{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:06:33Z","timestamp":1760241993629,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,22]],"date-time":"2018-11-22T00:00:00Z","timestamp":1542844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673055","61773056"],"award-info":[{"award-number":["61673055","61773056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.<\/jats:p>","DOI":"10.3390\/s18124090","type":"journal-article","created":{"date-parts":[[2018,11,22]],"date-time":"2018-11-22T09:18:25Z","timestamp":1542878305000},"page":"4090","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Fen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing100083, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wendong","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing100083, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing100083, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengpeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing100083, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ma, J.J., Meng, F.S., Zhou, Y.X., Wang, Y.Y., and Shi, P. (2018). Distributed water pollution source localization with mobile UV-visible spectrometer probes in wireless sensor networks. Sensors, 18.","DOI":"10.3390\/s18020606"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1504\/IJSNET.2014.066788","article-title":"Maximum WSN coverage in environments of heterogeneous path loss","volume":"16","author":"Mortazavi","year":"2014","journal-title":"Int. J. Sens. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1002\/wcm.2601","article-title":"Feasibility and performance evaluation of a 6LoWPAN-enabled platform for ubiquitous healthcare monitoring","volume":"16","author":"Touati","year":"2016","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1016\/j.eswa.2015.01.016","article-title":"A novel multimodal communication framework using robot partner for aging population","volume":"42","author":"Tang","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1109\/TITS.2017.2727224","article-title":"Vehicle classification and speed estimation using combined passive infrared\/ultrasonic sensors","volume":"19","author":"Odat","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"295","DOI":"10.4236\/wsn.2011.38030","article-title":"Sensor scheduling algorithm target tracking-oriented","volume":"3","author":"Yan","year":"2011","journal-title":"Wirel. Sens. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, S., Liu, M., and Zhang, Q. (2017). An artificial measurements-based adaptive filter for energy-efficient target tracking via underwater wireless sensor networks. Sensors, 17.","DOI":"10.3390\/s17050971"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.rser.2012.11.052","article-title":"Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review","volume":"19","author":"Reisi","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","first-page":"1788","article-title":"Efficient study of a coarse structure number on the bluff body during the harvesting of wind energy","volume":"40","author":"Wang","year":"2018","journal-title":"Energy Sources Part A Recov. Util. Environ. Eff."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/JSEN.2014.2343932","article-title":"Thermal energy harvesting wireless sensor node in aluminum core PCB technology","volume":"15","author":"Prijic","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"35243","DOI":"10.1109\/ACCESS.2018.2851203","article-title":"Thermal energy harvesting WSNs node for temperature monitoring in IIoT","volume":"6","author":"Hou","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9348","DOI":"10.1109\/ACCESS.2017.2703847","article-title":"RF energy harvesting wireless powered sensor networks for smart cities","volume":"5","author":"Liu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.rser.2014.07.035","article-title":"Energizing wireless sensor networks by energy harvesting systems: Scopes, challenges and approaches","volume":"38","author":"Kausar","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_14","first-page":"25","article-title":"Advanced forecasting methods for global crisis warning and models of intelligence","volume":"22","author":"Werbos","year":"1977","journal-title":"Gen. Syst. Yearb."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1109\/TNNLS.2014.2306201","article-title":"Adaptive dynamic programming for a class of complex-valued nonlinear systems","volume":"25","author":"Song","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_16","first-page":"1","article-title":"A new self-learning optimal control laws for a class of discrete-time nonlinear systems based on ESN architecture","volume":"57","author":"Song","year":"2014","journal-title":"Sci. China Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1007\/s00500-013-1170-z","article-title":"Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems","volume":"18","author":"Song","year":"2014","journal-title":"Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1109\/TCYB.2015.2492242","article-title":"Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems","volume":"46","author":"Wei","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"303","DOI":"10.3724\/SP.J.1004.2013.00303","article-title":"An overview of research on adaptive dynamic programming","volume":"39","author":"Zhang","year":"2013","journal-title":"Acta Autom. Sin."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1007\/s00521-015-1954-4","article-title":"ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks","volume":"27","author":"Song","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_21","first-page":"922","article-title":"Sensor scheduling for target tracking in networks of active sensors","volume":"32","author":"Xiao","year":"2006","journal-title":"Acta Autom. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s11036-011-0311-9","article-title":"Distributed active sensor scheduling for target tracking in ultrasonic sensor networks","volume":"17","author":"Zhang","year":"2012","journal-title":"Mob. Netw. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/469076","article-title":"Energy-efficient node selection for target tracking in wireless sensor networks","volume":"2013","author":"Wang","year":"2013","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/JSTSP.2008.2001310","article-title":"Distributed object tracking using a cluster-based Kalman filter in wireless camera networks","volume":"2","author":"Medeiros","year":"2008","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3836","DOI":"10.1109\/TIE.2012.2208439","article-title":"A distributed TDMA scheduling algorithm for target tracking in ultrasonic sensor networks","volume":"60","author":"Cheng","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TMC.2012.44","article-title":"Probability-based prediction and sleep scheduling for energy-efficient target tracking in sensor networks","volume":"12","author":"Jiang","year":"2013","journal-title":"IEEE. Trans. Mob. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Madaan, A., Makki, S.K., Osborne, L., and Sun, B. (2010, January 5\u20137). An Intelligent Energy Efficient Target Tracking Scheme for Wireless Sensor Environment. Proceedings of the IEEE International Symposium on Wireless Pervasive Computing, Modena, Italy.","DOI":"10.1109\/ISWPC.2010.5483770"},{"key":"ref_28","unstructured":"Xiao, W.D., Xie, L.H., Chen, J.F., and Shue, L. (2006, January 14\u201319). Multi-Step Adaptive Sensor Scheduling for Target Tracking in Wireless Sensor Networks. Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Song, B., Xiao, W.D., and Zhang, Z.H. (2013, January 20\u201323). Multi-Step Sensor Scheduling for Energy-Efficient High-Accuracy Collaborative Target Tracking in Wireless Sensor Networks. Green Computing and Communications (GreenCom). Proceedings of the 2013 IEEE and Internet of Things (iThings\/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing, Beijing, China.","DOI":"10.1109\/GreenCom-iThings-CPSCom.2013.233"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1109\/TIM.2008.2005822","article-title":"Energy-efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks","volume":"58","author":"Lin","year":"2009","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s11768-010-9194-8","article-title":"Energy-efficient adaptive sensor scheduling for target tracking in wireless sensor networks","volume":"8","author":"Xiao","year":"2010","journal-title":"J. Control Theor. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TAC.2011.2175070","article-title":"Optimal pruning for multi-step sensor scheduling","volume":"57","author":"Huber","year":"2012","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/TVT.2008.927726","article-title":"Sensor scheduling for target tracking by suboptimal algorithms","volume":"58","author":"Maheswararajah","year":"2009","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1109\/TCYB.2014.2371232","article-title":"Node topology effect on target tracking based on UWSNs using quantized measurements","volume":"45","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TWC.2013.030413.121120","article-title":"A learning theoretic approach to energy harvesting communication system optimization","volume":"12","author":"Blasco","year":"2013","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1109\/JIOT.2018.2817590","article-title":"Lyapunov optimization for energy harvesting wireless sensor communications","volume":"5","author":"Qiu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2234","DOI":"10.1109\/JIOT.2018.2828943","article-title":"A dynamic programming algorithm for high-level task scheduling in energy harvesting IoT","volume":"5","author":"Caruso","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"650","DOI":"10.3724\/SP.J.1004.2009.00650","article-title":"On data-driven control theory: The state of the art and perspective","volume":"35","author":"Hou","year":"2009","journal-title":"Acta Autom. Sin."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.automatica.2016.10.026","article-title":"Switching LPV controller design under uncertain scheduling parameters","volume":"76","author":"Zhao","year":"2017","journal-title":"Automatica"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sato, M., and Peaucelle, D. (2018, January 21\u201324). A new method for gain-scheduled output feedback controller design using inexact scheduling parameters. Proceedings of the 2018 IEEE Conference on Control Technology and Applications, Copenhagen, Denmark.","DOI":"10.1109\/CCTA.2018.8511424"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1016\/j.automatica.2005.02.006","article-title":"Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes","volume":"41","author":"Lee","year":"2005","journal-title":"Automatica"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/TCST.2010.2093136","article-title":"A novel data-driven control approach for a class of discrete-time nonlinear systems","volume":"19","author":"Hou","year":"2011","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2160","DOI":"10.1049\/iet-cta.2016.0209","article-title":"Adaptive iterative learning reliable control for a class of non-linearly parameterised systems with unknown state delays and input saturation","volume":"10","author":"Ji","year":"2016","journal-title":"IET Control. Theor. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.isatra.2017.05.013","article-title":"Methodology to assess quality of estimated disturbances in active disturbance rejection control structure for mechanical system","volume":"70","author":"Rosas","year":"2017","journal-title":"ISA Trans."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.procs.2018.10.277","article-title":"Second order intelligent proportional-integral fuzzy control of twin rotor aerodynamic systems","volume":"139","author":"Roman","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","first-page":"208","article-title":"Model-free sliding mode and fuzzy controllers for reverse osmosis desalination plants","volume":"16","author":"Vrkalovic","year":"2018","journal-title":"Int. J. Artif. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TNN.2011.2168538","article-title":"Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method","volume":"22","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/TNNLS.2015.2464080","article-title":"Data-driven zero-sum neuro-optimal control for a class of continuous-time unknown nonlinear systems with disturbance using ADP","volume":"27","author":"Wei","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.neucom.2017.09.020","article-title":"Data-driven adaptive dynamic programming schemes for non-zero-sum games of unknown discrete-time nonlinear systems","volume":"275","author":"Jiang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1049\/el.2016.4756","article-title":"Data-driven approximate optimal tracking control schemes for unknown non-affine non-linear multi-player systems via adaptive dynamic programming","volume":"53","author":"Jiang","year":"2017","journal-title":"IET Electron. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5468","DOI":"10.1109\/TIE.2017.2674581","article-title":"Adaptive dynamic programming-based optimal control scheme for energy storage systems with solar renewable energy","volume":"64","author":"Wei","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4110","DOI":"10.1109\/TIE.2017.2650872","article-title":"Mixed iterative adaptive dynamic programming for optimal battery energy control in smart residential microgrids","volume":"64","author":"Wei","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xiao, W.D., Liu, F., and Zhang, J.J. (2015, January 10\u201314). Adaptive Dynamic Programming for Multi-Point Scheduling in Energy Harvesting Wireless Sensor Networks. Proceedings of the 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th International Conference on Autonomic and Trusted Computing and 2015 IEEE 15th International Conference on Scalable Computing and Communications and Its Associated Workshops, Beijing, China.","DOI":"10.1109\/UIC-ATC-ScalCom-CBDCom-IoP.2015.270"},{"key":"ref_54","first-page":"1","article-title":"Efficient sleep scheduling algorithm for target tracking in double-storage energy harvesting sensor networks","volume":"2016","author":"Chen","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1115\/1.3658902","article-title":"New results in linear filtering and prediction theory","volume":"83","author":"Kalman","year":"1961","journal-title":"J. Basic Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4090\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:31:24Z","timestamp":1760196684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4090"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,22]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["s18124090"],"URL":"https:\/\/doi.org\/10.3390\/s18124090","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,11,22]]}}}