{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:40:25Z","timestamp":1761745225423,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:00:00Z","timestamp":1588636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS 1824440","CNS 1828363","CNS 1757533","CNS 1618398","CNS 1651947","CNS 1564128"],"award-info":[{"award-number":["CNS 1824440","CNS 1828363","CNS 1757533","CNS 1618398","CNS 1651947","CNS 1564128"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user\u2019s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model.<\/jats:p>","DOI":"10.3390\/jsan9020023","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T03:10:38Z","timestamp":1588821038000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Minimizing The Number of Channel Switches of Mobile Users in Cognitive Radio Ad-Hoc Networks"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3683-7051","authenticated-orcid":false,"given":"Rajorshi","family":"Biswas","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,5]]},"reference":[{"key":"ref_1","unstructured":"(2019, August 10). GeoLife GPS Trajectories. Available online: https:\/\/www.microsoft.com\/en-us\/download\/details.aspx?id=52367."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1002\/wcm.1017","article-title":"Channel status prediction for cognitive radio networks","volume":"12","author":"Tumuluru","year":"2012","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Qiu, R.C. (2010, January 18\u201321). Prediction of channel state for cognitive radio using higher-order hidden Markov model. Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), Charlotte-Concord, NC, USA.","DOI":"10.1109\/SECON.2010.5453870"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8044","DOI":"10.1109\/ACCESS.2016.2627243","article-title":"A Joint Tensor Completion and Prediction Scheme for Multi-Dimensional Spectrum Map Construction","volume":"4","author":"Tang","year":"2016","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Navabi, S., Wang, C., Bursalioglu, O.Y., and Papadopoulos, H. (2018, January 20\u201324). Predicting wireless channel features using neural networks. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422221"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T., Karra, K., and Clancy, T.C. (2017, January 25\u201328). Learning approximate neural estimators for wireless channel state information. Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan.","DOI":"10.1109\/MLSP.2017.8168144"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6853","DOI":"10.1109\/TVT.2015.2487047","article-title":"Analysis of spectrum occupancy using machine learning algorithms","volume":"65","author":"Azmat","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","unstructured":"Xue, H., and Gao, F. (2015, January 15\u201317). A machine learning based spectrum-sensing algorithm using sample covariance matrix. Proceedings of the 2015 10th International Conference on Communications and Networking in China (ChinaCom), Shanghai, China."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Uesugi, Y., Katagiri, K., Sato, K., Inage, K., and Fujii, T. (2019, January 22\u201325). Clustering of Signal Power Distribution toward Low Storage Crowdsourced Spectrum Database. Proceedings of the 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA.","DOI":"10.1109\/VTCFall.2019.8891469"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neucom.2017.05.101","article-title":"A hybrid Markov-based model for human mobility prediction","volume":"278","author":"Qiao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jeong, J., Leconte, M., and Proutiere, A. (2016, January 10\u201315). Cluster-aided mobility predictions. Proceedings of the IEEE INFOCOM 2016\u2014The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA.","DOI":"10.1109\/INFOCOM.2016.7524491"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1002\/dac.2649","article-title":"Extracting Mobility Pattern from Target Trajectory in Wireless Sensor Networks","volume":"28","author":"Misra","year":"2015","journal-title":"Int. J. Commun. Syst."},{"key":"ref_13","unstructured":"Iliadis, L., and Maglogiannis, I. (2016). On Learning Mobility Patterns in Cellular Networks. Artificial Intelligence Applications and Innovations, Springer International Publishing."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"(2013). Where to go from here? Mobility prediction from instantaneous information. Pervasive Mob. Comput., 9, 784\u2013797.","DOI":"10.1016\/j.pmcj.2013.07.006"},{"key":"ref_15","unstructured":"Satapathy, S.C., Raju, K.S., Mandal, J.K., and Bhateja, V. (2016). Minimizing Excessive Handover Using Optimized Cuckoo Algorithm in Heterogeneous Wireless Networks. Proceedings of the Second International Conference on Computer and Communication Technologies, Springer."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1007\/s11277-015-2345-y","article-title":"Minimizing Communication Interruptions Using Smart Proactive Channel Scanning Over IEEE 802.11 WLANs","volume":"82","author":"Tuysuz","year":"2015","journal-title":"Wirel. Pers. Commun."},{"key":"ref_17","unstructured":"(2019, August 11). WiGLE: Wireless Network Mapping. Available online: https:\/\/wigle.net\/."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/9\/2\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:52:31Z","timestamp":1760363551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/9\/2\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,5]]},"references-count":17,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["jsan9020023"],"URL":"https:\/\/doi.org\/10.3390\/jsan9020023","relation":{},"ISSN":["2224-2708"],"issn-type":[{"type":"electronic","value":"2224-2708"}],"subject":[],"published":{"date-parts":[[2020,5,5]]}}}