Vartika Agarwal
Network resource allocation security techniques and challenges for vehicular communication network management
- Authors Details :
- Vartika Agarwal,
- Sachin Sharma,
- Gagan Bansal
Journal title : Intelligent Systems Reference Library
Publisher : Springer International Publishing
Online ISSN : 1868-4408
Page Number : 123-137
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Internet of things describes the network of physical objects such as sensors, receivers, transmitters and other technologies which are used in VCN. In Vehicular communication network two or more vehicles are communicate with each other. VCN use advanced technologies to solve transportation related problems like long traffic delays, road accidents and air pollution. IOT based technologies make vehicular network smart. In this chapter we reviewed about network resource allocation security techniques, challenges and also discuss how we can make vehicular communication network smarter. We reviewed about different models and schemes for V2V communication. These schemes were developed to ensure a fair, efficient and transparent allocation of resource in an intelligent transportation system.
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DOI : https://doi.org/10.1007/978-3-030-99329-0_9
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- (1). Asheralieva, A., Khan, J. Y., Mahata, K., Ong, E. H.: A predictive network resource allocation technique for cognitive wireless networks. In: 2010 4th International Conference on Signal Processing and Communication Systems, IEEE. pp. 1–9 (2010)
- (2). Ferragut, A., Paganini, F.: Network resource allocation for users with multiple connections: fairness and stability. IEEE/ACM Trans. Netw. 22(2), 349–362 (2013)
- (3). Shams, F., Bacci, G., Luise, M.: A survey on resource allocation techniques in OFDM (A) networks. Comput. Netw. 65, 129–150 (2014)
- (4). Tsiropoulos, G.I., Dobre, O.A., Ahmed, M.H., Baddour, K.E.: Radio resource allocation techniques for efficient spectrum access in cognitive radio networks. IEEE Commun. Surv. & Tutor. 18(1), 824–847 (2014)
- (5). Lee, H., Lee, K.J., Kim, H., Clerckx, B., Lee, I.: Resource allocation techniques for wireless powered communication networks with energy storage constraint. IEEE Trans. Wireless Commun. 15(4), 2619–2628 (2015)
- (6). Wu, W., Zhou, F., Hu, R.Q., Wang, B.: Energy-Efficient resource allocation for secure noma-enabled mobile edge computing networks. IEEE Trans. Commun. 68(1), 493–505 (2020). https://doi.org/10.1109/TCOMM.2019.2949994
- (7). Pandiyan, S., Perumal, V.: A survey on various problems and techniques for optimizing energy efficiency in cloud architecture. Walailak J. Sci. Technol. (WJST) 14(10), 749–758 (2017)
- (8). Bermejo, B., Filiposka, S., Juiz, C., Gómez, B., Guerrero, C.: Improving the energy efficiency in cloud computing data centres through resource allocation techniques. Res. Adv. Cloud Comput. 211–236 (2017) Springer, Singapore
- (9). Song, Q., Wang, X., Qiu, T., Ning, Z.: An interference coordination-based distributed resource allocation scheme in heterogeneous cellular networks. IEEE Access 5, 2152–2162 (2017)
- (10). Liang, L., Li, G.Y., Xu, W.: Resource allocation for D2D-enabled vehicular communications. IEEE Trans. Commun. 65(7), 3186–3197 (2017)
- (11). Wu, W., Zhou, F., Hu, R.Q., Wang, B.: Energy-efficient resource allocation for secure NOMA-enabled mobile edge computing networks. IEEE Trans. Commun. 68(1), 493–505 (2019)
- (12). Jayakumar, S., Nandakumar, S.: A review on resource allocation techniques in D2D communication for 5G and B5G technology. Peer-To-Peer Netw. Appl. 14(1), 243–269 (2021)
- (13). Tayyaba, S.K., Khattak, H.A., Almogren, A., Shah, M.A., Din, I.U., Alkhalifa, I., Guizani, M.: 5G vehicular network resource management for improving radio access through machine learning. IEEE Access 8, 6792–6800 (2020)
- (14). Li, X., Xu, L. D.: A review of internet of Things—Resource allocation. IEEE Internet Things J. 8(11) 8657–8666 June 1, (2021). https://doi.org/10.1109/JIOT.2020.3035542
- (15). Maharaj, B. T., Awoyemi, B. S.: Modelling and analyses of resource allocation optimisation in cognitive radio networks. Dev. Cogn. Radio Netw. 85–118 (2022). Springer, Cham
- (16). Praveenchandar, J., Tamilarasi, A.: Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. J. Ambient. Intell. Humaniz. Comput. 12(3), 4147–4159 (2021)
- (17). HamaAli, K.W., Zeebaree, S.R.: Resources allocation for distributed systems: A review. Int. J. Sci. Bus. 5(2), 76–88 (2021)
- (18). Mohamed, A., Hamdan, M., Khan, S., Abdelaziz, A., Babiker, S. F., Imran, M., Marsono, M. N.: Software-defined networks for resource allocation in cloud computing: A survey. Comput. Netw. 195 108151 (2021)
- (19). Agarwal, V., Sharma, S., Bansal, G.: Secured scheduling techniques of network resource management in vehicular communication networks. In: 2021 5th International Conference On Intelligent Computing And Control Systems (Iciccs), IEEE pp. 198–202 (2021)
- (20). Zhang, M., Cumanan, K., Thiyagalingam, J., Tang, Y., Wang, W., Ding, Z., Dobre, O.A.: Exploiting deep learning for secure transmission in an underlay cognitive radio network. IEEE Trans. Veh. Technol. 70(1), 726–741 (2021)
- (21). Agarwal, V., Sharma, S., Agarwal, P.: IoT based smart transport management and vehicle-to-vehicle communication system. Comput. Netw., Big Data IoT. 709–716 (2021), Springer, Singapore
- (22). Agarwal, V., Sharma, S.: IoT based smart transport management system. In: International Conference on Advanced Informatics for Computing Research. pp. 207–216 (2020). Springer, Singapore
- (23). Sachan S., Sharma, R., Sehgal, A.: Energy efficient scheme for better connectivity in sustainable mobile wireless sensor networks. Sustain. Comput.: Inform. Syst. 30 100504 (2021)
- (24). Ghanem, S., Kanungo, P., Panda, G., et al.: Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00381-2
- (25). Sachan, S., Sharma, R., Sehgal, A.: SINR based energy optimization schemes for 5g vehicular sensor networks. Wireless Pers. Commun. (2021). https://doi.org/10.1007/s11277-021-08561-6
- (26). Priyadarshini, I., Mohanty, P., Kumar, R., et al.: A study on the sentiments and psychology of twitter users during COVID-19 lockdown period. Multimed. Tools Appl. (2021). https://doi.org/10.1007/s11042-021-11004-w
- (27). Azad, C., Bhushan, B., Sharma, R., et al.: Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus. Multimed. Syst. (2021). https://doi.org/10.1007/s00530-021-00817-2
- (28). Priyadarshini, I., Kumar, R., Tuan, L.M., et al.: A new enhanced cyber security framework for medical cyber physical systems. SICS Softw.-Inensiv. Cyber-Phys. Syst. (2021). https://doi.org/10.1007/s00450-021-00427-3
- (29). Ishaani, P., Raghvendra, K., Rohit, S., Pradeep Kumar, S., Suresh Chandra, S.: Identifying cyber insecurities in trustworthy space and energy sector for smart grids. Comput. & Electr. Eng. 93 107204 (2021)
- (30). Rajesh Singh, Rohit Sharma, Shaik Vaseem Akram, Anita Gehlot, Dharam Buddhi, Praveen Kumar Malik, Rajeev Arya.: Highway 4.0: Digitalization of highways for vulnerable road safety development with intelligent IoT sensors and machine learning. Saf. Sci. 143 105407 (2021). ISSN 0925–7535
- (31). Sahu, L., Sharma, R., Sahu, I., Das, M., Sahu, B., Kumar, R.: Efficient detection of Parkinson's disease using deep learning techniques over medical data. Expert. Syst. e12787 (2021). https://doi.org/10.1111/exsy.12787
- (32). Sharma, R., Kumar, R., Sharma, D.K., et al.: Water pollution examination through quality analysis of different rivers: a case study in India. Environ. Dev. Sustain. (2021). https://doi.org/10.1007/s10668-021-01777-3
- (33). Ha, D.H., Nguyen, P.T., Costache, R., et al.: Quadratic discriminant analysis based ensemble machine learning models for groundwater potential modeling and mapping. Water Resour. Manage. (2021). https://doi.org/10.1007/s11269-021-02957-6
- (34). Dhiman, G., Sharma, R.: SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00578-5
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