{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:39:28Z","timestamp":1760243968069,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2009,1,23]],"date-time":"2009-01-23T00:00:00Z","timestamp":1232668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Measurement losses adversely affect the performance of target tracking. The sensor network\u2019s life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node\u2019s path. First, we assume that the mobile sink node\u2019s position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods\u2019 performance.<\/jats:p>","DOI":"10.3390\/s90100696","type":"journal-article","created":{"date-parts":[[2009,1,23]],"date-time":"2009-01-23T11:40:49Z","timestamp":1232710849000},"page":"696-716","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Energy Efficient Sensor Scheduling with a Mobile Sink Node for the Target Tracking Application"],"prefix":"10.3390","volume":"9","author":[{"given":"Suhinthan","family":"Maheswararajah","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Melbourne School of Engineering, University of Melbourne, Australia"}]},{"given":"Saman","family":"Halgamuge","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Melbourne School of Engineering, University of Melbourne, Australia"}]},{"given":"Malin","family":"Premaratne","sequence":"additional","affiliation":[{"name":"Advanced Computing and Simulation Laboratory, Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2009,1,23]]},"reference":[{"key":"ref_1","unstructured":"Mahfoudh, S., and Minet, P. Survey of Energy Efficient Strategies in Wireless Ad Hoc and Sensor Networks."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tang, X., and Xu, J. (2006, January April). Extending Network Lifetime for Precision-Constrained Data Aggregation in Wireless Sensor Networks. Barcelona, Spain.","DOI":"10.1109\/INFOCOM.2006.149"},{"key":"ref_3","unstructured":"Chiasserini, C.-F., and Garetto, M. (2004, January November). Modeling the performance of wireless sensor networks. Hong Kong, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MCOM.2002.1024422","article-title":"A survey on sensor networks","volume":"40","author":"Akyildiz","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, D., Kaliappan, V.K., Chung, D., and Min, D. (2008, January July). An energy efficient dynamic routing scheme for clustered sensor network using a ubiquitous robot, Ho Chi Minh.","DOI":"10.1109\/RIVF.2008.4586355"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/TPDS.2006.74","article-title":"Optimal transmission radius for energy efficient broadcasting protocols in ad hoc and sensor networks","volume":"17","author":"Ingelrest","year":"2006","journal-title":"IEEE T. Parall. Distr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1387663.1387666","article-title":"A distributed and self-organizing scheduling algorithm for energy-efficient data aggregation in wireless sensor networks","volume":"4","author":"Supriyo","year":"2008","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1109\/TPDS.2007.1110","article-title":"ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks","volume":"18","author":"Bugra","year":"2007","journal-title":"IEEE T. Parall. Distr."},{"key":"ref_9","unstructured":"Heinzelman, W.R., Chandrakasan, A., and Balakrishnan, H. Energy-efficient communication protocol for wireless microsensor networks."},{"key":"ref_10","unstructured":"Tirta, Y., Li, Zhiyuan, Lu, Y., and Bagchi, S. Efficient collection of sensor data in remote fields using mobile collectors."},{"key":"ref_11","unstructured":"Wu, Y., Zhang, L., Wu, Y., and Niu, Z. Interest dissemination with directional antennas for wireless sensor networks with mobile sinks. New York, NY, USA."},{"key":"ref_12","unstructured":"Qin, Y., Sun, D., Li, N, and Cen, Y. (2004, January August). Path planning for mobile robot using the particle swarm optimization with mutation operator. Shanghai, China."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Verma, A., Sawant, H., and Tan, J. (2005, January March). Selection and Navigation of Mobile Sensor Nodes Using a Sensor Network. Kauai Island, HI, USA.","DOI":"10.1016\/j.pmcj.2005.08.006"},{"key":"ref_14","unstructured":"Xiao, W., Xie, L., Lin, J., and Li, J. (, January June). Multi-Sensor Scheduling for Reliable Target Tracking in Wireless Sensor Networks. Chengdu, China."},{"key":"ref_15","unstructured":"Kreucher, C., Hero, A., Kastella, K., and Shapo, B. (2005, January June). Information-based Sensor Management for Simultaneous Multitarget Tracking and Identification. Lexington, MA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Maheswararajah, S., Halgamuge, S., and Premaratne, M. (2009). Sensor Scheduling For Target Tracking by Sub-optimal Algorithms. IEEE T. Veh. Technol., Accepted for publication.","DOI":"10.1109\/TVT.2008.927726"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4300","DOI":"10.1109\/TSP.2007.896099","article-title":"Approximate Dynamic Programming for Communication-Constrained Sensor Network Management","volume":"55","author":"Williams","year":"2007","journal-title":"IEEE T. Signal Proces."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.3390\/s7071193","article-title":"Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks","volume":"7","author":"Xue","year":"2007","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1109\/TAES.2004.1309993","article-title":"Multisensor resource deployment using posterior Cramer-Rao bounds","volume":"40","author":"Hernandez","year":"2004","journal-title":"IEEE T. Aero. Elec. Sys."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chhetri, A.S., Morrell, D., and Papandreou-Suppappola, A. (2005, January July). Energy efficient target tracking in a sensor network using non-myopic sensor scheduling. Philadelphia, USA.","DOI":"10.1109\/ICIF.2005.1591904"},{"key":"ref_21","unstructured":"Shah, H., and Morrell, D. (4,, January March). Non-myopic sensor scheduling for a distributed sensor network, Las Vegas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.automatica.2005.09.016","article-title":"On a Stochastic Sensor Selection Algorithm with Applications in Sensor Scheduling and Sensor Coverage","volume":"42","author":"Gupta","year":"2006","journal-title":"Automatica"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TAES.2007.4285352","article-title":"PCRLB-based multisensor array management for multitarget tracking","volume":"43","author":"Tharmarasa","year":"2007","journal-title":"IEEE T. Aero. Elec. Sys."},{"key":"ref_24","unstructured":"John, S.B., and Alain, B. (, January December). Sensor scheduling problems. Austin, Texas."},{"key":"ref_25","unstructured":"Bhardwaj, M., and Chandrakasan, A.P. (2002, January June). Bounding the lifetime of sensor networks via optimal role assignments. New York, USA."},{"key":"ref_26","unstructured":"Gupta, V., Chung, T., Hassibi, B., and Murray, R.M. (2004, January May). Sensor scheduling algorithms requiring limited computation [vehicle sonar range-finder example]. Montreal, CA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1109\/78.668800","article-title":"Posterior Cramer-Rao bounds for discrete-time nonlinear filtering","volume":"46","author":"Tichavsky","year":"1998","journal-title":"IEEE T. Signal Proces."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/TSP.2002.1003062","article-title":"Algorithms for optimal scheduling and management of hidden Markov model sensors","volume":"50","author":"Krishnamurthy","year":"2002","journal-title":"IEEE T. Signal Proces."},{"key":"ref_29","unstructured":"Evans, J., and Krishnamurthy, V. Optimal sensor scheduling for Hidden Markov models. Seattle, WA, USA."},{"key":"ref_30","unstructured":"Yaakov, B., Thiagalingam, K., and Rong, L. (2002). Estimation with Applications to Tracking and Navigation, John Wiley & Sons, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kalman, R.E. (1960). A New Approach to Linear Filtering and Prediction Problems. Trans. ASME, J. Basic Eng., 35\u201345.","DOI":"10.1115\/1.3662552"},{"key":"ref_32","unstructured":"Bertsekas, D.P. (2005). Dynamic Programming and Optimal Control, Athena Scientific. [third edition]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/9\/2\/696\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:09:45Z","timestamp":1760220585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/9\/2\/696"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,1,23]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2009,2]]}},"alternative-id":["s90100696"],"URL":"https:\/\/doi.org\/10.3390\/s90100696","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2009,1,23]]}}}