- Approximation algorithms, Fog computing, Integer programming, Nonlinear programming, Quality of service, Resource allocation, Springs (components), Formulated problems, Linearization technique, Mixed integer non-linear programming problems, Optimal resource allocation, Outer approximation algorithm, Quality of Service constraints, Real-time application, Throughput maximization, Internet of things

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Published

**Resource Allocation and Throughput Maximization for IoT Real-time Applications.** / Basir, R.; Qaisar, S.; Ali, M.; Pervaiz, H.; Naeem, M.; Imran, M.A.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Basir, R, Qaisar, S, Ali, M, Pervaiz, H, Naeem, M & Imran, MA 2020, 'Resource Allocation and Throughput Maximization for IoT Real-time Applications', Paper presented at 91st IEEE Vehicular Technology Conference, 25/05/20 - 31/07/20. https://doi.org/10.1109/VTC2020-Spring48590.2020.9129613

Basir, R., Qaisar, S., Ali, M., Pervaiz, H., Naeem, M., & Imran, M. A. (2020). *Resource Allocation and Throughput Maximization for IoT Real-time Applications*. Paper presented at 91st IEEE Vehicular Technology Conference, . https://doi.org/10.1109/VTC2020-Spring48590.2020.9129613

Basir R, Qaisar S, Ali M, Pervaiz H, Naeem M, Imran MA. Resource Allocation and Throughput Maximization for IoT Real-time Applications. 2020. Paper presented at 91st IEEE Vehicular Technology Conference, . https://doi.org/10.1109/VTC2020-Spring48590.2020.9129613

@conference{9372babbe86441c0a7688a5b09d0ca58,

title = "Resource Allocation and Throughput Maximization for IoT Real-time Applications",

abstract = "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. {\textcopyright} 2020 IEEE.",

keywords = "Approximation algorithms, Fog computing, Integer programming, Nonlinear programming, Quality of service, Resource allocation, Springs (components), Formulated problems, Linearization technique, Mixed integer non-linear programming problems, Optimal resource allocation, Outer approximation algorithm, Quality of Service constraints, Real-time application, Throughput maximization, Internet of things",

author = "R. Basir and S. Qaisar and M. Ali and H. Pervaiz and M. Naeem and M.A. Imran",

note = "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, , http://www.coin-or.org/Bonmin/, accessed on Aug. 1 2019; 91st IEEE Vehicular Technology Conference, VTC Spring 2020 ; Conference date: 25-05-2020 Through 31-07-2020",

year = "2020",

month = may,

day = "25",

doi = "10.1109/VTC2020-Spring48590.2020.9129613",

language = "English",

}

TY - CONF

T1 - Resource Allocation and Throughput Maximization for IoT Real-time Applications

AU - Basir, R.

AU - Qaisar, S.

AU - Ali, M.

AU - Pervaiz, H.

AU - Naeem, M.

AU - Imran, M.A.

N1 - 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, , http://www.coin-or.org/Bonmin/, accessed on Aug. 1 2019

PY - 2020/5/25

Y1 - 2020/5/25

N2 - 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.

AB - 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.

KW - Approximation algorithms

KW - Fog computing

KW - Integer programming

KW - Nonlinear programming

KW - Quality of service

KW - Resource allocation

KW - Springs (components)

KW - Formulated problems

KW - Linearization technique

KW - Mixed integer non-linear programming problems

KW - Optimal resource allocation

KW - Outer approximation algorithm

KW - Quality of Service constraints

KW - Real-time application

KW - Throughput maximization

KW - Internet of things

U2 - 10.1109/VTC2020-Spring48590.2020.9129613

DO - 10.1109/VTC2020-Spring48590.2020.9129613

M3 - Conference paper

T2 - 91st IEEE Vehicular Technology Conference

Y2 - 25 May 2020 through 31 July 2020

ER -