Networking Models In Flying Ad Hoc Networks â€Myassignmenthelp.Com

Question: Discuss About The Networking Models In Flying Ad Hoc Networks? Answer: Intorducation Worldwide Interoperability for Microwave Access (WiMax) refers to the delivery of services to long mile wireless broadband access (Bernardos et al., 2014). It provides both multipoint and applications. It has able to improve the network security during transmission to last mile. WiMax uses three data encryption standards for protecting the data and information. WiMax uses Counter Mode with Cipher Block Chaining Message Authentication Code Protocol (CCMP) to encrypt all traffic on its network (Makris, Skoutas Skianis, 2013). The data encryption standards used by WiMax is described as below: Triple DES used to implement original Data Encryption Standard (DES) algorithm, hackers used for figuring out how to vanquish effortlessly. Triple DES was important standard and most used symmetric algorithm in organization (Osseiran et al., 2014). Triple DES uses three different keys that are 56 bits each. The key length is 168 bits; researchers would suggest that 112-bits in key quality is more similar to it. The Advanced Encryption Standard (AES) is algorithm trusted as standard by the U.S. Government. It is used in 128-piece frame. AES uses keys of 192 and 256 bits for encryption purposes (Sahingoz, 2014). It is used in many cases in the organization for providing security to the data and security of the business organization. RSA is a key encryption algorithm and standard for encrypting transmitted on Internet. RSA is used as an asymmetric algorithm because of its uses of a couple of keys (Viani et al., 2013). The key is an algorithm that is use to encrypt message. There is a private key to decode it. The limitation of RSA encryption is a data that takes aggressors a lot of time. The two examples of WPAN technologies are Bluetooth and Zigbee. There are various security challenges in the Bluetooth devices and technology. There are various attacks against confidentiality and data integrity (Ayyash et al., 2016). Bluetooth does not provide native client verification. The authorization of the Bluetooth device is not done that causes explicit behavior of other connected devices. There is a huge threat of DoS attacks on the Bluetooth devices during transmission of data and information. There can be malicious entry of external parties during the transmission process of data and information. The sensitive data and information might be damaged and corrupted during the wireless transmission. Bluesnarfing is a threat to this technology that allows the attackers to increase the use of the Bluetooth enabled devices. It looks for the IMEI number of the Bluetooth devices and get connected through this. After connecting with the parent Bluetooth devices, the connected device breaches all the data and information from the Bluetooth device. Bluejacking causes data breach from the Bluetooth enabled mobile phones. The attacker starts the bluejacking by sending false message to the mobile phones (Jiang et al., 2017). These span messages and phishing mail causes damage to the mobile phones. ZigBee is a wireless communication with low power and low-rate that aims to provide cryptographic keys for security. The link between the two devices is maintained by the security orotocol of the ZigBee. There are various physical attacks on the ZigBee radio regarding the frequency breach (Osseiran et al., 2014). The data packets collected during the wireless communication ca be breached. Shaikh, Faisal Karim, and Sherali Zeadally. "Energy harvesting in wireless sensor networks: A comprehensive review." Renewable and Sustainable Energy Reviews 55 (2016): 1041-1054. This paper deals with the harvesting of energy in the Wireless Sensor Network (WSN). WSN consist of large number of static sensor nodes that helps in low processing. There are various sources of energy for the WSNs including Radio Frequency-based energy harvesting, Solar-based energy harvesting, Thermal-based Energy Harvesting and Flow-based energy harvesting. There are various approaches discussed in the paper for the energy harvesting. As argued by (), the energy harvesting system might cause damage to the ecological balance of the nature. The harvesting from different sources causes depletion of the resources in the environment. Choochaisri, Apicharttrisorn Intanagonwiwat, (2017) argued that log lasting devices consumes more battery and causes energy draining. The energy-efficient reliable systems provides benefit to the users in the form of ultra-energy efficient sensors. Ulukus, Sennur, et al. "Energy harvesting wireless communications: A review of recent advances." IEEE Journal on Selected Areas in Communications 33.3 (2015): 360-381. This paper discusses about the new technologies implemented in the energy-harvesting sector. As commented by Fontes et al., (2017), the new advances in the energy harvesting have changed the traditional scenario of energy harvesting. There are various potential model used in recent market for energy harvesting. On the other hand, Choochaisri, Apicharttrisorn Intanagonwiwat, (2017) argued that the use of the modern techniques in the energy harvesting have increases the initial cost of various energy sources and also depleting the natural resources. Various equations and theories discussed in the paper that utilizes the traditional concept of energy harvesting and converting it into modern technology (Osseiran et al., 2014). The possible improvement in the traditional theory has been upgraded into modern tactics. The VLSI model is used for understanding the complexity and energy of decoding and encoding in system. References Ayyash, M., Elgala, H., Khreishah, A., Jungnickel, V., Little, T., Shao, S., ... Freund, R. (2016). Coexistence of WiFi and LiFi toward 5G: Concepts, opportunities, and challenges.IEEE Communications Magazine,54(2), 64-71. Bernardos, C. J., De La Oliva, A., Serrano, P., Banchs, A., Contreras, L. M., Jin, H., Ziga, J. C. (2014). An architecture for software defined wireless networking.IEEE wireless communications,21(3), 52-61. Choochaisri, S., Apicharttrisorn, K., Intanagonwiwat, C. (2017). Stable Desynchronization for Wireless Sensor Networks:(I) Concepts and Algorithms. arXiv preprint arXiv:1704.07002. Fontes, R. D. R., Mahfoudi, M., Dabbous, W., Turletti, T., Rothenberg, C. (2017). How Far Can We Go? Towards Realistic Software-Defined Wireless Networking Experiments.The Computer Journal, 1-14. Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K. C., Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks.IEEE Wireless Communications,24(2), 98-105. Makris, P., Skoutas, D. N., Skianis, C. (2013). A survey on context-aware mobile and wireless networking: On networking and computing environments' integration.IEEE communications surveys tutorials,15(1), 362-386. Osseiran, A., Boccardi, F., Braun, V., Kusume, K., Marsch, P., Maternia, M., ... Tullberg, H. (2014). Scenarios for 5G mobile and wireless communications: the vision of the METIS project.IEEE Communications Magazine,52(5), 26-35. Sahingoz, O. K. (2014). Networking models in flying ad-hoc networks (FANETs): Concepts and challenges.Journal of Intelligent Robotic Systems,74(1-2), 513. Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., Massa, A. (2013). Wireless architectures for heterogeneous sensing in smart home applications: Concepts and real implementation.Proceedings of the IEEE,101(11), 2381-2396.