SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

Anbalagan, Sudha; Kumar, Dhananjay; J, Mercy Faustina; Raja, Gunasekaran; Ejaz, Waleed

doi:10.1016/j.future.2017.12.013

Cited by 27 publications

(18 citation statements)

References 36 publications

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“…7 and Fig. 8 show the average throughput and latency values of users when connecting the user with WiFi, LTE, both LTE and WiFi [13,41] and the proposed IR-DON architecture. When users with different non-safety application traffic need a network connection, they will automatically get connected to any one of the network based on their application type.…”

Section: Performance Analysismentioning

confidence: 99%

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Raja

Ganapathisubramaniyan²,

Anbalagan

et al. 2020

IEEE Internet Things J.

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A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle's data traffic from congested cellular network's licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module.

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Section: Performance Analysismentioning

confidence: 99%

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Raja

Ganapathisubramaniyan²,

Anbalagan

et al. 2020

IEEE Internet Things J.

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“…On the other hand, if the TCAM size is greater, then the concurrent method is used to find the attacker. A framework for detecting and mitigating DDoS using sFlow and Open-Flow is implemented [24]. This framework works with the constraint that there can be only a single application session for an HTTP request from a single IP address.…”

Section: Related Workmentioning

confidence: 99%

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

Shakil

Mohammed

Arul

et al. 2019

Trans Emerging Tel Tech

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“…A considerable volume of research is reported on the authentication [5,[35][36][37][38][39][40][41]48] and on key agreement [18,[42][43][44][45][46] process to overcome the security gap that needs improvement in the recently proposed symmetric [13], and asymmetric security solutions [17]. Machine learning based authentication [2,4,46] and aggregation [47] is proposed to overcome the security flaws in the dynamic environments. Various security flaws in EPS-AKA are addressed in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…3. The EPC framework includes LTE radio base stations (eNB) [31,47] that connect to the control plane components such as > TII-17-2276 < 4 MME, User Plane Gateways such as Packet Data Network Gateway (PDN-GW) & Serving GW (SGW). The MME acts as a Key Distributor (KD) [3][4][5][6][7][8][9] in the LTE.…”

Section: The Lte Architecture With Ipg-akamentioning

confidence: 99%

A Console GRID Leveraged Authentication and Key Agreement Mechanism for LTE/SAE

Arul

Raja

Chaudry

et al. 2018

IEEE Trans. Ind. Inf.

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 Abstract-Growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among bulk of the human race that happens to be cellular technology users, has fueled the adaptation to Long Term Evolution (LTE)/ System Architecture Evolution (SAE). The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in authentication mechanism used in LTE that without countermeasures might give super user rights to unauthorized users. The LTE uses static LTE Key (LTE-K) to derive the entire key hierarchy, i.e., LTE follows Evolved Packet System -Authentication and Key Agreement (EPS-AKA) based authentication, which discloses user identity, location, and other Personally Identifiable Information (PII). To counter this, we propose a public key cryptosystem named "International mobile subscriber identity Protected Console Grid based Authentication and Key Agreement (IPG-AKA) protocol" to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves security of authentication procedures, it also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper proves that IPG-AKA improves security in authentication procedure and performance in the LTE.

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SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

Abstract: Kashif (2017)SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks. Future Generation Computer Systems. pp. 1-11.

Cited by 27 publications

References 36 publications

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

Intelligent Reward-Based Data Offloading in Next-Generation Vehicular Networks

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

A Console GRID Leveraged Authentication and Key Agreement Mechanism for LTE/SAE

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