Abstract
The Internet of Things (IoT) is an integration of smart sensing devices with connection ability for ease of communication. Introducing decision-making systems (DMSs) in the IoT for request processing improves the ease of access and service reliability for mobile end users. This paper proposes a two-level DMS for user service traffic smoothing (TS) in IoT communications. The two-level decision-making (2LDM) method employs traffic-aware queuing and minimum time scheduling processes for controlling the request message flows. The decision-making algorithm is modeled on time-dependent processing for minimizing the time delay in the queuing and request scheduling. The DMS considers the attributes associated with the cloud and devices to classify the request messages to prevent resource mapping failures. The disagreement between the request processing and cloud response is resolved optimally for improving the end user communication reliability in terms of the delay and resource mapping failures. Simulations evaluate the proposed DMS performance for the following metrics: sum rate, access delay, failure probability, response latency, and queue utilization. The results indicate that the proposed TS-2LDM outperforms the existing traffic controlling methods by improving the sum rate and queue utilization with controlled delay, failure, and response time.
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References
Rohit S, Agarwal P, Mahapatra RP (2020) Evolution in big data analytics on internet of things: applications and future plan. Multimedia Big Data Computing for IoT Applications 163:453–477
Tewari A, Gupta BB (2020) Security, privacy and trust of different layers in internet-of-things (IoTs) framework. Futur Gener Comput Syst 108:909–920
Wang J, Yang Y, Wang T, Sherratt RS, Zhang J (2020) Big data service architecture: a survey. Journal of Internet Technology 21(2):393–405
Wang J, Tang Y, He S, Zhao C, Sharma PK, Alfarraj O, Tolba A (2020) LogEvent2vec: LogEvent-to-vector based anomaly detection for large-scale logs in internet of things. Sensors 20(9):2451
Sami N, Mufti T, Sohail SS, Siddiqui J, Kumar D Future internet of things (IOT) from cloud perspective: aspects, applications and challenges, in internet of things (IoT), pp. 515-532. Springer
Munoz R, Vilalta R, Yoshikane N, Casellas R, Martinez R, Tsuritani T, Morita I (2018) Integration of IoT, transport SDN, and edge/cloud computing for dynamic distribution of IoT analytics and efficient use of network resources. J Lightwave Technol 36(7):1420–1428
Wang J, Wu W, Liao Z, Sangaiah AK, Sherratt RS (2019) An energy-efficient offloading scheme for low latency in collaborative edge computing. IEEE Access 7:149182–149190
Al-Sharif ZA, Al-Saleh MI, Alawneh LM, Jararweh YI, Gupta B (2020) Live forensics of software attacks on cyber–physical systems. Futur Gener Comput Syst 108:1217–1229
Lin J-W, Chen C-H, Chang J (2013) QoS-aware data replication for data-intensive applications in cloud computing systems. IEEE Transactions on Cloud Computing 1(1):101–115
Deebak BD, Al-Turjman F, Aloqaily M, Alfandi O (2020) IoT-BSFCAN: A smart context-aware system in IoT-Cloud using mobile-fogging Future Generation Computer Systems 109:368–381. https://doi.org/10.1016/j.future.2020.03.050
Wang X, Ning Z, Guo S, Wang L (2020) Imitation learning enabled task scheduling for online vehicular edge computing. IEEE Transactions on Mobile Computing. https://doi.org/10.1109/TMC.2020.3012509
Tolba A, Al-Makhadmeh Z (2019) A recursive learning technique for improving information processing through message classification in IoT–cloud storage. Comput Commun 150:719–728
Mubeen S, Nikolaidis P, Didic A, Pei-Breivold H, Sandstrom K, Behnam M (2017) Delay mitigation in offloaded cloud controllers in industrial IoT. IEEE Access 5:4418–4430
Deng Y, Chen Z, Zhang D, Zhao M (2018) Workload scheduling toward worst-case delay and optimal utility for single-hop fog-IoT architecture. IET Commun 12(17):2164–2173
Wang J, Wu W, Liao Z, Sherratt RS, Kim GJ, Alfarraj O, Tolba A (2020) A probability preferred priori offloading mechanism in mobile edge computing. IEEE Access 8:39758–39767
Ning Z, Zhang K, Wang X, Guo L, Hu X, Hung J, Hu B, Kwok R (2020) Intelligent edge computing in internet of vehicles: a joint computation offloading and caching solution. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2020.2997832
Yousefpour A, Ishigaki G, Gour R, Jue JP (2018) On reducing IoT service delay via fog offloading. IEEE Internet Things J 5(2):998–1010
Zhao L, Sun W, Shi Y, Liu J (2018) Optimal placement of cloudlets for access delay minimization in SDN-based internet of things networks. IEEE Internet Things J 5(2):1334–1344
Shah-Mansouri H, Wong VWS (2018) Hierarchical fog-cloud computing for IoT systems: a computation offloading game. IEEE Internet Things J 5(4):3246–3257
Wang J, Qi H, Li K, Zhou X (2018) PRSFC-IoT: a performance and resource aware orchestration system of service function chaining for internet of things. IEEE Internet Things J 5(3):1400–1410
Fan Q, Ansari N (2018) Application aware workload allocation for edge computing-based IoT. IEEE Internet Things J 5(3):2146–2153
Dinh T, Kim Y (2017) An efficient sensor-cloud interactive model for on-demand latency requirement guarantee, 2017 IEEE International Conference on Communications (ICC)
Sun X, Ansari N (2018) Dynamic resource caching in the IoT application layer for smart cities. IEEE Internet Things J 5(2):606–613
Feng X, Zhang J, Ren C, Guan T (2018) An unequal clustering algorithm concerned with time-delay for internet of things. IEEE Access 6:33895–33909
Nan Y, Li W, Bao W, Delicato FC, Pires PF, Zomaya AY (2018) A dynamic tradeoff data processing framework for delay-sensitive applications in cloud of things systems. Journal of Parallel and Distributed Computing 112:53–66
Xu D, Jiao W, Yin Z, Wu B, Peng Y, Chen X, Chen F, Fang D (2018) Enabling robust and reliable transmission in internet of things with multiple gateways. Comput Netw 146:183–199
Sun J, Sun S, Li K, Liao D, Sangaiah AK, Chang V (2018) Efficient algorithm for traffic engineering in cloud-of-things and edge computing. Comput Electr Eng 69:610–627
Yamada Y, Shinkuma R, Iwai T, Onishi T, Nobukiyo T, Satoda K (2018) Temporal traffic smoothing for IoT traffic in mobile networks. Comput Netw 146:115–124
Kim S, Kim D-Y (2017) Efficient data-forwarding method in delay-tolerant P2P networking for IoT services. Peer-to-Peer Networking and Applications 11(6):1176–1185
Al-Osta M, Bali A, Gherbi A (2018) Event driven and semantic based approach for data processing on IoT gateway devices, Journal of Ambient Intelligence and Humanized Computing
Lee D, Lee H (2018) IoT service classification and clustering for integration of IoT service platforms. J Supercomput 74(12):6859–6875
Al-Qerem A, Alauthman M, Almomani A, Gupta BB (2020) IoT transaction processing through cooperative concurrency control on fog-cloud computing environment. Soft Comput 24(8):5695–5711
Chen M, Yixue H (2018) Task offloading for mobile edge computing in software defined ultra-dense network. IEEE Journal on Selected Areas in Communications 36(3):587–597
Mass J, Srirama SN, Chang C (2020) STEP-ONE: simulated testbed for edge-fog processes based on the opportunistic network environment simulator. J Syst Softw 110587
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This article is part of the Topical Collection: Special Issue on Security of Mobile, Peer-to-peer and Pervasive Services in the Cloud
Guest Editors: B. B. Gupta, Dharma P. Agrawal, Nadia Nedjah, Gregorio Martinez Perez, and Deepak Gupta
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Tolba, A. A two-level traffic smoothing method for efficient cloud–IoT communications. Peer-to-Peer Netw. Appl. 14, 2743–2756 (2021). https://doi.org/10.1007/s12083-021-01106-5
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DOI: https://doi.org/10.1007/s12083-021-01106-5