Seamless handover between WiMAX and UMTS

Khan, M. M. A.; Ismail, M. F.; Dimyati, Kaharudin

doi:10.1109/micc.2009.5431444

Cited by 9 publications

(2 citation statements)

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“…The 3GPP IMS comes as a promising solution for integrating UMTS and WiMAX since the system offers the needed interworking environment independent of any access technology. To resolve the problems of session continuity and keep IP address of MS unchanged during VHO between WiMAX and UMTS, [2] and [3] proposed methods to use 3GPP IMS and MIP for session continuity and maintaining IP address respectively.…”

Section: Gpp Ims-based Solutionsmentioning

confidence: 99%

“…However, WiMAX networks are still in its infancy with many technical challenges lying ahead. The main hindrance to its competitive edge is the lack of an infrastructure for the WiMAX Core Network (CN), which makes the deployment of WiMAX on its own a far future [2]. Besides that it is impossible to become a head-to-head competitor against current cellular networking infrastructures [3].…”

Section: Introductionmentioning

confidence: 99%

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UMTS-WiMAX Vertical Handover in Next Generation Wireless Networks

Alamri

Akkari

2011

IJDPS

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Section: Gpp Ims-based Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UMTS-WiMAX Vertical Handover in Next Generation Wireless Networks

Alamri

Akkari

2011

IJDPS

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A Survey on Media Independent Handover (MIH) and IP Multimedia Subsystem (IMS) in Heterogeneous Wireless Networks

Khattab

Alani

2013

Int J Wireless Inf Networks

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Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

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