Resource Allocation and Throughput Maximization for IoT Real-time Applications

Basir, R. and Qaisar, S. and Ali, M. and Pervaiz, H. and Naeem, M. and Imran, M.A. (2020) Resource Allocation and Throughput Maximization for IoT Real-time Applications. In: 91st IEEE Vehicular Technology Conference, 2020-05-252020-07-31, Online.

Full text not available from this repository.


The foreseen enormous generation of mobile data would result in congestion of the spectrum available. To efficiently use the available spectrum new paradigm named fog computing is a promising solution. In this paper, we developed a fog-IoT network to provide an \varepsilon-optimal resource allocation to maximize the overall network throughput. A joint cloudlet selection and power allocation problem is formulated under association and Quality-of-Service (QoS) constraints. The formulated problem falls in class of mixed-integer nonlinear programming (MINLP) problem which is NP-hard generally. We solved our problem by applying a less complex linearization technique that uses the outer approximation algorithm (OAA). Resource allocation and power allocation are efficiently conducted as a result of this optimization, which is less complicated compared to exhaustive search. © 2020 IEEE.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
91st IEEE Vehicular Technology Conference
Additional Information:
Conference code: 161536 Export Date: 30 July 2020 CODEN: IVTCD References: Peng, M., Sun, Y., Li, X., Mao, Z., Wang, C., Recent advances in cloud radio access networks: System architectures, key techniques , and open issues (2016) IEEE Communications Surveys & Tutorials, 18 (3), pp. 2282-2308; Truong, H.-L., Dustdar, S., Principles for engineering iot cloud systems (2015) IEEE Cloud Computing, 2 (2), pp. 68-76; Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W., A survey on internet of things: Architecture, enabling technologies, security and privacy , and applications (2017) IEEE Internet of Things Journal, 4 (5), pp. 1125-1142; Atlam, H.F., Walters, R.J., Wills, G.B., Fog computing and the internet of things: A review (2018) Big Data and Cognitive Computing, 2 (2), p. 10; Basir, R., Qaisar, S., Ali, M., Aldwairi, M., Ashraf, M.I., Mahmood, A., Gidlund, M., Fog computing enabling industrial internet of things: State-of-the-art and research challenges (2019) Sensors, 19 (21), p. 4807; Li, L., Guan, Q., Jin, L., Guo, M., Resource allocation and task offloading for heterogeneous real-time tasks with uncertain duration time in a fog queueing system (2019) IEEE Access, 7, pp. 9912-9925; Zhang, C., Sun, Y., Mo, Y., Zhang, Y., Bu, S., Social-aware content downloading for fog radio access networks supported device-to-device communications (2016) 2016 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB). IEEE, pp. 1-4; Han, C., Wang, W., Wang, Y., Zhang, Z., Computational resource constrained multi-cell joint processing in cloud radio access networks (2017) 2017 IEEE International Conference on Communications (ICC). IEEE, pp. 1-6; Shah, S.D.A., Zhao, H.P., Kim, H., An efficient resource management scheme for fog radio access networks with limited fronthaul capacity (2018) TENCON 2018-2018 IEEE Region 10 Conference. IEEE, pp. 1188-1192; Deng, Y., Chen, Z., Zhang, D., Zhao, M., Workload scheduling toward worst-case delay and optimal utility for single-hop fog-iot architecture (2018) IET Communications, 12 (17), pp. 2164-2173; He, S., Huang, W., Wang, J., Ren, J., Huang, Y., Zhang, Y., Cacheenabled hierarchical transmission scheme for fog radio access networks (2018) 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, pp. 1-5; Li, Q., Zhao, J., Gong, Y., Zhang, Q., Energy-efficient computation offloading and resource allocation in fog computing for internet of everything (2019) China Communications, 16 (3), pp. 32-41; Hanif, M.F., Smith, P.J., On the statistics of cognitive radio capacity in shadowing and fast fading environments (2010) IEEE Transactions on Wireless Communications, 9 (2), pp. 844-852; Duran, M.A., Grossmann, I.E., An outer-approximation algorithm for a class of mixed-integer nonlinear programs (1986) Mathematical Programming, 36 (3), pp. 307-339; Fletcher, R., Leyffer, S., Solving mixed integer nonlinear programs by outer approximation (1994) Mathematical Programming, 66 (1-3), pp. 327-349; Floudas, C.A., Pardalos, P.M., (2008) Encyclopedia of Optimization, , Springer Science & Business Media; Floudas, C.A., (1995) Nonlinear and Mixed-integer Optimization: Fundamentals and Applications, , Oxford University Press on Demand; Ali, M., Riaz, N., Ashraf, M.I., Qaisar, S., Naeem, M., Joint cloudlet selection and latency minimization in fog networks (2018) IEEE Transactions on Industrial Informatics, 14 (9), pp. 4055-4063; Elbamby, M.S., Bennis, M., Saad, W., Latva-Aho, M., Hong, C.S., Proactive edge computing in fog networks with latency and reliability guarantees (2018) EURASIP Journal on Wireless Communications and Networking, 2018 (1), p. 209; Bonami, P., Basic Open-Source Nonlinear Mixed Integer Programming, ,, accessed on Aug. 1 2019
ID Code:
Deposited By:
Deposited On:
23 Jun 2021 18:25
Last Modified:
20 Oct 2021 06:30