Abstract
With an exploding use of Mobiles, smartphones and IoT device, mobile edge computing (MEC) emerged as technological boon in computing paradigm. The device offloads the computationally intensive task as well as task relevant to storage to the MEC cloud server to meet the requirement of service delay, extend the battery lifespan of mobile, and resolve the problem of limited mobile device resources. With this reference we propose novel framework architecture with security to perform offloading of high storage and computationally intensive task to MEC server with minimum energy consumption and delay. For dynamic resource allocation, we employ two scheduling policy one at mobile side i.e. SJFP and other at MEC server side i.e. eSFFDRR. Before to offload task, first AES encryption technique is employed to secure input parameter, and then compressed this encrypted data to secure task data and utilize more bandwidth. Our experimental result depicts that for high storage and computationally intensive task our proposed framework can save 85–87% processing time, 70% of memory utilization with minimum energy consumption. The experimental result also proved that our proposed work improves the performance of computationally intensive mobile application with reduced consumption of mobile device resources like computation time, memory utilization, CPU usage and energy consumption.
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11 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04277-y
References
Ahn S, Lee J, Park S, Newaz SHS, Choi JK (2018) Competitive partial computation offloading for maximizing energy efficiency in mobile cloud computing. IEEE Access 6:899–912. https://doi.org/10.1109/ACCESS.2017.2776323
Al-Shuwaili A, Simeone O (2017) Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wirel Commun Lett 6(3):398–401. https://doi.org/10.1109/LWC.2017.2696539
Amiribesheli M, Benmansour A, Bouchachia A (2015) A review of smart homes in healthcare. J Ambient Intel Humaniz Comput 6(4):495–517
Barbarossa S, Sardellitti S, Di Lorenzo P (2013) Joint allocation of computation and communication resources in multiuser mobile cloudcomputing. In: IEEE Workshop on SPAWC
Barbera MV, Kosta S, Mei A, Stefa J (2013) To offload or not to offload. The bandwidth and energy costs of mobile cloud computing. In: IEEE INFOCOM
CISCO (2011) The Internet of Things how the next evolution of the Internet is changing everything, White paper, Apr. http://www.cisco.com/c/dam/enus/about/ac79/docs/innov/IoTIBSG 04 11FINAL.pdf
Chen X, Jiao L, Li W, Fu X (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw 24(5):2795–2808. https://doi.org/10.1109/TNET.2015.2487344
Chen M, Liang B, Dong M (2016) Joint offloading decision and resource allocation for multi-user multi-task mobile cloud. In: IEEE international conference on communications (ICC), Kuala Lumpur, 2016, pp 1–6. https://doi.org/10.1109/ICC.2016.7510999
Chiang M, Zhang T (2016) Fog and IoT: an overview of research opportunities. IEEE Internet Things J 3(6):854–864
Dai Y, Xu D, Maharjan S, Zhang Y (2018) Joint computation offloading and user association in multi-task mobile edge computing. IEEE Trans Veh Technol 67(12):12313–12325. https://doi.org/10.1109/TVT.2018.2876804
Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mobile Comput 1587–1611:13
Elgendy I, Zhang W, Liu C, Hsu C (2018) An efficient and secured framework for mobile cloud computing. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2018.2847347
Gubbi J, Buyya R, Marusic S, Palaniswmi M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. ELSEVIER Future Gener Compt Syst 29(7):1645–1660
Guerrero C, Lera I, Juiz C (2019) A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Human Comput 10(6):2435–2452
Guo J, Liu H (2018) Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks. IEEE Trans Veh Technol 67(5):14–4526. https://doi.org/10.1109/TVT.2018.2790421
Guo H, Liu J, Qin H, Zhang H (2017) Collaborative Computation Offloading for Mobile-Edge Computing over Fiber-Wireless Networks, GLOBECOM 2017—2017 IEEE Global Communications Conference, Singapore, pp 1–6. https://doi.org/10.1109/GLOCOM.2017.8254982
Hu ChY, Patel M, Sabella D, Sprecher N, Young V (2015) Mobile Edge Computing A key technology towards 5G-First edition
Huang D, Wang P, Niyato D (2012) A dynamic offloading algorithm for mobile computing. IEEE Trans Wirel Commun 11(6):1991–1995. https://doi.org/10.1109/TWC.2012.041912.110912
Iosifidis G, Gao L, Huang J, Tassiulas L (2013) An iterative double auction mechanism for mobile data offloading. In: IEEE WiOpt
Liu J, Mao Y, Zhang J, Letaief KB (2016) Delay-optimal computation task scheduling for mobile-edge computing systems. In: IEEE International Symposium on Information Theory (ISIT), Barcelona, pp 1451–1455. 10.1109/ISIT.2016.7541539
Lyu X et al (2018) Selective offloading in mobile edge computing for the green internet of things. IEEE Network 32(1):54–60. https://doi.org/10.1109/MNET.2018.1700101
Ma X, Lin C, Xiang X, Chen C (2015) Game-theoretic analysis of computation offloading for cloudlet-based mobile cloud computing. In: The 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Nov 2–6, 2015, Cancun, Mexico. ACM Press, New York, 271–278
Maimó LF, Celdrán AH, Pérez MG, Clemente FJG, Pérez GM (2019) Dynamic management of a deep learning-based anomaly detection system for 5G networks. J Ambient Intell Humaniz Comput 10(8):3083–3097
Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tut 19(4):2322–2358
Mishra S, Sangaiah AK, Sahoo MN et al (2019) Pareto-optimal cost optimization for large scale cloud systems using joint allocation of resources. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01601-x
Pham Q, Leanh T, Tran NH, Park BJ, Hong CS (2018) Decentralized computation offloading and resource allocation for mobile-edge computing: a matching game approach. IEEE Access 6:75868–75885. https://doi.org/10.1109/ACCESS.2018.2882800
Ren J, Yu G, Cai Y, He Y (2018) Latency optimization for resource allocation in mobile-edge computation offloading. IEEE Trans Wireless Commun 17(8):5506–5519
Ren J, Yu G, Cai Y, He Y, Qu F (2017) Partial offloading for latency minimization in mobile-edge computing, GLOBECOM 2017—2017 IEEE Global Communications Conference, Singapore, pp 1–6. https://doi.org/10.1109/GLOCOM.2017.8254550
Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23
Talaat FM, Saraya MS, Saleh AI, Ali HA, Ali SHA (2020) load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment. J Ambient Intell Humaniz Comput 1–16
Tao X, Ota K, Dong M, Qi H, Li K (2017) Performance guaranteed computation offloading for mobile-edge cloud computing. IEEE Wirel Commun Lett 6(6):774–777. https://doi.org/10.1109/LWC.2017.2740927
Wu S, Tseng Y, Lin C, Sheu J (2002) A multi-channel mac protocol with power control for multi-hop mobile ad hoc networks. Comput J 45(1):101–110
Xu Q, Li D, Zhu H (2019) Energy-saving computation offloading by joint data compression and resource allocation for mobile-edge computing. IEEE Commun Lett 23(4):704–707. https://doi.org/10.1109/LCOMM.2019.2897630
You C, Huang K, Chae H, Kim B (2017) Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans Wirel Commun 16(3):1397–1411. https://doi.org/10.1109/TWC.2016.2633522
Zhang K et al (2016) Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access 4:5896–5907. https://doi.org/10.1109/ACCESS.2016.2597169
Zhang J, Xia W, Yan F, Shen L (2018) Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access 6:19324–19337. https://doi.org/10.1109/ACCESS.2018.2819690
Zhang J et al (2017) An evolutionary game for joint wireless and cloud resource allocation in mobile edge computing. In: 2017 9th international conference on wireless communications and signal processing (WCSP), Nanjing, pp 1–6. https://doi.org/10.1109/WCSP.2017.8170956
Zhao P, Tian H, Qin C, Nie G (2017) Energy-saving offloading by jointly allocating radio and computational resources for mobile edge computing. IEEE Access 5:11255–11268. https://doi.org/10.1109/ACCESS.2017.2710056
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04277-y
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Anoop, S., Singh, J.A.P. RETRACTED ARTICLE: Multi-user energy efficient secured framework with dynamic resource allocation policy for mobile edge network computing. J Ambient Intell Human Comput 12, 7317–7332 (2021). https://doi.org/10.1007/s12652-020-02407-y
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DOI: https://doi.org/10.1007/s12652-020-02407-y