{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:48:35Z","timestamp":1760240915142,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,29]],"date-time":"2019-10-29T00:00:00Z","timestamp":1572307200000},"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":["61771410"],"award-info":[{"award-number":["61771410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Innovation Fund Project by Southwest University of Science and Technology","award":["No. 19ycx0106"],"award-info":[{"award-number":["No. 19ycx0106"]}]},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province","award":["No. 2017RYY05, No. 2018RYJ03"],"award-info":[{"award-number":["No. 2017RYY05, No. 2018RYJ03"]}]},{"name":"Horizontal Project","award":["No. HX2017134, No. HX2018264, No. E10203788"],"award-info":[{"award-number":["No. HX2017134, No. HX2018264, No. E10203788"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There are a lot of redundant data in wireless sensor networks (WSNs). If these redundant data are processed and transmitted, the node energy consumption will be too fast and will affect the overall lifetime of the network. Data fusion technology compresses the sampled data to eliminate redundancy, which can effectively reduce the amount of data sent by the node and prolong the lifetime of the network. Due to the dynamic nature of WSNs, traditional data fusion techniques still have many problems. Compressed sensing (CS) theory has introduced new ideas to solve these problems for WSNs. Therefore, in this study we analyze the data fusion scheme and propose an algorithm that combines improved clustered (ICL) algorithm low energy adaptive clustering hierarchy (LEACH) and CS (ICL-LEACH-CS). First, we consider the factors of residual energy, distance, and compression ratio and use the improved clustered LEACH algorithm (ICL-LEACH) to elect the cluster head (CH) nodes. Second, the CH uses a Gaussian random observation matrix to perform linear compressed projection (LCP) on the cluster common (CM) node signal and compresses the N-dimensional signal into M-dimensional information. Then, the CH node compresses the data by using a CS algorithm to obtain a measured value and sends the measured value to the sink node. Finally, the sink node reconstructs the signal using a convex optimization method and uses a least squares algorithm to fuse the signal. The signal reconstruction optimization problem is modeled as an equivalent      \u2113 1     -norm problem. The simulation results show that, compared with other data fusion algorithms, the ICL-LEACH-CS algorithm effectively reduces the node\u2019s transmission while balancing the load between the nodes.<\/jats:p>","DOI":"10.3390\/s19214704","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T05:18:26Z","timestamp":1572499106000},"page":"4704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Research on Data Fusion Scheme for Wireless Sensor Networks with Combined Improved LEACH and Compressed Sensing"],"prefix":"10.3390","volume":"19","author":[{"given":"Yu","family":"Song","sequence":"first","affiliation":[{"name":"School of Information Engineering, South West University of Science and Technology, Mianyang 621010, China"},{"name":"Department of Network Information Management Center, Sichuan University of Science and Engineering, Zigong 643000, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, South West University of Science and Technology, Mianyang 621010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"He","sequence":"additional","affiliation":[{"name":"School of Information Engineering, South West University of Science and Technology, Mianyang 621010, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China"},{"name":"School of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, South West University of Science and Technology, Mianyang 621010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maschi, L., Pinto, A., Meneguette, R., and Baldassin, A. (2018). Data summarization in the node by parameters (DSNP): Local data fusion in an IoT environment. Sensors, 18.","DOI":"10.3390\/s18030799"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.3390\/s150202964","article-title":"A data fusion method in wireless sensor networks","volume":"15","author":"Izadi","year":"2015","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/ACCESS.2017.2666200","article-title":"A survey on software-defined wireless sensor networks: Challenges and design requirements","volume":"5","author":"Kobo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1109\/JSEN.2013.2240617","article-title":"A delay-aware network structure for wireless sensor networks with in-network data fusion","volume":"13","author":"Cheng","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","volume":"52","author":"Romberg","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_7","first-page":"1","article-title":"Reliable data fusion of hierarchical wireless sensor networks with asynchronous measurement for greenhouse monitoring","volume":"99","author":"Bai","year":"2018","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1109\/TC.2006.157","article-title":"Adaptive data fusion for energy efficient routing in wireless sensor networks","volume":"55","author":"Luo","year":"2006","journal-title":"IEEE Trans. Comput."},{"key":"ref_9","first-page":"14","article-title":"An energy-efficient fuzzy based data fusion and tree based clustering algorithm for wireless sensor networks","volume":"Volume 683","author":"Venkatesh","year":"2018","journal-title":"The International Symposium on Intelligent Systems Technologies and Applications"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Soltani, M., Hempel, M., and Sharif, H. (2014, January 10\u201314). Data fusion utilization for optimizing large-scale Wireless Sensor Networks. Proceedings of the 2014 IEEE International Conference on Communications (ICC), Sydney, NSW, Australia.","DOI":"10.1109\/ICC.2014.6883346"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, W., Xie, M., Liu, A., Zhao, M., Xiong, N., Zhao, M., and Dai, W. (2018). Differentiated data aggregation routing scheme for energy conserving and delay sensitive wireless sensor networks. Sensors, 18.","DOI":"10.3390\/s18072349"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/CC.2015.7112038","article-title":"A survey on the privacy-preserving data aggregation in wireless sensor networks","volume":"12","author":"Xu","year":"2015","journal-title":"China Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21174","DOI":"10.3390\/s141121174","article-title":"Privacy-preserving data aggregation in two-tiered wireless sensor networks with mobile nodes","volume":"14","author":"Yao","year":"2014","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vinodha, D., and Anita, E.A.M. (2017, January 23\u201324). A survey on privacy preserving data aggregation in wireless sensor networks. Proceedings of the 2017 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India.","DOI":"10.1109\/ICICES.2017.8070768"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4550","DOI":"10.1109\/JIOT.2018.2837048","article-title":"Inter-Community Detection Scheme for Social Internet of Things: A Compressive Sensing Over Graphs Approach","volume":"5","author":"Wang","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1109\/TGRS.2018.2856923","article-title":"Collaborative Compressive Radar Imaging with Saliency Priors","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/TSIPN.2018.2838038","article-title":"Joint sparsity pattern recovery with 1-bit compressive sensing in distributed sensor networks","volume":"5","author":"Gupta","year":"2019","journal-title":"IEEE Trans. Signal Inf. Process. Over Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/LSP.2015.2503804","article-title":"Dictionary Learning for Blind One Bit Compressed Sensing","volume":"23","author":"Zayyani","year":"2015","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/TWC.2002.804190","article-title":"An Application-Specific Protocol Architecture for Wireless Microsensor Networks","volume":"1","author":"Heinzelman","year":"2002","journal-title":"IEEE Trans Wirel. Commun."},{"key":"ref_20","first-page":"1","article-title":"Energy-Efficient Distributed Compressed Sensing Data Aggregation for Cluster-Based Underwater Acoustic Sensor Networks","volume":"2016","author":"Wang","year":"2016","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/TPDS.2013.90","article-title":"Transmission-Efficient Clustering Method for Wireless Sensor Networks Using Compressive Sensing","volume":"25","author":"Xie","year":"2014","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3950","DOI":"10.1109\/JSEN.2019.2893912","article-title":"An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs","volume":"19","author":"Wang","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_23","first-page":"1","article-title":"A kind of effective data aggregating method based on compressive sensing for wireless sensor network","volume":"159","author":"Zhang","year":"2018","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nguyen, M.T., and Teague, K.A. (2014, January 26\u201328). Compressive Sensing Based Data Gathering in Clustered Wireless Sensor Networks. Proceedings of the 2014 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina Del Rey, CA, USA.","DOI":"10.1109\/DCOSS.2014.11"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1007\/s11277-016-3757-z","article-title":"Performance Optimization Based on Compressive Sensing for Wireless Sensor Networks","volume":"95","author":"Ju","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3991\/ijoe.v12i08.5646","article-title":"Research on Data Fusion Technology Based on Compressed Sensing","volume":"12","author":"Tian","year":"2016","journal-title":"Int. J. Online Eng. (IJOE)"},{"key":"ref_27","unstructured":"Handy, M.J., Haase, M., and Timmermann, D. (2002, January 9\u201311). Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection. Proceedings of the 4th International Workshop on Mobile and Wireless Communications Network, Stockholm, Sweden."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yue, J., Zhang, W., Xiao, W., Tang, D., and Tang, J. (2011, January 23\u201325). A Novel Cluster-Based Data Fusion Algorithm for Wireless Sensor Networks. Proceedings of the 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China.","DOI":"10.1109\/wicom.2011.6040309"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7553","DOI":"10.1109\/ACCESS.2017.2696745","article-title":"Unbalanced Expander based Compressive Data Gathering in Clustered Wireless Sensor Networks","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wohwe Sambo, D., Yenke, B.O., F\u00f6rster, A., and Dayang, P. (2019). Optimized clustering algorithms for large wireless sensor networks: A review. Sensors, 19.","DOI":"10.3390\/s19020322"},{"key":"ref_31","first-page":"767","article-title":"Improving on LEACH Protocol of Wireless Sensor using Fuzzy Logic","volume":"3","author":"Ran","year":"2010","journal-title":"J. Inf. Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.7763\/IJMLC.2011.V1.12","article-title":"LEACH-GA: Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol for Wireless Sensor Networks","volume":"1","author":"Liu","year":"2011","journal-title":"Int. J. Mach. Learn. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4704\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:30:20Z","timestamp":1760189420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/21\/4704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,29]]},"references-count":32,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19214704"],"URL":"https:\/\/doi.org\/10.3390\/s19214704","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,10,29]]}}}