Abstract
The next generation 5G networks are expected to provide high data rates (upto 10 Gbps), significant increase in capacity of base stations, exceptionally low latency and remarkable improvement in end-to-end Quality of Service (QoS). The present 4G LTE/LTE-A networks introduced numerous network services, like Device to Device (D2D) communications, Big Data, Internet of Things (IoT) and Internet of Vehicles (IoV), making it smarter than previous generations. However, due to the proliferation of smart devices, advanced multimedia applications along with the increase in demand for wireless data and usage, these network services are facing many challenges. This article explores the challenges of current network services and envisions social network as an optimal solution to solve the challenges. Furthermore, we believe that these network services would work synchronously under the common umbrella of 5G networks. We scrutinize the role of single converged network formed after fusing sociology with the network services, which has the potential to meet the goals and requirements of 5G networks. Operators, service providers and the end users will benefit from this convergence by exploiting synergies and enabling an optimum use of the two fused networks, i.e. next generation mobile networks and social networks to form “Socio-5G networks”. To show the effectiveness of our proposal, we perform simulations on our testbed and demonstrate that the delay experienced by Social IoT devices reduces up to 3.5 times and maximum device support increases by 2 times as compared normal 5G networks. On the other hand, social D2D shows up to 67% increase in data rate while providing coverage for upto 80% devices as compared to normal D2D that provides only 50% coverage, thus, reducing the chances of outage.
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Notes
- 1.
“3GPP system standards heading into the 5G era”, [Online]. Available: http://www.3gpp.org/news-events/.
- 2.
“Social Internet of Things: Turning Smart Objects into Social Objects to Boost the IoT” by L. Atzori et. al in IEEE Internet of Things newsletter (2014).
- 3.
“Internet of Things and 5G: GISFI IoT WG Activities”, Available online: www.gisfi.org.
References
Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. In IEEE Communications Surveys and Tutorials, 99, 1–40.
S. Singh, et al.: A survey on 5G network technologies from social perspective. IETE Technical Review Taylor and Francis, pp. 1–10. https://doi.org/10.1080/02564602.2016.1141077.
Zhang, Y., et al. (2015). Social network aware device-to-device communication in wireless networks. IEEE Transactions on Wireless Communications, 14(1), 177–190.
Sun, Y., et al. (2014). Efficient resource allocation for mobile social networks in D2D communication underlaying cellular networks. In IEEE International Conference on Communications (ICC). Sydney: NSW.
Hamed, A. A., & Wu, X. (2014). Does social media big data make the world smaller? An exploratory analysis of keyword-hashtag networks. In IEEE International Congress on Big Data (BigData Congress). Anchorage, AK.
Roh, W., et al. (2014). Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results. EEE Communications Magazine, 52(2), 106–13.
Nitti, M., Atzori, L., & Cvijikj, I. P. (2015). Friendship selection in the Social Internet of Things: Challenges and possible strategies. IEEE Internet of Things Journal, 2(3), 240–247.
Atzori, L., Iera, A., & Morabito, G. (2014). From smart objects to social objects: The next evolutionary step of the internet of things. IEEE Communications Magazine, 52(1), 97–105.
Luan, T. H., et al. (2015). Feel bored? join verse! engineering vehicular proximity social networks. IEEE Transactions on Vehicular Technology, 64(3), 1120–1131.
Lequerica, I., Longaron, M. G., & Ruiz, P. M. (2010). Drive and share: Efficient provisioning of social networks in vehicular scenarios. IEEE Communications Magazine, 48(11), 90–97.
Osseiran, A., et al. (2014). Scenarios for 5G mobile and wireless communications: The vision of the METIS project. IEEE Communications Magazine, 52(5), 26–35.
Zheng, K., et al. (2016). Big data-driven optimization for mobile networks toward 5G. IEEE Network, 30(1), 44–51.
Saxena, N., Roy, A., Sahu, B. J. R., & Kim, H. (2017, February). Efficient IoT gateway over 5G wireless: a new design with prototype and implementation results. IEEE Communication Magazine, 55(2), 97–105.
Rappaport, T. S., et al. (2013). Broadband millimeter wave propagation measurements and models using adaptive beam antennas for outdoor Urban cellular communications. IEEE Transactions Antennas and Propagation, 61(4), 1850–59.
Choi, S., Chung, K. S., & Yu, H. (2014). Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Cluster Computing, 17(3), 911–926.
Wu, Y., et al. (2013, June) Cloudmov: Cloud-based mobile social tv. IEEE Transactions on Multimedia, 15(4), 821–832.
Voutyras, O., et al. (2014, october). An architecture supporting knowledge flow in social internet of things systems. In IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Larnaca.
Barbarossa, S., Sardellitti, S., & Di Lorenzo, P. (2014). Communicating while computing: Distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Processing Magazine, 31(6), 45–55.
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Jit Singh, H., Singh, D., Singh, S., Sahu, B.J.R., Lakshmi Narasimhan, V. (2021). When Sociology Meets Next Generation Mobile Networks. In: Goyal, D., Bălaş, V.E., Mukherjee, A., Hugo C. de Albuquerque, V., Gupta, A.K. (eds) Information Management and Machine Intelligence. ICIMMI 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-4936-6_62
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DOI: https://doi.org/10.1007/978-981-15-4936-6_62
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