{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:46:46Z","timestamp":1760240806831,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T00:00:00Z","timestamp":1569283200000},"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":["61701074, 61772480, 61402425"],"award-info":[{"award-number":["61701074, 61772480, 61402425"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["G1323519020"],"award-info":[{"award-number":["G1323519020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate knowledge of network topology is vital for network monitoring and management. Network tomography can probe the underlying topologies of the intervening networks solely by sending and receiving packets between end hosts: the performance correlations of the end-to-end paths between each pair of end hosts can be mapped to the lengths of their shared paths, which could be further used to identify the interior nodes and links. However, such performance correlations are usually heavily affected by the time-varying cross-traffic, making it hard to keep the estimated lengths consistent during different measurement periods, i.e., once inconsistent measurements are collected, a biased inference of the network topology then will be yielded. In this paper, we prove conditions under which it is sufficient to identify the network topology accurately against the time-varying cross-traffic. Our insight is that even though the estimated length of the shared path between two paths might be \u201czoomed in or out\u201d by the cross-traffic, the network topology can still be recovered faithfully as long as we obtain the relative lengths of the shared paths between any three paths accurately.<\/jats:p>","DOI":"10.3390\/s19194125","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"4125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["General Identifiability Condition for Network Topology Monitoring with Network Tomography"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1904-6557","authenticated-orcid":false,"given":"Shengli","family":"Pan","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongwang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0843-2540","authenticated-orcid":false,"given":"Zhiyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cyberspace Security Key Laboratory of Sichuan Province &amp; Cyberspace Security Technology Laboratory of CETC, China Electronic Technology Cyber Security Co. LTD., Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3276-1202","authenticated-orcid":false,"given":"Deze","family":"Zeng","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Xu","sequence":"additional","affiliation":[{"name":"Cyberspace Security Key Laboratory of Sichuan Province &amp; Cyberspace Security Technology Laboratory of CETC, China Electronic Technology Cyber Security Co. LTD., Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihong","family":"Rao","sequence":"additional","affiliation":[{"name":"Cyberspace Security Key Laboratory of Sichuan Province &amp; Cyberspace Security Technology Laboratory of CETC, China Electronic Technology Cyber Security Co. LTD., Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Raposo, D., Rodrigues, A., Sinche, S., Sa Silva, J., and Boavida, F. (2018). Industrial IoT Monitoring: Technologies and Architecture Proposal. Sensors, 18.","DOI":"10.3390\/s18103568"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10970","DOI":"10.1109\/TVT.2018.2865951","article-title":"Dynamic contract incentive mechanism for cooperative wireless networks","volume":"83","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_3","unstructured":"Hogg, S. (2019, July 25). Raspberry Pi as a Network Monitoring Node. Available online: https:\/\/www.networkworld.com\/article\/2225683\/cisco-subnet-raspberry-pi-as-a-network-monitoring-node.html."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, J., and Yang, J. (2019). Maximizing Coverage Quality with Budget Constrained in Mobile Crowd-Sensing Network for Environmental Monitoring Applications. Sensors, 19.","DOI":"10.3390\/s19102399"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1109\/SURV.2011.081611.00040","article-title":"A survey on selective routing topology inference through active probing","volume":"14","author":"Zhang","year":"2011","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/COMST.2008.4564479","article-title":"Network topologies: Inference, modeling, and generation","volume":"10","author":"Haddadi","year":"2008","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Coates, M., Castro, R., Nowak, R., Gadhiok, M., King, R., and Tsang, Y. (2002, January 15\u201319). Maximum likelihood network topology identification from edge-based unicast measurements. Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and Modeling of Computer Systems, Marina Del Rey, CA, USA.","DOI":"10.1145\/511334.511337"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1109\/LCOMM.2014.2317743","article-title":"Topology Inference with Network Tomography Based on t-test","volume":"18","author":"Zhang","year":"2014","journal-title":"IEEE Commun. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TSP.2013.2254476","article-title":"A Binary Independent Component Analysis Approach to Tree Topology Inference","volume":"61","author":"Nguyen","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4594","DOI":"10.1109\/TIT.2017.2739779","article-title":"Finding the Right Tree: Topology Inference Despite Spatial Dependences","volume":"64","author":"Bowden","year":"2018","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/TNET.2004.838612","article-title":"Network tomography from measured end-to-end delay covariance","volume":"12","author":"Duffield","year":"2004","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7798","DOI":"10.1109\/TIT.2011.2168901","article-title":"Network tomography based on additive metrics","volume":"57","author":"Ni","year":"2011","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/TNET.2011.2175747","article-title":"Efficient Network Tomography for Internet Topology Discovery","volume":"20","author":"Erikson","year":"2012","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s12083-015-0380-9","article-title":"Taking a free ride for routing topology inference in peer-to-peer networks","volume":"9","author":"Qin","year":"2016","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1708","DOI":"10.1109\/TSP.2006.890830","article-title":"Hierarchical inference of unicast network topologies based on end-to-end measurements","volume":"55","author":"Shih","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jaggard, A., Kopparty, S., Ramachandran, V., and Wright, R.N. (2013, January 17\u201321). The design space of probing algorithms for network-performance measurement. Proceedings of the ACM SIGMETRICS\/International Conference on Measurement and Modeling of Computer Systems, Pittsburgh, PA, USA.","DOI":"10.1145\/2465529.2465765"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khan, A.A., Ghani, S., and Siddiqui, S. (2018). A Preemptive Priority-Based Data Fragmentation Scheme for Heterogeneous Traffic in Wireless Sensor Networks. Sensors, 18.","DOI":"10.3390\/s18124473"},{"key":"ref_18","unstructured":"Rabbat, M.G. (2003). Multiple-Source Network Tomography. [Ph.D. Thesis, Rice University]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/TSP.2014.2304431","article-title":"Active Learning of Multiple Source Multiple Destination Topologies","volume":"62","author":"Sattari","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Di Pietro, A., Ficara, D., Giordano, S., Oppedisano, F., Procissi, G., and Vitucci, F. (2009, January 14\u201318). Merging spanning trees in tomographic network topology discovery. Proceedings of the 2009 IEEE International Conference on Communications, Dresden, Germany.","DOI":"10.1109\/ICC.2009.5199178"},{"key":"ref_21","unstructured":"Ettehad, M., Duffield, N., and Berkolaiko, G. (2019). Optimizing Consistent Merging and Pruning of Subgraphs in Network Tomography. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"A39","DOI":"10.1190\/geo2017-0524.1","article-title":"Unsupervised seismic facies analysis via deep convolutional autoencoders","volume":"83","author":"Qian","year":"2018","journal-title":"Geophysics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4125\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:23:28Z","timestamp":1760189008000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,24]]},"references-count":22,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194125"],"URL":"https:\/\/doi.org\/10.3390\/s19194125","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,9,24]]}}}