Mobility management—A personal perspective

Das, Sajal K.

doi:10.1016/j.comcom.2018.08.012

Cited by 9 publications

(9 citation statements)

References 96 publications

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“…All these are single path protocols, but multipath extensions of some of these have come to existence 8 . In the literature of MANETs, some researchers have implemented QoS extensions of specific routing protocols where most of those extensions incorporate route stability as an essential criterion 16‐20 …”

Section: Related Workmentioning

confidence: 99%

“…8 In the literature of MANETs, some researchers have implemented QoS extensions of specific routing protocols where most of those extensions incorporate route stability as an essential criterion. [16][17][18][19][20] In the study in Reference 21, two QoS based anycast algorithms RBH and RBHS have been proposed that work file both with accurate and inaccurate link state information. RBH uses path length as a metric while computing the probability of choosing the next link.…”

Section: Related Workmentioning

confidence: 99%

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Autoregressive moving average based anycast with support vector machine clustering in mobile ad‐hoc networks

Ghosh¹,

Banerjee

Sufian

et al. 2021

Trans Emerging Tel Tech

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Nodes in mobile ad-hoc networks (MANETs) are battery-powered, therefore, need energy-efficient techniques to cut down the cost of energy consumption. Energy consumption is directly related to route discovery, which could be reduced through anycasting. Anycast is an operation in MANETs, where the source sends messages to one particular destination node of anycast region.In general, the message transmitted is divided into certain packets, and those are transmitted continuously one after the other through selected routes. If the link from anycast source to destination breaks in between or an anycast destination goes out of the anycast region, in that case, a new anycast destination will have to be selected on a new route to the destination. This will incur huge message costs and energy. In this work, we select desirable anycast destinations based on their eligibility by using autoregressive moving average and support vector machine. Eligibility is modeled as a function of residual energy, location, the relative velocity concerning neighbors, and the number of packets successfully forwarded earlier by those neighbors' locations. Reducing the number of anycast receivers reduces the message cost of protocols significantly. We have compared our protocol's performance with the same of anycast-AODV, anycast-DSR, anycast-FAIR, and MQAR (mobility and QoS-aware anycast routing protocol) results show significant improvement. Collective improvement amounts to 89% more energy preservation and 39% delay on average.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Autoregressive moving average based anycast with support vector machine clustering in mobile ad‐hoc networks

Ghosh¹,

Banerjee

Sufian

et al. 2021

Trans Emerging Tel Tech

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“…This can be built in the form of component interaction with the organizational unit by describing each task, as displayed in the picture [26]. From the flow diagram developed above, a minimal main task is performed by the unit associated with measurable tasks [27], [28] as shown in the table below.…”

Section: Modeling Functions Of Business Directorate Of Human Resourcementioning

confidence: 99%

Communication Management of Digital Information Data in Human Resources as a Policy Making Strategic Program for University

Suryadi¹

2019

Adv. sci. technol. eng. syst. j.

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Modern universities aim at carrying capacity human resources component in its tri dharma (Three Pillars of Higher Education). UPI with sufficient conditions in the management of several resources has undoubtedly tried to establish itself as a modern university. The superior human resource support in every competition and the university's tri dharma is the primary resource of UPI. The results of all HR roles have become inputs for the management of digital data and information. Therefore, communication management containing the university's strategic policies can be effectively and promptly translated as well as implemented by human resources. To conduct dissemination and sustainability both nationally and internationally, this research formulated three main components in managing policy communication on human resources in a modern university, which include: (a) architecture vision, (b) value change; (c) flow diagrams, all of which refer to the achievement of vision and mission.

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“…Recently, a few studies have focused on mobility management issues in terms of mobility prediction, autonomic vertical handover, security, Software-Defined Network (SDN), Software Defined Network Virtualization (SDNV), Network Function Virtualization (NFV), and battery consumption models [33][34][35][36][37][38]. On top of that, a survey based on real measurement data conducted shown how Long Term Evolution -Advanced (LTE-A) network performs during the mobility of users in comparison with the first phase of LTE releases.…”

Section: Introductionmentioning

confidence: 99%

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

et al. 2020

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Ensuring a seamless connection during the mobility of various User Equipment (UE) will be one of the major challenges facing the practical implementation of the Fifth Generation (5G) networks and beyond. Several key determinants will significantly contribute to numerous mobility challenges. One of the most important determinants is the use of millimeter waves (mm-waves) as it is characterized by high path loss. The inclusion of various types of small coverage Base Stations (BSs), such as Picocell, Femtocell and drone-based BSs is another challenge. Other issues include the use of Dual Connectivity (DC), Carrier Aggregation (CA), the massive growth of mobiles connections, network diversity, the emergence of connected drones (as BS or UE), ultra-dense network, inefficient optimization processes, central optimization operation, partial optimization, complex relation in optimization operations, and the use of inefficient handover decision algorithms. The relationship between these processes and diverse wireless technologies can cause growing concerns in relation to handover associated with mobility. The risk becomes critical with high mobility speed scenarios. Therefore, mobility issues and their determinants must be efficiently addressed. This paper aims to provide an overview of mobility management in 5G networks. The work examines key factors that will significantly contribute to the increase of mobility issues. Furthermore, the innovative, advanced, efficient, and smart handover techniques that have been introduced in 5G networks are discussed. The study also highlights the main challenges facing UEs' mobility as well as future research directions on mobility management in 5G networks and beyond.

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Mobility management—A personal perspective

Cited by 9 publications

References 96 publications

Autoregressive moving average based anycast with support vector machine clustering in mobile ad‐hoc networks

Autoregressive moving average based anycast with support vector machine clustering in mobile ad‐hoc networks

Communication Management of Digital Information Data in Human Resources as a Policy Making Strategic Program for University

Key Challenges, Drivers and Solutions for Mobility Management in 5G Networks: A Survey

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