MobiScan: an enhanced invisible screen‐camera communication system for IoT applications

Article


Zhang, X., Liu, J., Ba, Z., Tao, Y. and Cheng, X. 2022. MobiScan: an enhanced invisible screen‐camera communication system for IoT applications. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4151
TypeArticle
TitleMobiScan: an enhanced invisible screen‐camera communication system for IoT applications
AuthorsZhang, X., Liu, J., Ba, Z., Tao, Y. and Cheng, X.
Abstract

In recent years, dynamic and invisible screen-camera short-range communication for IoT devices have become a popular research field due to its superior user experience and no-extra hardware required. However, most existing screen-camera communication systems suffer from poor data security, high computational overhead, and limited capture angle, which make them infeasible in practice. In this work, we propose MobiScan, a dynamic and invisible screen-to-camera communication system that is able to ensure data security, real-time communication, and flexible capture angle. MobiScan is composed of two parts: a novel fast frame correction scheme and a multilevel data pattern scheme to address the above problems. The fast frame correction scheme proposes a novel frame correct approach that utilizes the sensor data on the smartphone to correct the captured frame by reproducing the relationship between positions of the screen and the smartphone. Through this method, the scheme can save frame correction time without calculating frame content to identify the degree of deformation. The multilevel data pattern scheme protects the data privacy by sending the classified information to targeted users, which can reduce the time overhead and extend the application scenario by redesigning the data structure. The experimental results show that for flexible capture angle problem, the maximum capture angle is 180° in the rotate view and 60° in the side view, bottom view, and overlook view. For real-time communication problem, the time overhead is reduced by 90% in the procedure of the frame correction. In the data decoding process, the time overhead is reduced by 10%. MobiScan enables a flexible capture angle on the PC platform for secure and real-time communication.

PublisherWiley
JournalTransactions on Emerging Telecommunications Technologies
ISSN2161-3915
Electronic2161-3915
Publication dates
Online15 Dec 2020
Print17 Apr 2022
Publication process dates
Deposited06 Jan 2021
Accepted29 Aug 2020
Submitted17 Jul 2020
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1002/ett.4151
Web of Science identifierWOS:000598688400001
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/8938q

  • 75
    total views
  • 0
    total downloads
  • 2
    views this month
  • 0
    downloads this month

Export as

Related outputs

Social and moral psychology of COVID-19 across 69 countries
Azevedo, F., Pavlović, T., Rêgo, G., Ay, F., Gjoneska, B., Etienne, T., Ross, R., Schönegger, P., Riaño-Moreno, J., Cichocka, A., Capraro, V., Cian, L., Longoni, C., Chan, H., Van Bavel, J., Sjåstad, H., Nezlek, J., Alfano, M., Gelfand, M., Birtel, M., Cislak, A., Lockwood, P., Abts, K., Agadullina, E., Aruta, J., Besharati, S., Bor, A., Choma, B., Crabtree, C., Cunningham, W., De, K., Ejaz, W., Elbaek, C., Findor, A., Flichtentrei, D., Franc, R., Gruber, J., Gualda, E., Horiuchi, Y., Huynh, T., Ibanez, A., Imran, M., Israelashvili, J., Jasko, K., Kantorowicz, J., Kantorowicz-Reznichenko, E., Krouwel, A., Laakasuo, M., Lamm, C., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Minda, J., Mitkidis, P., Olsson, A., Otterbring, T., Packer, D., Perry, A., Petersen, M., Puthillam, A., Rothmund, T., Santamaría-García, H., Schmid, P., Stoyanov, D., Tewari, S., Todosijević, B., Tsakiris, M., Tung, H., Umbres, R., Vanags, E., Vlasceanu, M., Vonasch, A., Yucel, M., Zhang, Y., Abad, M., Adler, E., Akrawi, N., Mdarhri, H., Amara, H., Amodio, D., Antazo, B., Apps, M., Ba, M., Barbosa, S., Bastian, B., Berg, A., Bernal-Zárate, M., Bernstein, M., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., Cameron, C., Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Daniels, M., Davis, V., Dayley, P., Delouvée, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Drosinou, M., Dulleck, U., Ekmanis, J., Ertan, A., Farhana, H., Farkhari, F., Farmer, H., Fenwick, A., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., Garcia-Navarro, E., Garladinne, P., Ghajjou, O., Gkinopoulos, T., Gray, K., Griffin, S., Gronfeldt, B., Gümren, M., Gurung, R., Halperin, E., Harris, E., Herzon, V., Hruška, M., Huang, G., Hudecek, M., Isler, O., Jangard, S., Jorgensen, F., Kachanoff, F., Kahn, J., Dangol, A., Keudel, O., Koppel, L., Koverola, M., Kubin, E., Kunnari, A., Kutiyski, Y., Laguna, O., Leota, J., Lermer, E., Levy, J., Levy, N., Li, C., Long, E., Maglić, M., McCashin, D., Metcalf, A., Mikloušić, I., El Mimouni, S., Miura, A., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nesh-Nash, T., Nitschke, J., Nurse, M., Ohtsubo, Y., de Mello, V., O’Madagain, C., Onderco, M., Palacios-Galvez, M., Palomöki, J., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Pavlović, Z., Payán-Gómez, C., Perander, S., Pitman, M., Prasad, R., Pyrkosz-Pacyna, J., Rathje, S., Raza, A., Rhee, K., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Santi, G., Santiago-Tovar, N., Savage, D., Scheffer, J., Schultner, D., Schutte, E., Scott, A., Sharma, M., Sharma, P., Skali, A., Stadelmann, D., Stafford, C., Stanojević, D., Stefaniak, A., Sternisko, A., Stoica, A., Stoyanova, K., Strickland, B., Sundvall, J., Thomas, J., Tinghög, G., Torgler, B., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Lange, P., van Prooijen, J., van Rooy, D., Västfjäll, D., Verkoeijen, P., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Wetter, E., Whillans, A., White, K., Habib, R., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R., Boggio, P. and Sampaio, W. 2023. Social and moral psychology of COVID-19 across 69 countries. Scientific Data. 10 (1), p. 272. https://doi.org/10.1038/s41597-023-02080-8
IHWC: intelligent hidden web crawler for harvesting data in urban domains
Kaur, S., Singh, A., Geetha, G. and Cheng, X. 2021. IHWC: intelligent hidden web crawler for harvesting data in urban domains. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-022-00839-x
A tamper-resistant broadcasting scheme for secure communication in internet of autonomous vehicles
Sun, J., Tao, J., Zhang, H., Zhao, Y., Nie, L., Cheng, X. and Zhang, T. 2023. A tamper-resistant broadcasting scheme for secure communication in internet of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems. 25 (3), pp. 2837-2846. https://doi.org/10.1109/TITS.2023.3265403
Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices
Bao, Y., Qiu, W. and Cheng, X. 2023. Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices. Expert Systems. 40 (6). https://doi.org/10.1111/exsy.13220
A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations
Bukhari, M., Yasmin, S., Habib, A., Cheng, X., Ullah, F., Yoo, J. and Lee, D. 2023. A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations. Journal of Healthcare Engineering. 2023 (1). https://doi.org/10.1155/2023/1847115
Fake news stance detection using selective features and FakeNET
Aljrees, T., Cheng, X., Ahmed, M., Umer, M., Majeed, R., Alnowaiser, K., Abuzinadah, N. and Ashraf, I. 2023. Fake news stance detection using selective features and FakeNET. PLoS ONE. 18 (7). https://doi.org/10.1371/journal.pone.0287298
PIGNUS: a deep learning model for IDS in industrial internet-of-things
Jayalaxmi, P., Saha, R., Kumar, G., Alazab, M., Conti, M. and Cheng, X. 2023. PIGNUS: a deep learning model for IDS in industrial internet-of-things. Computers and Security. 132. https://doi.org/10.1016/j.cose.2023.103315
Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution
Liu, Y., Wan, B., Shi, D. and Cheng, X. 2023. Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution. Remote Sensing. 15 (2). https://doi.org/10.3390/rs15020364
Cooperative conflict detection and resolution and safety assessment for 6G enabled unmanned aerial vehicles
Li, S., Cheng, X., Huang, X., Otaibi, S. and Wang, H. 2023. Cooperative conflict detection and resolution and safety assessment for 6G enabled unmanned aerial vehicles. IEEE Transactions on Intelligent Transportation Systems. 24 (2), pp. 2183-2198. https://doi.org/10.1109/TITS.2021.3137458
An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches
Mittal, M., Kobielnik, M., Gupta, S., Cheng, X. and Wozniak, M. 2022. An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches. Journal of Cloud Computing. 11 (1), pp. 1-21. https://doi.org/10.1186/s13677-022-00344-z
Efficient, revocable, and privacy-preserving fine-grained data sharing with keyword search for the cloud-assisted medical IoT system
Bao, Y., Qiu, W., Tang, P. and Cheng, X. 2022. Efficient, revocable, and privacy-preserving fine-grained data sharing with keyword search for the cloud-assisted medical IoT system. IEEE Journal of Biomedical and Health Informatics. 26 (5), pp. 2041-2051. https://doi.org/10.1109/JBHI.2021.3100871
Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network
Yin, X., Liu, J., Cheng, X. and Xiong, X. 2022. Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network. IEEE Transactions on Intelligent Transportation Systems. 25 (7), pp. 9840-9852. https://doi.org/10.1109/TITS.2021.3118701
A differentiated learning environment in domain model for learning disabled learners
Thapliyal, M., Ahuja, N., Shankar, A., Cheng, X. and Kumar, M. 2022. A differentiated learning environment in domain model for learning disabled learners. Journal of Computing in Higher Education. 34, pp. 60-82. https://doi.org/10.1007/s12528-021-09278-y
Offline signature verification using deep neural network with application to computer vision
Sharma, N., Gupta, S., Mehta, P., Cheng, X., Shankar, A., Singh, P. and Nayak, S. 2022. Offline signature verification using deep neural network with application to computer vision. Journal of Electronic Imaging (JEI). 31 (4). https://doi.org/10.1117/1.JEI.31.4.041210
Intrusion detection and prevention system for an IoT environment
Kumar, A., Abhishek, K., Ghalib, M., Shankar, A. and Cheng, X. 2022. Intrusion detection and prevention system for an IoT environment. Digital Communications and Networks. 8 (4), pp. 540-551. https://doi.org/10.1016/j.dcan.2022.05.027
An empirical study on Retinex methods for low-light image enhancement
Rasheed, M., Guo, G., Shi, D., Khan, H. and Cheng, X. 2022. An empirical study on Retinex methods for low-light image enhancement. Remote Sensing. 14 (18). https://doi.org/10.3390/rs14184608
A tagging SNP set method based on network community partition of linkage disequilibrium and node centrality
Wan, Q., Cheng, X., Zhang, Y., Lu, G., Wang, S. and He, S. 2022. A tagging SNP set method based on network community partition of linkage disequilibrium and node centrality. Current Bioinformatics. 17 (9), pp. 825-834. https://doi.org/10.2174/1574893617666220324155813
Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation
Ullah, F., Ullah, S., Naeem, M., Mostarda, L., Rho, S. and Cheng, X. 2022. Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation. Sensors. 22 (15), pp. 1-26. https://doi.org/10.3390/s22155883
A deep convolutional neural network stacked ensemble for malware threat classification in internet of things
Naeem, H., Cheng, X., Ullah, F., Jabbar, S. and Dong, S. 2022. A deep convolutional neural network stacked ensemble for malware threat classification in internet of things. Journal of Circuits, Systems and Computers. 31 (17). https://doi.org/10.1142/s0218126622503029
RFAP: a revocable fine-grained access control mechanism for autonomous vehicle platoon
Zhao, Y., Wang, Y., Cheng, X., Chen, H., Yu, H. and Ren, Y. 2022. RFAP: a revocable fine-grained access control mechanism for autonomous vehicle platoon. IEEE Transactions on Intelligent Transportation Systems. 23 (7), pp. 9668-9679. https://doi.org/10.1109/TITS.2021.3105458
Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Pavlović, T., Azevedo, F., De, K., Riaño-Moreno, J., Maglić, M., Gkinopoulos, T., Donnelly-Kehoe, P., Payán-Gómez, C., Huang, G., Kantorowicz, J., Birtel, M., Schönegger, P., Capraro, V., Santamaría-García, H., Yucel, M., Ibanez, A., Rathje, S., Wetter, E., Stanojević, D., van Prooijen, J., Hesse, E., Elbaek, C., Franc, R., Pavlović, Z., Mitkidis, P., Cichocka, A., Gelfand, M., Alfano, M., Ross, R., Sjåstad, H., Nezlek, J., Cislak, A., Lockwood, P., Abts, K., Agadullina, E., Amodio, D., Apps, M., Aruta, J., Besharati, S., Bor, A., Choma, B., Cunningham, W., Ejaz, W., Farmer, H., Findor, A., Gjoneska, B., Gualda, E., Huynh, T., Imran, M., Israelashvili, J., Kantorowicz-Reznichenko, E., Krouwel, A., Kutiyski, Y., Laakasuo, M., Lamm, C., Levy, J., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Olsson, A., Otterbring, T., Packer, D., Palomäki, J., Perry, A., Petersen, M., Puthillam, A., Rothmund, T., Schmid, P., Stadelmann, D., Stoica, A., Stoyanov, D., Stoyanova, K., Tewari, S., Todosijević, B., Torgler, B., Tsakiris, M., Tung, H., Umbreș, R., Vanags, E., Vlasceanu, M., Vonasch, A., Zhang, Y., Abad, M., Adler, E., Mdarhri, H., Antazo, B., Ay, F., Ba, M., Barbosa, S., Bastian, B., Berg, A., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., de Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cian, L., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Davis, V., Minda, J., Dayley, P., Delouvée, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Dulleck, U., Ekmanis, J., Etienne, T., Farhana, H., Farkhari, F., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., García-Navarro, E., Garladinne, P., Gray, K., Griffin, S., Gronfeldt, B., Gruber, J., Halperin, E., Herzon, V., Hruška, M., Hudecek, M., Isler, O., Jangard, S., Jørgensen, F., Keudel, O., Koppel, L., Koverola, M., Kunnari, A., Leota, J., Lermer, E., Li, C., Longoni, C., McCashin, D., Mikloušić, I., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nitschke, J., Nurse, M., de Mello, V., Palacios-Galvez, M., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Perander, S., Pitman, M., Raza, A., Rêgo, G., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Sampaio, W., Santi, G., Schultner, D., Schutte, E., Scott, A., Skali, A., Stefaniak, A., Sternisko, A., Strickland, B., Thomas, J., Tinghög, G., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Rooy, D., Västfjäll, D., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R., Boggio, P., Whillans, A., Van Lange, P., Prasad, R., Onderco, M., O'Madagain, C., Nesh-Nash, T., Laguna, O., Kubin, E., Gümren, M., Fenwick, A., Ertan, A., Bernstein, M., Amara, H. and Van Bavel, J. 2022. Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus. 1 (3), pp. 1-15. https://doi.org/10.1093/pnasnexus/pgac093
Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling
Wang, T., Wu, K., Du, T. and Cheng, X. 2022. Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling. Computational Intelligence. 38 (3), pp. 714-730. https://doi.org/10.1111/coin.12389
National identity predicts public health support during a global pandemic
Van Bavel, J., Cichocka, A., Capraro, V., Sjåstad, H., Nezlek, J., Pavlović, T., Alfano, M., Gelfand, M., Azevedo, F., Birtel, M., Cislak, A., Lockwood, P., Ross, R., Abts, K., Agadullina, E., Aruta, J., Besharati, S., Bor, A., Choma, B., Crabtree, C., Cunningham, W., De, K., Ejaz, W., Elbaek, C., Findor, A., Flichtentrei, D., Franc, R., Gjoneska, B., Gruber, J., Gualda, E., Horiuchi, Y., Huynh, T., Ibanez, A., Imran, M., Israelashvili, J., Jasko, K., Kantorowicz, J., Kantorowicz-Reznichenko, E., Krouwel, A., Laakasuo, M., Lamm, C., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Minda, J., Mitkidis, P., Olsson, A., Otterbring, T., Packer, D., Perry, A., Petersen, M., Puthillam, A., Riaño-Moreno, J., Rothmund, T., Santamaría-García, H., Schmid, P., Stoyanov, D., Tewari, S., Todosijević, B., Tsakiris, M., Tung, H., Umbreș, R., Vanags, E., Vlasceanu, M., Vonasch, A., Yucel, M., Zhang, Y., Abad, M., Adler, E., Akrawi, N., Mdarhri, H., Amara, H., Amodio, D., Antazo, B., Apps, M., Ay, F., Ba, M., Barbosa, S., Bastian, B., Berg, A., Bernal-Zárate, M., Bernstein, M., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., Cameron, C., Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cian, L., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Daniels, M., Davis, V., Dayley, P., Delouvee, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Drosinou, M., Dulleck, U., Ekmanis, J., Ertan, A., Etienne, T., Farhana, H., Farkhari, F., Farmer, H., Fenwick, A., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., Garcia-Navarro, E., Garladinne, P., Ghajjou, O., Gkinopoulos, T., Gray, K., Griffin, S., Gronfeldt, B., Gümren, M., Gurung, R., Halperin, E., Harris, E., Herzon, V., Hruška, M., Huang, G., Hudecek, M., Isler, O., Jangard, S., Jørgensen, F., Kachanoff, F., Kahn, J., Dangol, A., Keudel, O., Koppel, L., Koverola, M., Kubin, E., Kunnari, A., Kutiyski, Y., Laguna, O., Leota, J., Lermer, E., Levy, J., Levy, N., Li, C., Long, E., Longoni, C., Maglić, M., McCashin, D., Metcalf, A., Mikloušić, I., El Mimouni, S., Miura, A., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nesh-Nash, T., Nitschke, J., Nurse, M., Ohtsubo, Y., Oldemburgo de Mello, V., O’Madagain, C., Onderco, M., Palacios-Galvez, M., Palomäki, J., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Pavlović, Z., Payán-Gómez, C., Perander, S., Pitman, M., Prasad, R., Pyrkosz-Pacyna, J., Rathje, S., Raza, A., Rêgo, G., Rhee, K., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Sampaio, W., Santi, G., Santiago-Tovar, N., Savage, D., Scheffer, J., Schönegger, P., Schultner, D., Schutte, E., Scott, A., Sharma, M., Sharma, P., Skali, A., Stadelmann, D., Stafford, C., Stanojević, D., Stefaniak, A., Sternisko, A., Stoica, A., Stoyanova, K., Strickland, B., Sundvall, J., Thomas, J., Tinghög, G., Torgler, B., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Lange, P., van Prooijen, J., van Rooy, D., Västfjäll, D., Verkoeijen, P., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Wetter, E., Whillans, A., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R. and Boggio, P. 2022. National identity predicts public health support during a global pandemic. Nature Communications. 13 (1), pp. 1-14. https://doi.org/10.1038/s41467-021-27668-9
CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model
Ullah, F., Naeem, M., Naeem, H., Cheng, X. and Alazab, M. 2022. CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model. International Journal of Intelligent Systems. 37 (9), pp. 5768-5795. https://doi.org/10.1002/int.22813
Secure smart contracts for cloud-based manufacturing using Ethereum blockchain
Kumar, A., Abhishek, K., Nerurkar, P., Ghalib, M., Shankar, A. and Cheng, X. 2022. Secure smart contracts for cloud-based manufacturing using Ethereum blockchain. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4129
Editorial: Security of cloud service for the manufacturing industry
Cheng, X., Liu, Z. and Ning, Y. 2022. Editorial: Security of cloud service for the manufacturing industry. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4369
Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors
Li, S., Chen, Y., Wu, X., Cheng, X. and Tian, Z. 2021. Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors. Journal of Sensors. 2021, pp. 1-12. https://doi.org/10.1155/2021/8820413
Secure and energy-efficient smart building architecture with emerging technology IoT
Kumar, A., Sharma, S., Goyal, N., Singh, A., Cheng, X. and Singh, P. 2021. Secure and energy-efficient smart building architecture with emerging technology IoT. Computer Communications. 176, pp. 207-217. https://doi.org/10.1016/j.comcom.2021.06.003
Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment
Narayanan, S., Baby, C., Perumal, B., Bhatt, R., Cheng, X., Ghalib, M. and Shankar, A. 2021. Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment. International Journal of Intelligent Systems. 36 (8), pp. 4280-4267. https://doi.org/10.1002/int.22459
PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation
Wang, S., Zhang, Y., Cheng, X., Zhang, X. and Zhang, Y. 2021. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation. Computational and Mathematical Methods in Medicine. 2021, pp. 1-18. https://doi.org/10.1155/2021/6633755
Task bundling in worker-centric mobile crowdsensing
Zhao, T., Yang, Y., Wang, E., Mumtaz, S. and Cheng, X. 2021. Task bundling in worker-centric mobile crowdsensing. International Journal of Intelligent Systems. 36 (9), pp. 4936-4961. https://doi.org/10.1002/int.22497
ShadowFPE: new encrypted web application solution based on shadow DOM
Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-y
Learning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873
Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm
Liu, W., Huang, Y., Ye, Z., Cai, W., Yang, S., Cheng, X. and Frank, I. 2020. Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm. Applied Sciences. 10 (9). https://doi.org/10.3390/app10093225
Reliability analysis of an air traffic network: from network structure to transport function
Li, S., Zhang, Z. and Cheng, X. 2020. Reliability analysis of an air traffic network: from network structure to transport function. Applied Sciences. 10 (9). https://doi.org/10.3390/app10093168
XOR multiplexing technique for nanocomputers
Yu, L., Diao, M., Chen, X. and Cheng, X. 2020. XOR multiplexing technique for nanocomputers. Applied Sciences. 10 (8). https://doi.org/10.3390/app10082825
Fine-grained action recognition by motion saliency and mid-level patches
Liu, F., Zhao, L., Cheng, X., Dai, Q., Shi, X. and Qiao, J. 2020. Fine-grained action recognition by motion saliency and mid-level patches. Applied Sciences. 10 (8). https://doi.org/10.3390/app10082811
Adaptive dynamic disturbance strategy for differential evolution algorithm
Wang, T., Wu, K., Du, T. and Cheng, X. 2020. Adaptive dynamic disturbance strategy for differential evolution algorithm. Applied Sciences. 10 (6). https://doi.org/10.3390/app10061972
A game theoretic analysis of resource mining in blockchain
Singh, R., Dwivedi, A., Srivastava, G., Wisznieska-Mayszkiel, A. and Cheng, X. 2020. A game theoretic analysis of resource mining in blockchain. Cluster Computing. 23 (3), pp. 2035-2046. https://doi.org/10.1007/s10586-020-03046-w
Hybridization of cognitive computing for food services
Zhang, X., Yang, S., Srivastava, G., Chen, M. and Cheng, X. 2020. Hybridization of cognitive computing for food services. Applied Soft Computing. 89. https://doi.org/10.1016/j.asoc.2019.106051
Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network
Xie, X., Zhang, Z., Wang, J. and Cheng, X. 2019. Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network. Journal on Communications. 40 (8), pp. 143-150. https://doi.org/10.11959/j.issn.1000-436x.2019172
Incremental association rule mining based on matrix compression for edge computing
Zhou, D., Ouyang, M., Kuang, Z., Li, Z., Zhou, J. and Cheng, X. 2019. Incremental association rule mining based on matrix compression for edge computing. IEEE Access. 7, pp. 1730444-173053. https://doi.org/10.1109/ACCESS.2019.2956823
Facial landmark detection via attention-adaptive deep network
Sadiq, M., Shi, D., Guo, M. and Cheng, X. 2019. Facial landmark detection via attention-adaptive deep network. IEEE Access. 7, pp. 181041-181050. https://doi.org/10.1109/ACCESS.2019.2955156
Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha
Yhang, X., Mohanty, S., Parida, A., Pani, S., Dong, B. and Cheng, X. 2020. Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access. 8, pp. 30223-30233. https://doi.org/10.1109/ACCESS.2020.2972435
A sparse Bayesian learning method for structural equation model-based gene regulatory network inference
Li, Y., Liu, D., Chu, J., Zhu, Y., Liu, J. and Cheng, X. 2020. A sparse Bayesian learning method for structural equation model-based gene regulatory network inference. IEEE Access. 8, pp. 40067-40080. https://doi.org/10.1109/ACCESS.2020.2976743
Micro-distortion detection of lidar scanning signals based on geometric analysis
Liu, S., Chen, X., Li, Y. and Cheng, X. 2019. Micro-distortion detection of lidar scanning signals based on geometric analysis. Symmetry. 11 (12), pp. 2-13. https://doi.org/10.3390/sym11121471
Verifying cryptographic protocols
Ma, X. and Cheng, X. 2005. Verifying cryptographic protocols. IEEE Journal of Intelligent Cybernetic Systems.
Verifying security protocols by knowledge analysis
Ma, X. and Cheng, X. 2008. Verifying security protocols by knowledge analysis. International Journal of Security and Networks. 3 (3), pp. 183-192. https://doi.org/10.1504/IJSN.2008.020092
A face recognition algorithm using a fusion method based on Adaboost Bidirectional 2DLDA
Wang, S., Li, W., Cheng, X., Wang, Y. and Jiang, Y. 2012. A face recognition algorithm using a fusion method based on Adaboost Bidirectional 2DLDA. Advances in Information Sciences and Service Sciences. 4 (23), pp. 181-188. https://doi.org/10.4156/AISS.vol4.issue23.23
A security design for cloud computing: an implementation of an on premises authentication with Kerberos and IPSec within a network
Umar, M. and Cheng, X. 2012. A security design for cloud computing: an implementation of an on premises authentication with Kerberos and IPSec within a network. International Journal of Advanced Research in Computer Science. 3 (1), pp. 10-16. https://doi.org/10.26483/ijarcs.v3i1.6040
Comparative experiments on resource discovery in P2P networks
Gautam, S. and Cheng, X. 2014. Comparative experiments on resource discovery in P2P networks. Journal of Next Generation Information Technology. 5 (1), pp. 89-98.
Unbalanced private set intersection cardinality protocol with low communication cost
Lv, S., Ye, J., Yin, S. and Cheng, X. 2020. Unbalanced private set intersection cardinality protocol with low communication cost. Future Generation Computer Systems. 102, pp. 1054-1061. https://doi.org/10.1016/j.future.2019.09.022
Finding sands in the eyes: vulnerabilities discovery in IoT with EUFuzzer on human machine interface
Men, J., Xu, G., Han, Z., Sun, Z., Zhou, X., Lian, W. and Cheng, X. 2019. Finding sands in the eyes: vulnerabilities discovery in IoT with EUFuzzer on human machine interface. IEEE Access. 7, pp. 103751-103759. https://doi.org/10.1109/ACCESS.2019.2931061
Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs
Shi, F., Chen, Z. and Cheng, X. 2020. Behavior modelling and individual recognition of sonar transmitter for secure communication in UASNs. IEEE Access. 8, pp. 2447-2454. https://doi.org/10.1109/ACCESS.2019.2923059
Introduction of key problems in long-distance learning and training
Liu, S., Li, Z., Zhang, Y. and Cheng, X. 2019. Introduction of key problems in long-distance learning and training. Mobile Networks and Applications. 24 (1), pp. 1-4. https://doi.org/10.1007/s11036-018-1136-6
Vulnerabilities and limitations of MQTT protocol used between IoT devices
Dinculeană, D. and Cheng, X. 2019. Vulnerabilities and limitations of MQTT protocol used between IoT devices. Applied Sciences. 9 (5). https://doi.org/10.3390/app9050848
Imbalanced big data classification based on virtual reality in cloud computing
Xie, W. and Cheng, X. 2020. Imbalanced big data classification based on virtual reality in cloud computing. Multimedia Tools and Applications. 79 (23-24), pp. 16403-16420. https://doi.org/10.1007/s11042-019-7317-x
Platform of quality evaluation system for multimedia video communication based NS2
Yu, G., Xu, J. and Cheng, X. 2018. Platform of quality evaluation system for multimedia video communication based NS2. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-1164-x
An authentication scheme to defend against UDP DrDoS attacks in 5G networks
Huang, H., Hu, L., Chu, J. and Cheng, X. 2019. An authentication scheme to defend against UDP DrDoS attacks in 5G networks. IEEE Access. 7, pp. 175970-175979. https://doi.org/10.1109/ACCESS.2019.2957565
Data provenance with retention of reference relations
Wang, C., Yang, L., Wu, Y., Wu, Y., Cheng, X., Li, Z. and Liu, Z. 2018. Data provenance with retention of reference relations. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2876879
Editorial: Recent advances of content understanding in image and multimedia
Liu, S., Cheng, X. and Min, G. 2017. Editorial: Recent advances of content understanding in image and multimedia. Recent Patents on Computer Science. 10 (1), pp. 2-5. https://doi.org/10.2174/221327591001170808093310
Channel state information-based detection of Sybil attacks in wireless networks
Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z. and Cheng, X. 2018. Channel state information-based detection of Sybil attacks in wireless networks. Journal of Internet Services and Information Security. 8 (1), pp. 2-17. https://doi.org/10.22667/JISIS.2018.02.28.002
Research on trust model in container-based cloud service
Xie, X., Yuan, T., Zhou, X. and Cheng, X. 2018. Research on trust model in container-based cloud service. Computers, Materials and Continua. 56 (2), pp. 273-283. https://doi.org/10.3970/cmc.2018.03587
Introduction of recent advanced hybrid information processing
Liu, S., Li, Z., Cheng, X. and Lin, Y. 2018. Introduction of recent advanced hybrid information processing. Mobile Networks and Applications. 23 (4), pp. 673-676. https://doi.org/10.1007/s11036-018-1013-3
Accurate Sybil attack detection based on fine-grained physical channel information
Wang, C., Zhu, L., Gong, L., Zhao, Z., Yang, L., Liu, Z. and Cheng, X. 2018. Accurate Sybil attack detection based on fine-grained physical channel information. Sensors. 18 (3). https://doi.org/10.3390/s18030878
DivORAM: Towards a practical oblivious RAM with variable block size
Liu, Z., Huang, Y., Li, J., Cheng, X. and Shen, C. 2018. DivORAM: Towards a practical oblivious RAM with variable block size. Information Sciences. 447, pp. 1-11. https://doi.org/10.1016/j.ins.2018.02.071
M-SSE: an effective searchable symmetric encryption with enhanced security for mobile devices
Gao, C., Lv, S., Wei, Y., Wang, Z., Liu, Z. and Cheng, X. 2018. M-SSE: an effective searchable symmetric encryption with enhanced security for mobile devices. IEEE Access. 6, pp. 38860-38869. https://doi.org/10.1109/ACCESS.2018.2852329
A distributed anomaly detection system for in-vehicle network using HTM
Wang, C., Zhao, Z., Gong, L., Zhu, L., Liu, Z. and Cheng, X. 2018. A distributed anomaly detection system for in-vehicle network using HTM. IEEE Access. 6, pp. 9091-9098. https://doi.org/10.1109/ACCESS.2018.2799210
Crime pattern recognition based on high-performance computing
Eissa, A., Cheng, X. and Petridis, M. 2018. Crime pattern recognition based on high-performance computing. 2017 International Conference Next Generation Community Policing. Heraklion, Crete, Greece 25 - 27 Oct 2017
A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface
Liu, S., Pan, Z. and Cheng, X. 2017. A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals. 25 (04), pp. 1740004-1-11. https://doi.org/10.1142/s0218348x17400047
Degradation and encryption for outsourced PNG images in cloud storage
Wang, Y., Du, J., Cheng, X., Liu, Z. and Lin, K. 2016. Degradation and encryption for outsourced PNG images in cloud storage. International Journal of Grid and Utility Computing. 7 (1), pp. 22-28. https://doi.org/10.1504/IJGUC.2016.073773
Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud
Xie, X., Liu, R., Cheng, X., Hu, X. and Ni, J. 2016. Trust-Driven and PSO-SFLA based job scheduling algorithm on Cloud. Intelligent Automation & Soft Computing: An International Journal . 22 (4), pp. 561-566.
Numeric characteristics of generalized M-set with its asymptote
Liu, S., Cheng, X., Fu, W., Zhou, Y. and Li, Q. 2014. Numeric characteristics of generalized M-set with its asymptote. Applied Mathematics and Computation. 243, pp. 767-774. https://doi.org/10.1016/j.amc.2014.06.016
Local semantic indexing for resource discovery on overlay network using mobile agents
Singh, M., Cheng, X. and Belavkin, R. 2014. Local semantic indexing for resource discovery on overlay network using mobile agents. International Journal of Computational Intelligence Systems. 7 (3), pp. 432-455. https://doi.org/10.1080/18756891.2013.856257
Fractal property of generalized M-set with rational number exponent
Liu, S., Cheng, X., Lan, C., Fu, W., Zhou, J., Li, Q. and Gao, G. 2013. Fractal property of generalized M-set with rational number exponent. Applied Mathematics and Computation. 220, pp. 668-675. https://doi.org/10.1016/j.amc.2013.06.096
Mechanical verification of cryptographic protocols
Cheng, X., Ma, X., Huang, S. and Cheng, M. 2010. Mechanical verification of cryptographic protocols. Network Security. https://doi.org/10.1007/978-0-387-73821-5_5
DNSsec in Isabelle – replay attack and origin authentication
Kammueller, F., Kirsal-Ever, Y. and Cheng, X. 2013. DNSsec in Isabelle – replay attack and origin authentication. SMC 2013: IEEE International Conference on Systems, Man, and Cybernetics. Manchester, UK 13 - 16 Oct 2013 IEEE. pp. 4772-4777 https://doi.org/10.1109/SMC.2013.812
A cooperative particle swarm optimizer with statistical variable interdependence learning
Sun, L., Yoshida, S., Cheng, X. and Liang, Y. 2012. A cooperative particle swarm optimizer with statistical variable interdependence learning. Information Sciences. 186 (1), pp. 20-39. https://doi.org/10.1016/j.ins.2011.09.033
Survey of grid resource monitoring and prediction strategies.
Hu, L., Cheng, X. and Che, X. 2010. Survey of grid resource monitoring and prediction strategies. International Journal of Intelligent Information Processing. 1 (2).
Efficient identity-based broadcast encryption without random oracles.
Hu, L., Liu, Z. and Cheng, X. 2010. Efficient identity-based broadcast encryption without random oracles. Journal of Computers. 5 (3), pp. 331-336.
Solving job shop scheduling problem using genetic algorithm with penalty function
Sun, L., Cheng, X. and Liang, Y. 2010. Solving job shop scheduling problem using genetic algorithm with penalty function. International Journal of Intelligent Information Processing. 1 (2), pp. 65-77.
Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization
Cheng, X., Che, X. and Hu, L. 2010. Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization. International Journal of Computational Intelligence Systems. 3 (1), pp. 70-83. https://doi.org/10.2991/ijcis.2010.3.1.7
Resource discovery using mobile agents
Singh, M., Cheng, X. and Belavkin, R. 2010. Resource discovery using mobile agents. Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference. Changchun, Jilin Province 18 - 22 Aug 2010 IEEE. pp. 72 -77 https://doi.org/10.1109/FCST.2010.93
Resource discovery using mobile agents
Singh, M., Cheng, X. and He, X. 2009. Resource discovery using mobile agents. in: Tao, D., Xu, D. and Li, X. (ed.) Semantic Mining Technologies for Multimedia Databases. New York, USA Information Science Reference. pp. 419-448
New e-Learning system architecture based on knowledge engineering technology
Li, Y., Chen, Z., Huang, R. and Cheng, X. 2009. New e-Learning system architecture based on knowledge engineering technology. 2009 IEEE International Conference on Systems, Man and Cybernetics. San Antonio, TX, USA 11 - 14 Oct 2009 IEEE. pp. 5140-5144 https://doi.org/10.1109/ICSMC.2009.5346013
Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals
Li, Y., Guo, H., Gao, G., Huang, R. and Cheng, X. 2009. Ubiquitous e-learning System for dynamic mini-courseware assembling and delivering to mobile terminals. in: Kim, J., Delen, D., Jinsoo, P., Ko, F., Rui, C., Hyung, J., Lee, W. and Kou, G. (ed.) NCM 2009: Fifth International Joint Conference on INC, IMS, and IDC; [proceedings]. IEEE. pp. 1081-1086
Formal verification of the merchant registration phase of the SET protocol.
Cheng, X. and Ma, X. 2005. Formal verification of the merchant registration phase of the SET protocol. International Journal of Automation and Computing. 2 (2), pp. 155-162. https://doi.org/10.1007/s11633-005-0155-5
Programming style based program partition
Li, Y., Yang, H., Cheng, X. and Zhu, X. 2005. Programming style based program partition. International Journal of Software Engineering and Knowledge Engineering. 15 (6), pp. 1027-1061. https://doi.org/10.1142/S0218194005002610
An improved model-based method to test circuit faults
Cheng, X., Ouyang, D., Yunfei, J. and Zhang, C. 2005. An improved model-based method to test circuit faults. Theoretical Computer Science. 341 (1-3), pp. 150-161. https://doi.org/10.1016/j.tcs.2005.04.004
Topology control of ad hoc wireless networks for energy efficiency
Cheng, M., Cardei, M., Sun, J., Cheng, X., Wang, L., Xu, Y. and Du, D. 2004. Topology control of ad hoc wireless networks for energy efficiency. IEEE Transactions on Computers. 53 (12), pp. 1629-1635. https://doi.org/10.1109/TC.2004.121