{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:44:36Z","timestamp":1760233476758,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T00:00:00Z","timestamp":1610668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National  Science  Centre  in  Poland","award":["2016\/23\/B\/ST7\/03937"],"award-info":[{"award-number":["2016\/23\/B\/ST7\/03937"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure and modes of operation of such an ultra-dense network of neurons and glial cells, the authors selected the most worthwhile when planning a dense wireless network. These ideas were captured, modeled in the context of wireless data transmission. The performance of such an approach have been analyzed in two ways, first, the theoretic limits of such an approach has been derived based on the stochastic geometry, in particular\u2014based on the percolation theory. Additionally, computer experiments have been carried out to verify the performance of the proposed transmission schemes in four simulation scenarios. Achieved results showed the prospective improvement of the reliability of the wireless networks while applying proposed bio-inspired solutions and keeping the transmission extremely simple.<\/jats:p>","DOI":"10.3390\/s21020576","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:34:25Z","timestamp":1611113665000},"page":"576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Brain-Inspired Data Transmission in Dense Wireless Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3434-1917","authenticated-orcid":false,"given":"\u0141ukasz","family":"Ku\u0142acz","sequence":"first","affiliation":[{"name":"Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6766-7836","authenticated-orcid":false,"given":"Adrian","family":"Kliks","sequence":"additional","affiliation":[{"name":"Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Naeem, M.K., Abozariba, R., Asaduzzaman, M., and Patwary, M. (September, January 31). Towards the Mobility Issues of 5G-NOMA through User Dissociation and Re-association Control. Proceedings of the 2020 IEEE 21st International Symposium on \u201cA World of Wireless, Mobile and Multimedia Networks\u201d (WoWMoM), Cork, Ireland.","DOI":"10.1109\/WoWMoM49955.2020.00078"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Okasaka, S., Weiler, R.J., Keusgen, W., Pudeyev, A., Maltsev, A., Karls, I., and Sakaguchi, K. (2016). Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular. Sensors, 16.","DOI":"10.3390\/s16091362"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MWC.2011.5876496","article-title":"A survey on 3GPP heterogeneous networks","volume":"18","author":"Damnjanovic","year":"2011","journal-title":"IEEE Wirel. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/COMST.2016.2532458","article-title":"Next Generation 5G Wireless Networks: A Comprehensive Survey","volume":"18","author":"Agiwal","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4784","DOI":"10.1109\/TSP.2015.2446440","article-title":"Performance Analysis of Cloud Radio Access Networks With Distributed Multiple Antenna Remote Radio Heads","volume":"63","author":"Khan","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2522","DOI":"10.1109\/COMST.2016.2571730","article-title":"Ultra-Dense Networks: A survey","volume":"18","author":"Kamel","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, P., and Gao, S. (2016). Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks. Sensors, 16.","DOI":"10.3390\/s16091475"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1109\/TCCN.2020.2992628","article-title":"Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks","volume":"6","author":"Lei","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_9","unstructured":"Bullock, J., Boyle, J., and Wang, M.B. (2001). Physiology, Lippincott Williams & Wilkins."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.cmet.2011.08.016","article-title":"Brain energy metabolism: Focus on astrocyte-neuron metabolic cooperation","volume":"14","author":"Belanger","year":"2011","journal-title":"Cell Metab."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1109\/TSMCC.2010.2090141","article-title":"Biologically Inspired Network Systems: A Review and Future Prospects","volume":"41","author":"Nakano","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/MCOM.2010.5621985","article-title":"Bio-inspired networking: From theory to practice","volume":"48","author":"Dressler","year":"2010","journal-title":"IEEE Commun. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ku\u0142acz, L., and Kliks, A. (2020, January 5\u20138). Reliability of Bio-Inspired Ultra-Dense Networks. Proceedings of the 2020 Baltic URSI Symposium (URSI), Warsaw, Poland.","DOI":"10.23919\/URSI48707.2020.9254015"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Do, D.-T., Nguyen, T.-T.T., Le, C.-B., and Lee, J.W. (2020). Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications. Sensors, 20.","DOI":"10.3390\/s20020386"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, X., Guo, H., Wang, X., Wang, X., Wang, C., Guizani, M., and Du, X. (2020). A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs. Sensors, 20.","DOI":"10.3390\/s20216370"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, Y., Yang, W., Xiang, Z., Wang, B., and Cai, Y. (2019). On the Performance of Random Cognitive mmWave Sensor Networks. Sensors, 19.","DOI":"10.3390\/s19143184"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1109\/JSAC.2009.090902","article-title":"Stochastic geometry and random graphs for the analysis and design of wireless networks","volume":"27","author":"Haenggi","year":"2009","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kulacz, L., and Kliks, A. (September, January 31). Reliability Assessment of Bio-Inspired Ultra-Dense Networks Using Percolation Theory. Proceedings of the 2020 IEEE 21st International Symposium on \u201cA World of Wireless, Mobile and Multimedia Networks\u201d (WoWMoM), Cork, Ireland.","DOI":"10.1109\/WoWMoM49955.2020.00039"},{"key":"ref_19","first-page":"39","article-title":"Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks","volume":"1","author":"Kliks","year":"2019","journal-title":"J. Telecommun. Inf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ku\u0142acz, \u0141., and Kliks, A. (2020, January 17\u201319). Simplified and Reliable Wireless Data Transmission in Ultra Dense Networks. Proceedings of the 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Hvar, Croatia.","DOI":"10.23919\/SoftCOM50211.2020.9238341"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sobral, J.V.V., Rodrigues, J.J.P.C., Rab\u00ealo, R.A.L., Saleem, K., and Furtado, V. (2019). LOADng-IoT: An Enhanced Routing Protocol for Internet of Things Applications over Low Power Networks. Sensors, 19.","DOI":"10.3390\/s19010150"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TEVC.2007.896686","article-title":"Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems","volume":"12","author":"Valle","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1109\/TSMCA.2003.817391","article-title":"Ant colony optimization for routing and load-balancing: Survey and new directions","volume":"33","author":"Sim","year":"2003","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Humans"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/SURV.2013.062613.00014","article-title":"On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches","volume":"16","author":"Zhang","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1109\/41.170966","article-title":"Theory and applications of neural networks for industrial control systems","volume":"39","author":"Fukuda","year":"1992","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MWC.2006.1632481","article-title":"Emerging standards for wireless mesh technology","volume":"13","author":"Lee","year":"2006","journal-title":"IEEE Wirel. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4694","DOI":"10.1109\/TNNLS.2017.2766162","article-title":"Brain-Inspired Wireless Communications: Where Reservoir Computing Meets MIMO-OFDM","volume":"29","author":"Mosleh","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Toyonaga, S., Kominami, D., and Murata, M. (2015, January 16\u201319). Brain-inspired method for constructing a robust virtual wireless sensor network. Proceedings of the 2015 International Conference on Computing and Network Communications (CoCoNet), Trivandrum, India.","DOI":"10.1109\/CoCoNet.2015.7411167"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3509","DOI":"10.1109\/TWC.2012.081312.111538","article-title":"Brain-Inspired Dynamic Spectrum Management for Cognitive Radio Ad Hoc Networks","volume":"11","author":"Khozeimeh","year":"2012","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_32","unstructured":"Oldewurtel, F., and Mahonen, P. (November, January 29). Neural Wireless Sensor Networks. Proceedings of the International Conference on System and Networks Communications (ICSNC), Tahiti, French Polynesia."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"136161","DOI":"10.1109\/ACCESS.2019.2942312","article-title":"Microbiome-Gut-Brain Axis as a Biomolecular Communication Network for the Internet of Bio-NanoThings","volume":"7","author":"Akyildiz","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1109\/TCOMM.2012.010213.110093","article-title":"A physical channel model for nanoscale neuro-spike communications","volume":"61","author":"Balevi","year":"2013","journal-title":"IEEE Trans. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Veleti\u0107, M., Mesiti, F., Floor, P.A., and Balasingham, I. (2015, January 8\u201312). Communication theory aspects of synaptic transmission. Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK.","DOI":"10.1109\/ICC.2015.7248472"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.1109\/TCOMM.2013.042313.120799","article-title":"A communication theoretical analysis of synaptic multiple-access channel in hippocampal-cortical neurons","volume":"61","author":"Malak","year":"2013","journal-title":"IEEE Trans. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sanhaji, F., Satori, H., and Satori, K. (2017, January 14\u201316). Clustering Based on Neural Networks in Wireless Sensor Networks. Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems (ICCWCS\u201917), New York, NY, USA.","DOI":"10.1145\/3167486.3167505"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Atakan, B., and Akan, O. (2006, January 11\u201313). Immune system based distributed node and rate selection in wireless sensor networks. Proceedings of the 1st International Conference on Bio Inspired Models of Network, Information and Computing Systems (BIONETICS \u201906), Cavalese, Italy.","DOI":"10.1145\/1315843.1315847"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gissler, B., and Shrivastava, P. (2015, January 26\u201329). A System for Design Decisions based on Reliability Block Diagrams. Proceedings of the 2015 Annual Reliability and Maintainability Symposium (RAMS), Palm Harbor, FL, USA.","DOI":"10.1109\/RAMS.2015.7105105"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1109\/JSAC.2009.090922","article-title":"A stochastic geometry approach to coexistence in heterogeneous wireless networks","volume":"27","author":"Pinto","year":"2009","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.ress.2015.05.021","article-title":"Network reliability analysis based on percolation theory","volume":"142","author":"Li","year":"2015","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/JPROC.2008.2008764","article-title":"A Mathematical Theory of Network Interference and Its Applications","volume":"97","author":"Win","year":"2009","journal-title":"Proc. IEEE"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/576\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:11:25Z","timestamp":1760159485000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,15]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020576"],"URL":"https:\/\/doi.org\/10.3390\/s21020576","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,1,15]]}}}