10_1109_JAS_2024_124338 crossref_primary_10_1080_00207721_2020_1801883 crossref_primary_10_1109_TSIPN_2023_3277278 crossref_primary_10_1109_TSP_2023_3326966 crossref_primary_10_1007_s11432_024_4259_x crossref_primary_10_1109_TFUZZ_2020_3002393 crossref_primary_10_3390_e23111452 crossref_primary_10_20965_jaciii_2020_p0917 crossref_primary_10_1109_TCYB_2019_2917179 crossref_primary_10_1002_acs_3342 crossref_primary_10_1109_TCYB_2021_3067822 crossref_primary_10_1109_TAC_2020_3018437 crossref_primary_10_1109_TCYB_2019_2956736 crossref_primary_10_1016_j_ins_2020_07_041 crossref_primary_10_1016_j_jfranklin_2021_06_024 crossref_primary_10_1109_TCYB_2020_2983544 crossref_primary_10_1177_01423312211043666 crossref_primary_10_1109_TCYB_2021_3049838 crossref_primary_10_1109_TSMC_2019_2907620 crossref_primary_10_1007_s12555_018_0780_8 crossref_primary_10_1049_cth2_12718 crossref_primary_10_1016_j_neucom_2020_07_023 crossref_primary_10_1109_TVT_2024_3458994 crossref_primary_10_1080_00207721_2025_2456001 crossref_primary_10_1109_TNSE_2022_3201395 crossref_primary_10_1002_rnc_6699 crossref_primary_10_1109_TCNS_2020_2980362 crossref_primary_10_1109_TSMC_2025_3560404 crossref_primary_10_1016_j_jfranklin_2025_107782 crossref_primary_10_1007_s11432_020_3178_3 crossref_primary_10_1016_j_isatra_2019_04_018 crossref_primary_10_1109_ACCESS_2023_3303203 crossref_primary_10_1016_j_ifacol_2023_10_641 crossref_primary_10_1016_j_neunet_2020_06_023 crossref_primary_10_1109_TCYB_2019_2924258 crossref_primary_10_1109_TCYB_2020_2987576 crossref_primary_10_1002_rnc_5900 crossref_primary_10_1109_TVT_2022_3184305 crossref_primary_10_1109_JIOT_2022_3222188 crossref_primary_10_1080_21642583_2022_2074169 crossref_primary_10_1109_TNSE_2022_3223040 crossref_primary_10_1007_s40314_023_02415_6 crossref_primary_10_1109_TCYB_2020_3010917 crossref_primary_10_1002_acs_3591 crossref_primary_10_1016_j_neunet_2019_09_006 crossref_primary_10_1109_TCYB_2017_2776976 crossref_primary_10_1109_TFUZZ_2020_3029292 crossref_primary_10_1109_TCYB_2021_3054633 crossref_primary_10_1002_acs_3906 crossref_primary_10_1080_00207721_2021_1998722 crossref_primary_10_1080_00207721_2021_1998843 crossref_primary_10_1109_JAS_2021_1003826 crossref_primary_10_1080_00207721_2024_2315219 crossref_primary_10_1016_j_jfranklin_2019_06_028 crossref_primary_10_1109_ACCESS_2019_2925009 crossref_primary_10_1007_s11071_024_10016_z crossref_primary_10_1016_j_isatra_2023_03_017 crossref_primary_10_1016_j_neucom_2021_12_100 crossref_primary_10_1109_TCNS_2022_3185145 crossref_primary_10_1109_TSMC_2020_3010825 crossref_primary_10_1016_j_ins_2021_01_072 crossref_primary_10_1016_j_jfranklin_2022_06_049 crossref_primary_10_1109_TIM_2022_3174307 crossref_primary_10_1016_j_isatra_2022_01_010 crossref_primary_10_1016_j_jfranklin_2024_106672 crossref_primary_10_1109_TCYB_2021_3049461 crossref_primary_10_1016_j_jfranklin_2022_09_017 crossref_primary_10_1109_TFUZZ_2020_2983904 crossref_primary_10_1109_TCYB_2019_2948427 crossref_primary_10_1109_TSMC_2018_2876203 crossref_primary_10_1016_j_neucom_2022_03_013 crossref_primary_10_1080_00207721_2020_1871528 crossref_primary_10_1016_j_jfranklin_2022_05_009 crossref_primary_10_1016_j_isatra_2022_02_046 crossref_primary_10_1080_00207721_2021_1872118 crossref_primary_10_1016_j_cnsns_2022_106634 crossref_primary_10_1016_j_cnsns_2025_108663 crossref_primary_10_1177_01423312241228893 crossref_primary_10_1109_TCYB_2020_2996296 crossref_primary_10_1016_j_ins_2020_05_023 crossref_primary_10_1109_TCYB_2019_2900478 crossref_primary_10_3390_s21217256 crossref_primary_10_1109_TCST_2022_3180942 crossref_primary_10_1016_j_ins_2021_01_057 crossref_primary_10_1016_j_neucom_2022_05_096 crossref_primary_10_1109_JSYST_2024_3379572 crossref_primary_10_1109_TCYB_2021_3057545 crossref_primary_10_1109_TII_2018_2817248 crossref_primary_10_1109_TCYB_2019_2923011 crossref_primary_10_1109_TSMC_2022_3222175 crossref_primary_10_1016_j_automatica_2021_109684 crossref_primary_10_1007_s11432_020_3243_7 crossref_primary_10_1109_TCYB_2018_2877413 crossref_primary_10_1109_TCYB_2019_2917929 crossref_primary_10_1109_TCYB_2017_2789296 crossref_primary_10_1109_TCYB_2019_2936413 crossref_primary_10_1109_TII_2019_2905295 crossref_primary_10_1109_TSIPN_2020_3046220 crossref_primary_10_1109_TASE_2025_3577977 crossref_primary_10_1109_TCST_2018_2842208 crossref_primary_10_1016_j_neucom_2022_07_021 crossref_primary_10_1109_TSIPN_2023_3239681 crossref_primary_10_1016_j_neucom_2018_07_087 crossref_primary_10_1109_TSMC_2019_2905253 crossref_primary_10_1016_j_jfranklin_2021_07_036 crossref_primary_10_1080_00207721_2022_2049918 crossref_primary_10_1002_rnc_5668 crossref_primary_10_1016_j_cnsns_2023_107408 crossref_primary_10_1109_JSYST_2021_3063357 crossref_primary_10_1109_TCYB_2019_2924450 crossref_primary_10_1007_s12555_022_0763_7 crossref_primary_10_1109_TSMC_2018_2882540 crossref_primary_10_1109_ACCESS_2019_2914922 crossref_primary_10_1080_21642583_2022_2158959 crossref_primary_10_1177_10775463211036827 crossref_primary_10_1109_TBDATA_2020_2988778 crossref_primary_10_1109_TSIPN_2024_3497773 crossref_primary_10_1016_j_ins_2019_03_058 crossref_primary_10_1109_TCYB_2019_2903522 crossref_primary_10_1109_TAC_2019_2934389 crossref_primary_10_1109_TCYB_2019_2956491 crossref_primary_10_1109_TITS_2020_2970472 crossref_primary_10_1002_rnc_5674 crossref_primary_10_1016_j_eswa_2023_123048 crossref_primary_10_1109_TCYB_2019_2921733 crossref_primary_10_3390_machines11020128 crossref_primary_10_1080_00207179_2019_1622790 crossref_primary_10_1007_s11071_024_09778_3 crossref_primary_10_1016_j_nahs_2023_101338 crossref_primary_10_1016_j_epsr_2023_109417 crossref_primary_10_1109_TCYB_2018_2883793 crossref_primary_10_1109_TASE_2024_3360718 crossref_primary_10_1016_j_neucom_2022_08_041 crossref_primary_10_1109_TAC_2024_3496577 crossref_primary_10_1109_TCYB_2019_2891112 crossref_primary_10_1109_TSG_2025_3525558 crossref_primary_10_1016_j_rser_2024_114922 crossref_primary_10_1016_j_automatica_2021_109784 crossref_primary_10_1016_j_neucom_2022_10_020 crossref_primary_10_1016_j_jfranklin_2021_08_006 crossref_primary_10_1109_TCYB_2018_2890645 crossref_primary_10_1109_TSMC_2020_3035768 crossref_primary_10_1109_TCSII_2021_3109884 crossref_primary_10_1109_TNNLS_2020_3026646 crossref_primary_10_1109_TITS_2019_2934481 crossref_primary_10_1109_TSMC_2019_2946212 crossref_primary_10_1109_ACCESS_2022_3213938 crossref_primary_10_1002_asjc_3017 crossref_primary_10_1016_j_ins_2019_10_057 crossref_primary_10_1109_TFUZZ_2020_3009729 crossref_primary_10_1109_TSMC_2019_2895060 crossref_primary_10_1016_j_automatica_2023_111066 crossref_primary_10_1002_rnc_6276 crossref_primary_10_1177_01423312211037965 crossref_primary_10_1016_j_ins_2020_01_047 crossref_primary_10_1016_j_isatra_2022_02_001 crossref_primary_10_1080_00207721_2020_1862354 crossref_primary_10_1109_TCYB_2019_2917543 crossref_primary_10_1016_j_ins_2020_08_018 crossref_primary_10_1109_JIOT_2021_3138784 crossref_primary_10_1007_s11432_019_2694_y crossref_primary_10_1109_TCYB_2021_3071462 crossref_primary_10_1109_JSEN_2024_3369245 crossref_primary_10_1109_TCYB_2021_3131695 crossref_primary_10_1007_s11633_021_1306_z crossref_primary_10_1016_j_eswa_2022_119427 crossref_primary_10_1109_TCYB_2022_3151653 crossref_primary_10_1109_TCYB_2022_3208363 crossref_primary_10_1016_j_isatra_2022_12_012 crossref_primary_10_1109_LSP_2025_3565369 crossref_primary_10_1631_FITEE_2300625 crossref_primary_10_1002_rnc_4640 crossref_primary_10_1109_TCYB_2022_3142035 crossref_primary_10_1109_JAS_2021_1004060 crossref_primary_10_1016_j_neucom_2021_06_017 crossref_primary_10_1109_JAS_2022_105416 crossref_primary_10_1016_j_isatra_2022_02_037 crossref_primary_10_3390_s23020698 crossref_primary_10_1002_rnc_5997 crossref_primary_10_1109_TSP_2020_3042947 crossref_primary_10_3390_s20030627 crossref_primary_10_1016_j_ins_2020_08_007 crossref_primary_10_1007_s11071_024_09401_5 crossref_primary_10_1109_TCYB_2019_2954955 crossref_primary_10_1109_TCYB_2017_2771560 crossref_primary_10_1016_j_neucom_2018_08_087 crossref_primary_10_1016_j_fss_2019_11_005 crossref_primary_10_1109_ACCESS_2021_3097159 crossref_primary_10_1109_TASE_2025_3598062 crossref_primary_10_1109_TCYB_2022_3208012 crossref_primary_10_1109_TCNS_2022_3203365 crossref_primary_10_1016_j_automatica_2020_109128 crossref_primary_10_1109_TCYB_2019_2894294 crossref_primary_10_1088_1674_1056_acb91b crossref_primary_10_1016_j_jfranklin_2022_01_033 crossref_primary_10_1002_rnc_4672 crossref_primary_10_1016_j_automatica_2025_112530 crossref_primary_10_1016_j_ifacol_2022_07_253 crossref_primary_10_1016_j_inffus_2020_01_009 crossref_primary_10_1109_TSMC_2019_2956945 crossref_primary_10_1109_TSMC_2021_3102406 crossref_primary_10_1109_TSMC_2019_2896292 crossref_primary_10_1109_TSIPN_2022_3211172 crossref_primary_10_1109_TII_2020_2972033 crossref_primary_10_1007_s40435_021_00767_7 crossref_primary_10_1109_TCYB_2019_2921821 crossref_primary_10_1016_j_isatra_2022_07_023 crossref_primary_10_1109_TCYB_2018_2865499 crossref_primary_10_3390_e24050733 crossref_primary_10_1002_rnc_5867 crossref_primary_10_1016_j_ins_2018_02_039 crossref_primary_10_1016_j_jfranklin_2021_10_038 crossref_primary_10_1007_s11432_019_2693_x crossref_primary_10_1016_j_jfranklin_2020_02_037 crossref_primary_10_1080_00207721_2020_1814898 crossref_primary_10_1109_ACCESS_2019_2935534 crossref_primary_10_1109_ACCESS_2023_3234226 crossref_primary_10_1109_TSMC_2025_3558286 crossref_primary_10_1109_JSEN_2018_2859378 crossref_primary_10_1109_TSIPN_2021_3130435 crossref_primary_10_1080_00207721_2020_1797227 crossref_primary_10_1080_00207721_2023_2216274 crossref_primary_10_1016_j_jfranklin_2019_09_002 crossref_primary_10_1109_TASE_2024_3401740 crossref_primary_10_1109_TNSE_2023_3326611 crossref_primary_10_1016_j_isatra_2021_09_002 crossref_primary_10_1016_j_jfranklin_2025_108007 crossref_primary_10_1080_21642583_2022_2063203 crossref_primary_10_1002_asjc_2810 |
| Cites_doi | 10.1109/TII.2016.2607150 10.1016/j.ins.2015.07.047 10.1109/TIE.2017.2701778 10.1007/978-1-4612-0039-0 10.1016/j.automatica.2010.06.025 10.1109/TAC.2014.2366855 10.1016/j.sysconle.2014.05.001 10.1109/TCYB.2016.2570860 10.1049/iet-cta.2016.0781 10.1109/TIE.2015.2499169 10.1109/TSMCB.2009.2020436 10.1109/TSMC.2015.2435700 10.1109/TIE.2017.2652346 10.1109/TAC.2012.2206694 10.1016/j.ins.2014.08.047 10.1109/TAC.2011.2174666 10.1016/j.sigpro.2011.11.002 10.1016/S0098-1354(97)00261-5 10.1137/1038003 10.1049/iet-cta.2012.0732 10.1016/j.sigpro.2016.02.023 10.1109/TSMCA.2005.843383 10.1002/rnc.3598 10.1109/TMECH.2016.2632304 10.1109/TCSI.2011.2169912 10.1109/TSMCB.2012.2236647 10.1016/j.automatica.2009.04.011 10.1016/j.jfranklin.2013.02.024 10.1109/TNNLS.2015.2411734 10.1109/TSP.2007.908943 10.1109/MCOM.2002.1024422 10.1109/TCSI.2011.2112594 10.1109/TII.2015.2506545 10.1016/j.fss.2014.12.015 10.1016/j.automatica.2012.09.010 10.1109/TII.2012.2231085 10.1080/03081079.2014.883715 10.1109/9.935060 10.1080/03081079.2015.1106738 10.1109/TIE.2015.2504044 10.1109/TIE.2016.2613929 10.1109/TAC.2012.2190184 10.1109/TAC.2015.2390554 10.1016/j.epsr.2013.02.002 10.1109/TAC.2010.2057951 10.1016/j.automatica.2014.10.092 10.1109/TAC.2013.2256015 10.1080/00207721.2011.565135 10.1109/TCYB.2016.2582081 10.1109/TAC.2010.2042987 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| DBID | 97E RIA RIE AAYXX CITATION NPM 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TCYB.2017.2769722 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | Aerospace Database PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 2168-2275 |
| EndPage | 183 |
| ExternalDocumentID | 29990117 10_1109_TCYB_2017_2769722 8114330 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: Australian Research Council grantid: DP160103567 funderid: 10.13039/501100000923 |
| GroupedDBID | 0R~ 4.4 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM RIG 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c279t-c04eca6ecb50ad80cf3b39be9b358615a1a0446f503bf2959e314f43210248f93 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 425 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000454242300014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2267 2168-2275 |
| IngestDate | Wed Oct 01 13:41:59 EDT 2025 Sun Nov 09 06:47:39 EST 2025 Thu Jan 02 23:04:36 EST 2025 Tue Nov 18 22:28:57 EST 2025 Sat Nov 29 06:48:35 EST 2025 Wed Aug 27 03:03:37 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c279t-c04eca6ecb50ad80cf3b39be9b358615a1a0446f503bf2959e314f43210248f93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-7207-0716 0000-0002-9576-7401 0000-0003-0180-0897 |
| PMID | 29990117 |
| PQID | 2158902196 |
| PQPubID | 85422 |
| PageCount | 13 |
| ParticipantIDs | crossref_citationtrail_10_1109_TCYB_2017_2769722 pubmed_primary_29990117 proquest_journals_2158902196 proquest_miscellaneous_2068346067 crossref_primary_10_1109_TCYB_2017_2769722 ieee_primary_8114330 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-Jan. 2019-1-00 2019-Jan 20190101 |
| PublicationDateYYYYMMDD | 2019-01-01 |
| PublicationDate_xml | – month: 01 year: 2019 text: 2019-Jan. |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE transactions on cybernetics |
| PublicationTitleAbbrev | TCYB |
| PublicationTitleAlternate | IEEE Trans Cybern |
| PublicationYear | 2019 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 zhang (ref29) 2015; 51 ref15 ref14 ref52 ref11 ge (ref28) 2017 ref10 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 zhang (ref30) 2017; 47 ref40 ref35 ref34 ref37 ref36 ref31 ref33 ref32 ref2 ref1 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref27 |
| References_xml | – ident: ref26 doi: 10.1109/TII.2016.2607150 – ident: ref45 doi: 10.1016/j.ins.2015.07.047 – ident: ref35 doi: 10.1109/TIE.2017.2701778 – ident: ref48 doi: 10.1007/978-1-4612-0039-0 – ident: ref11 doi: 10.1016/j.automatica.2010.06.025 – ident: ref47 doi: 10.1109/TAC.2014.2366855 – ident: ref9 doi: 10.1016/j.sysconle.2014.05.001 – ident: ref46 doi: 10.1109/TCYB.2016.2570860 – ident: ref44 doi: 10.1049/iet-cta.2016.0781 – ident: ref14 doi: 10.1109/TIE.2015.2499169 – ident: ref24 doi: 10.1109/TSMCB.2009.2020436 – ident: ref13 doi: 10.1109/TSMC.2015.2435700 – ident: ref21 doi: 10.1109/TIE.2017.2652346 – ident: ref31 doi: 10.1109/TAC.2012.2206694 – ident: ref40 doi: 10.1016/j.ins.2014.08.047 – ident: ref34 doi: 10.1109/TAC.2011.2174666 – ident: ref52 doi: 10.1016/j.sigpro.2011.11.002 – ident: ref50 doi: 10.1016/S0098-1354(97)00261-5 – ident: ref49 doi: 10.1137/1038003 – ident: ref8 doi: 10.1049/iet-cta.2012.0732 – ident: ref15 doi: 10.1016/j.sigpro.2016.02.023 – ident: ref18 doi: 10.1109/TSMCA.2005.843383 – ident: ref39 doi: 10.1002/rnc.3598 – ident: ref20 doi: 10.1109/TMECH.2016.2632304 – ident: ref2 doi: 10.1109/TCSI.2011.2169912 – ident: ref7 doi: 10.1109/TSMCB.2012.2236647 – ident: ref19 doi: 10.1016/j.automatica.2009.04.011 – ident: ref25 doi: 10.1016/j.jfranklin.2013.02.024 – ident: ref33 doi: 10.1109/TNNLS.2015.2411734 – ident: ref5 doi: 10.1109/TSP.2007.908943 – ident: ref1 doi: 10.1109/MCOM.2002.1024422 – ident: ref10 doi: 10.1109/TCSI.2011.2112594 – ident: ref38 doi: 10.1109/TII.2015.2506545 – volume: 47 start-page: 1618 year: 2017 ident: ref30 article-title: Energy-efficient distributed filtering in sensor networks: A unified switched system approach publication-title: IEEE Trans Cybern – ident: ref37 doi: 10.1016/j.fss.2014.12.015 – ident: ref16 doi: 10.1016/j.automatica.2012.09.010 – ident: ref12 doi: 10.1109/TII.2012.2231085 – ident: ref51 doi: 10.1080/03081079.2014.883715 – ident: ref17 doi: 10.1109/9.935060 – ident: ref42 doi: 10.1080/03081079.2015.1106738 – ident: ref27 doi: 10.1109/TIE.2015.2504044 – ident: ref22 doi: 10.1109/TIE.2016.2613929 – year: 2017 ident: ref28 article-title: A survey on recent advances in distributed sampled-data cooperative control of multi-agent systems publication-title: Neurocomputing – ident: ref3 doi: 10.1109/TAC.2012.2190184 – ident: ref41 doi: 10.1109/TAC.2015.2390554 – ident: ref23 doi: 10.1016/j.epsr.2013.02.002 – ident: ref36 doi: 10.1109/TAC.2010.2057951 – volume: 51 start-page: 55 year: 2015 ident: ref29 article-title: Event-based $H_{\infty }$ filtering for sampled-data systems publication-title: Automatica doi: 10.1016/j.automatica.2014.10.092 – ident: ref32 doi: 10.1109/TAC.2013.2256015 – ident: ref6 doi: 10.1080/00207721.2011.565135 – ident: ref43 doi: 10.1109/TCYB.2016.2582081 – ident: ref4 doi: 10.1109/TAC.2010.2042987 |
| SSID | ssj0000816898 |
| Score | 2.6359272 |
| Snippet | This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 171 |
| SubjectTerms | Computational geometry Convexity Discrete time systems Distributed set-membership estimation dynamic event-triggered transmission scheme (ETS) Ellipsoids Estimation Kalman filters Noise measurement Optimization Packets (communication) Parameter estimation Pollution measurement recursive convex optimization Remote sensors Sensors Time-varying systems unknown-but-bounded (UBB) noise Wireless sensor networks wireless sensor networks (WSNs) |
| Title | A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks |
| URI | https://ieeexplore.ieee.org/document/8114330 https://www.ncbi.nlm.nih.gov/pubmed/29990117 https://www.proquest.com/docview/2158902196 https://www.proquest.com/docview/2068346067 |
| Volume | 49 |
| WOSCitedRecordID | wos000454242300014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 2168-2275 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816898 issn: 2168-2267 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB21FQcuQCkfKaUyEgdAuM3Gzjo-lnYrLixIXaTlFDnOuEIq2arZ5fcz43gjIQESN0sZJ1be2J7xzPgBvFZoTdChkV5XStIO3UrrtJVTq9FNnPVlGyLZhJnPq-XSftmB92MtDCLG5DM84WaM5bcrv-GjstOKjHfyv3dh1xgz1GqN5ymRQCJS3xbUkGRVmBTEnOT2dHH-7QPncZmTwkytKZjEhhZirrs0v-1IkWLl79Zm3HUuH_7feB_Bg2RdirNBHfZhB7vHsJ_mby_epEum3x7A5kxcDGT0YsYpj3JBbvo1E3eKuH0R_HyOJq4I1B8oyLQVF3zHLtNjkcwVruUnZDIRzvYSM1oohhpI8ZnmhuCc2htaQ0mu66nrfEg275_A18vZ4vyjTBQM0hfGrqXPNXo3Rd-UuWur3AfVKNugbVRZkTFEcHJEOJS5akJhS4tqooPmwqBCV8Gqp7DXrTp8DoJcl9wWzk3aUGjVGqdMi2ROFqHyGpuQQb6FofbpfnKmybipo5-S25pBrBnEOoGYwbuxy-1wOce_hA8YoVEwgZPB0RbrOk3fviY7iOOvtDpl8Gp8TH-eoymuw9WGZPJppTT5fyaDZ4OOjO_eqtbhn7_5Au7TyOxwknMEe-u7Db6Ee_7n-nt_d0zavayOo3b_Ahzn88c |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9QwDLbGQIIXYIwfhQFB4gEQ2dokvTSPY7tpiO1A2iGNp6pNHTRp66H1jr8fO-1VQgIk3iLVaaN-TmLHdj6A1xqdDSbU0ptCS9qhG-kq4-TEGayyyvm8CZFsws5mxfm5-7IB78daGESMyWe4y80Yy28WfsVHZXsFGe_kf9-Am7kxKuurtcYTlUghEclvFTUk2RV2CGNmqdubH3z7wJlcdlfZibOKaWxoKebKS_vbnhRJVv5ub8Z95-je_434Ptwd7Eux3yvEFmxg-wC2hhnciTfDNdNvt2G1Lw57Onox5aRHOSdH_TtTd4q4gZEC8EmaOCNYr1CQcSsO-ZZdJsgimTNcylNkOhHO9xJTWir6KkjxmWaH4KzaS1pFSa7tqOusTzfvHsLXo-n84FgOJAzSK-uW0qcGfTVBX-dp1RSpD7rWrkZX67wgc4gA5ZhwyFNdB-VyhzozwXBpkDJFcPoRbLaLFp-AIOcldaqqsiYooxtbadsgGZQqFN5gHRJI1zCUfrihnIkyLsvoqaSuZBBLBrEcQEzg3djlR389x7-EtxmhUXAAJ4GdNdblMIG7kiwhjsDS-pTAq_Ex_XmOp1QtLlYkk04KbcgDtAk87nVkfPdatZ7--Zsv4fbx_PSkPPk4-_QM7tAoXX-uswOby-sVPodb_ufyort-EXX8Fy_99iY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Dynamic+Event-Triggered+Transmission+Scheme+for+Distributed+Set-Membership+Estimation+Over+Wireless+Sensor+Networks&rft.jtitle=IEEE+transactions+on+cybernetics&rft.au=Ge%2C+Xiaohua&rft.au=Han%2C+Qing-Long&rft.au=Wang%2C+Zidong&rft.date=2019-01-01&rft.eissn=2168-2275&rft.volume=49&rft.issue=1&rft.spage=171&rft_id=info:doi/10.1109%2FTCYB.2017.2769722&rft_id=info%3Apmid%2F29990117&rft.externalDocID=29990117 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2267&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2267&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2267&client=summon |