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In view of the above challenges, in this paper, we propose a novel prediction method for the missing traffic flow data caused by abnormal sensors, named <jats:inline-formula><jats:alternatives><jats:tex-math>$$ASMVP_{distr-LSH}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>A<\/mml:mi>\n                    <mml:mi>S<\/mml:mi>\n                    <mml:mi>M<\/mml:mi>\n                    <mml:mi>V<\/mml:mi>\n                    <mml:msub>\n                      <mml:mi>P<\/mml:mi>\n                      <mml:mrow>\n                        <mml:mi>d<\/mml:mi>\n                        <mml:mi>i<\/mml:mi>\n                        <mml:mi>s<\/mml:mi>\n                        <mml:mi>t<\/mml:mi>\n                        <mml:mi>r<\/mml:mi>\n                        <mml:mo>-<\/mml:mo>\n                        <mml:mi>L<\/mml:mi>\n                        <mml:mi>S<\/mml:mi>\n                        <mml:mi>H<\/mml:mi>\n                      <\/mml:mrow>\n                    <\/mml:msub>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> based on distributed locality-sensitive hashing (LSH) technique. At last, a case study is presented to illustrate the feasibility and effectiveness of our approach <jats:inline-formula><jats:alternatives><jats:tex-math>$$ASMVP_{distr-LSH}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>A<\/mml:mi>\n                    <mml:mi>S<\/mml:mi>\n                    <mml:mi>M<\/mml:mi>\n                    <mml:mi>V<\/mml:mi>\n                    <mml:msub>\n                      <mml:mi>P<\/mml:mi>\n                      <mml:mrow>\n                        <mml:mi>d<\/mml:mi>\n                        <mml:mi>i<\/mml:mi>\n                        <mml:mi>s<\/mml:mi>\n                        <mml:mi>t<\/mml:mi>\n                        <mml:mi>r<\/mml:mi>\n                        <mml:mo>-<\/mml:mo>\n                        <mml:mi>L<\/mml:mi>\n                        <mml:mi>S<\/mml:mi>\n                        <mml:mi>H<\/mml:mi>\n                      <\/mml:mrow>\n                    <\/mml:msub>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>.<\/jats:p>","DOI":"10.1007\/s40747-023-00992-x","type":"journal-article","created":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T05:02:32Z","timestamp":1677733352000},"page":"5081-5091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["LSH-based missing value prediction for abnormal traffic sensors with privacy protection in edge computing"],"prefix":"10.1007","volume":"9","author":[{"given":"Ailing","family":"Gao","sequence":"first","affiliation":[]},{"given":"Xiaomei","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7493-4522","authenticated-orcid":false,"given":"Ying","family":"Miao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"issue":"10","key":"992_CR1","doi-asserted-by":"publisher","first-page":"2159044","DOI":"10.1142\/S0218001421590448","volume":"35","author":"Z Wang","year":"2021","unstructured":"Wang Z, Liu J, Shen S, Li M (2021) Restaurant recommendation in vehicle context based on prediction of traffic conditions. 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