Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Adholiya, Ashish
- Effect of COVID-19 Outbreak on Travel and Tourism Industry: A Study on Udaipur Hoteliers’ Perspective
Abstract Views :95 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Pacific Business School, Udaipur, Rajasthan, IN
2 Associate Professor, Amity School of Hospitality, Amity University, Rajasthan, IN
3 Assistant Professor, Post Graduate Centre, Management and Science University, University Drive, MY
1 Assistant Professor, Pacific Business School, Udaipur, Rajasthan, IN
2 Associate Professor, Amity School of Hospitality, Amity University, Rajasthan, IN
3 Assistant Professor, Post Graduate Centre, Management and Science University, University Drive, MY
Source
Journal of Hospitality Application and Research, Vol 17, No 1 (2022), Pagination: 69-90Abstract
Year 2020 started with the one carryover disease presented as atypical pneumonia which was identified as a severe acute respiratory syndrome later termed as COVID-19 and spread all through the globe. Some of the major countries namely America, India, Italy, Spain, China (Spreader Country), France, Britain, and many more experienced unexceptional spread throughout their states because the disease was communicable and can easily be transmitted through contact with the infected person. So, as one of the remedial actions to regulate the transmission most of the countries preferred the lockdown as a key strategy to stop the commutation, guidelines issues for home isolation, and social distancing enforced at all the extent. This lockdown severely affected millions of people and economic sectors, among all the sectors travel, tourism, and hospitality sector affected the most and still looking for its revival because still travel restrictions are on, and travelers or tourist are not preferring to visit tourist destinations because of suspect of its spread from the tourist places. Due to all these, even after unlock guideline released by the government still the hoteliers looking for the paced revival from the economic setbacks observed by them. This research work is a quantitative research work performed on the hoteliers’ opinion for the negative effect on the industry and their business because of travel restrictions in continuous lockdowns and how exactly they are planned for their revival. The research work embraces relevance for all those having concerns with the revival and sustainable development of the industry or sector.Keywords
COVID-19, Travel, Tourism and Hospitality Industry, Lockdown, Social Distancing, Economic Setback, Hoteliers, Sustainable Development, RevivalReferences
- Alonso, A. D., Kok, S. K., Bressan, A., O’Shea, M., Sakellarios, N.,… Santoni, L. J. (2020). COVID-19, aftermath, impacts, and hospitality firms: An international perspective. International Journal of Hospitality Management.
- Bagnera, S. M., & Stewart, E. (2020). Boston hospitality review. Retrieved from https://www.bu.edu/bhr/2020/03/25/navigating-hotel -operations-in-times-of-covid-19/
- Browne, A., Ahmad, S., Beck, C. R., & Nguyen-Van-Tam, J. S. (2016). The roles of transportation and transportation hubs in the propagation of influenza and corona viruses: A systematic review. J. Travel Med., 23(1).
- Chen, M. H., Jang, S. S., & Kim, W. G. (2007). The impact of SARS outbreak on Taiwanese hotel stock performance: An event study approach. International Journal of Hospitality Management, 26(1), 200-212.
- Davahli, M. R., Karwowskim, W., Sonmez, S., & Apostolopoulos, Y. (2020). The hospitality industry in the face of the COVID-19 pandemic: Current topics and research methods. International Journal of Environmental Research and Public Health, 17, 7366.
- Gossling S., Scott, D., & Hall, C. M. (2020). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 1-20.
- Gursoy, D., Chi, C. G., & Chi, O. H. (2020). COVID-19 study 2 report: Restaurant and hotel industry: Restaurant and hotel customers’ sentiment analysis. Would they come back? If they would, WHEN? (Report No. 2), Carson College of Business, Washington State University.
- Kim, D. H. (2020). Health effects of the COVID-19 pandemic by sex. Korean J. Women Health Nurs., 26(2), 106-108.
- Kim, S. S., Chun, H., & Lee, H. (2006). The effect of SARS on Korean hotel industry and measures to overcome the crisis: A study on six Korean five star hotels. Asia Pacific Journal Tour. Res., 10(4), 369-377.
- Law, R. (2005). A perspective on SARS and education in hospitality and tourism. Journal Teach. Travel. Tour, 5(4), 53-59.
- Lodder, W., & Husman D. R. (2020). SARS-COV-2 in waste water: Potential health risk, but also data source. Lancet Gastroenterol Hepatol, 5(6), 533-534.
- Marques, G., & Agarwal, D. (2020). Automated medical diagnosis of COVID-19 through efficient net convolution neural network. Apple Soft Computing.
- Min, J. C., Lim, C., & Kung, H. H. (2011). Intervention analysis of SARS on Japanese tourism demand for Taiwan. Qual. Quant., 45(1), 91-102.
- Mohanty, S. K. (2020). Contextualising geographical vulnerability to COVID-19 in India. Lancet Global Health.
- Nazneen, S., Hong, X., & Ud Din, N. (2020). COVID-19 crises and tourist travel risk perceptions.
- Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., & Iosifidis, C. (2020). The socio-economic implications of the corona virus pandemic (COVID-19): A review. International Journal of Surgery, 78, 185-193.
- Radhakrishna, S. A. (2020). COVID-19 - Post-pandemic, India’s tourism sector stares at 70% job loss.
- Sharma, S. (2020). Hindustan times, 2m distancing, masks key to stopping COVID-19 spread: Study.
- Tappe, A., & Luhby, T. (2020). 22 million Americans have filed for unemployment benefits in the last four weeks. Retrieved from https://www.cnn.com/2020/04/16/economy/unemployment-benefitscoronavirus/index
- Tiwari, V. (2020). Work from home, no large gathering: 10 facts on centre’s COVID directives.
- Tse, A. C. B., So, S., & Sin, L. (2006). Crisis management and recovery: How restaurants in hongkong responded to SARS. International J. Hospitality Management, 25(1), 3-11.
- Wen, Z., Huimin, G., & Kavanugh, R. R. (2005). The impacts of SARS on the consumer behaviour on Chinese domestic tourist. Current Issue Tour, 8(1), 22-38.
- Yeolekar, A., Bhalerao, S., & Bhalerao, M. (2020). The new normal of ENT OPD - Adapting safe practices.
- Energy Efficient Multi Hop D2D Communication Using Deep Reinforcement Learning in 5G Networks
Abstract Views :94 |
PDF Views:0
Authors
Affiliations
1 Department Faculty of Computer Science, Pacific Academy of Higher Education and Research University, Udaipur (Rajasthan)., IN
1 Department Faculty of Computer Science, Pacific Academy of Higher Education and Research University, Udaipur (Rajasthan)., IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 3 (2023), Pagination: 401-421Abstract
One of the most potential 5G technologies for wireless networks is device-to-device (D2D) communication. It promises peer-to-peer consumers high data speeds, ubiquity, and low latency, energy, and spectrum efficiency. These benefits make it possible for D2D communication to be completely realized in a multi-hop communication scenario. However, the energy efficient multi hop routing is more challenging task. Hence, this research deep reinforcement learning based multi hop routing protocol is introduced. In this, the energy consumption is considered by the proposed double deep Q learning technique for identifying the possible paths. Then, the optimal best path is selected by the proposed Gannet Chimp optimization (GCO) algorithm using multi-objective fitness function. The assessment of the proposed method based on various measures like packet delivery ratio, latency, residual energy, throughput and network lifetime accomplished the values of 99.89, 1.63, 0.98, 64 and 99.69 respectively.Keywords
5G Networks, D2D Communication, Energy Efficient Routing, Multi-Hop Path, Deep Q Learning, Optimal Path Selection.References
- Z. Li, C. Guo, and Y. Xuan, “A Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation Framework for D2D Communications,” in 2019 IEEE Global Communications Conference (GLOBECOM), Dec. 2019, pp. 1–6. doi: 10.1109/GLOBECOM38437.2019.9013763.
- V. Sridhar and S. E. Roslin, “Energy Efficient Device to Device Data Transmission Based on Deep Artificial Learning in 6G Networks,” Int. J. Comput. Networks Appl., vol. 9, no. 5, pp. 568–577, 2022, doi: 10.22247/ijcna/2022/215917.
- M. Alnakhli, S. Anand, and R. Chandramouli, “Joint Spectrum and Energy Efficiency in Device to Device Communication Enabled Wireless Networks,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 2, pp. 217–225, Jun. 2017, doi: 10.1109/TCCN.2017.2689015.
- M. Waqas et al., “A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1863–1886, 2020, doi: 10.1109/COMST.2019.2923708.
- R. A. Diab, N. Bastaki, and A. Abdrabou, “A Survey on Routing Protocols for Delay and Energy-Constrained Cognitive Radio Networks,” IEEE Access, vol. 8, pp. 198779–198800, 2020, doi: 10.1109/ACCESS.2020.3035325.
- L. Li, L. Chang, and F. Song, “A Smart Collaborative Routing Protocol for QoE Enhancement in Multi-Hop Wireless Networks,” IEEE Access, vol. 8, pp. 100963–100973, 2020, doi: 10.1109/ACCESS.2020.2997350.
- X. Zhou, M. Sun, G. Y. Li, and B. H. Fred Juang, “Intelligent wireless communications enabled by cognitive radio and machine learning,” China Commun., vol. 15, no. 12, pp. 16–48, 2018.
- K. M. Thilina, Kae Won Choi, N. Saquib, and E. Hossain, “Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE J. Sel. Areas Commun., vol. 31, no. 11, pp. 2209–2221, Nov. 2013, doi: 10.1109/JSAC.2013.131120.
- R. Joon and P. Tomar, “Energy Aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 6989–7000, Oct. 2022, doi: 10.1016/j.jksuci.2022.03.021.
- J. Ramkumar and R. Vadivel, “Improved Wolf prey inspired protocol for routing in cognitive radio Ad Hoc networks,” Int. J. Comput. Networks Appl., vol. 7, no. 5, pp. 126–136, 2020, doi: 10.22247/ijcna/2020/202977.
- M. C. Hlophe and B. T. Maharaj, “QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning,” J. Commun. Networks, vol. 22, no. 3, pp. 185–204, Jun. 2020, doi: 10.1109/JCN.2020.000013.
- H. B. Salameh, S. Mahasneh, A. Musa, R. Halloush, and Y. Jararweh, “Effective peer-to-peer routing in heterogeneous half-duplex and full-duplex multi-hop cognitive radio networks,” Peer-to-Peer Netw. Appl., vol. 14, no. 5, pp. 3225–3234, Sep. 2021, doi: 10.1007/s12083-021-01183-6.
- Y. Zhi, J. Tian, X. Deng, J. Qiao, and D. Lu, “Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks,” Digit. Commun. Networks, vol. 8, no. 5, pp. 834–842, Oct. 2022, doi: 10.1016/j.dcan.2021.09.013.
- S. Yu and J. W. Lee, “Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks,” Sensors, vol. 22, no. 23, p. 9459, Dec. 2022, doi: 10.3390/s22239459.
- X. Li, G. Chen, G. Wu, Z. Sun, and G. Chen, “Research on Multi-Agent D2D Communication Resource Allocation Algorithm Based on A2C,” Electronics, vol. 12, no. 2, p. 360, Jan. 2023, doi: 10.3390/electronics12020360.
- S. H. A. Kazmi, F. Qamar, R. Hassan, and K. Nisar, “Routing-Based Interference Mitigation in SDN Enabled Beyond 5G Communication Networks: A Comprehensive Survey,” IEEE Access, vol. 11, pp. 4023– 4041, 2023, doi: 10.1109/ACCESS.2023.3235366.
- J. Zhang, W. Gao, G. Chuai, and Z. Zhou, “An Energy-Effective and QoS-Guaranteed Transmission Scheme in UAV-Assisted Heterogeneous Network,” Drones, vol. 7, no. 2, p. 141, Feb. 2023, doi: 10.3390/drones7020141.
- X. Li, G. Chen, G. Wu, Z. Sun, and G. Chen, “D2D Communication Network Interference Coordination Scheme Based on Improved Stackelberg,” Sustainability, vol. 15, no. 2, p. 961, Jan. 2023, doi: 10.3390/su15020961.
- D. Han and J. So, “Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications,” Sensors, vol. 23, no. 3, p. 1295, Jan. 2023, doi: 10.3390/s23031295.
- P. Tam, R. Corrado, C. Eang, and S. Kim, “Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications,” Appl. Sci., vol. 13, no. 5, p. 3083, Feb. 2023, doi: 10.3390/app13053083.
- L. Nagapuri et al., “Energy Efficient Underlaid D2D Communication for 5G Applications,” Electronics, vol. 11, no. 16, p. 2587, Aug. 2022, doi: 10.3390/electronics11162587.
- N. Khan, I. A. Khan, J. U. Arshed, M. Afzal, M. M. Ahmed, and M. Arif, “5G-EECC: Energy-Efficient Collaboration-Based Content Sharing Strategy in Device-to-Device Communication,” Secur. Commun. Networks, vol. 2022, pp. 1–13, Jan. 2022, doi: 10.1155/2022/1354238.
- I. Ioannou, C. Christophorou, V. Vassiliou, and A. Pitsillides, “A novel Distributed AI framework with ML for D2D communication in 5G/6G networks,” Comput. Networks, vol. 211, p. 108987, Jul. 2022, doi: 10.1016/j.comnet.2022.108987.
- M. K. Chamran, K.-L. A. Yau, M. H. Ling, and Y.-W. Chong, “A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach,” Sensors, vol. 22, no. 16, p. 6021, Aug. 2022, doi: 10.3390/s22166021.
- V. Tilwari, T. Song, and S. Pack, “An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks,” Electronics, vol. 11, no. 24, p. 4118, Dec. 2022, doi: 10.3390/electronics11244118.
- J.-S. Pan, L.-G. Zhang, R.-B. Wang, V. Snášel, and S.-C. Chu, “Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems,” Math. Comput. Simul., vol. 202, pp. 343–373, Dec. 2022, doi: 10.1016/j.matcom.2022.06.007.
- M. Khishe and M. R. Mosavi, “Chimp optimization algorithm,” Expert Syst. Appl., vol. 149, p. 113338, Jul. 2020, doi: 10.1016/j.eswa.2020.113338.