CBILEM: A novel energy aware mobility handling protocol for SDN based NDN-MANETs

Khalid, Ammarah; Rehman, Rana Asif; Burhan, Muhammad

doi:10.1016/j.adhoc.2022.103049

Cited by 9 publications

(4 citation statements)

References 56 publications

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“…This proposes an intelligent method for improving the movement of producers, utilizing predictive analytics to optimize efficiency in accessing material. The study [61] examines techniques for producer mobility that prioritize energy efficiency, considering the effects on content retrieval and network resource usage. The authors acknowledge the importance of sustainability in NDN by connecting producer mobility strategies with energy-conscious concerns.…”

Section: Technological Advancement Ndn Producer Mobility Strategiesmentioning

confidence: 99%

Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

Azamuddin,

Mohd Aman,

Sallehuddin

et al. 2024

Mathematics

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One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies’ strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.

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Section: Technological Advancement Ndn Producer Mobility Strategiesmentioning

confidence: 99%

Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

Azamuddin,

Mohd Aman,

Sallehuddin

et al. 2024

Mathematics

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“…Specifically, the management plane involves fault, mobility, and energy management. Thus, it collects performance metrics such as energy consumption, device mobility metrics (position, velocity, acceleration), and device availability/failure rate [270].…”

Section: Types Of Datamentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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“…The authors in [94] proposed a controller-based intelligent location and energy-aware protocol to address the consumer, producer, and intermediate mobility issues in wireless mobile ad-hoc networks. The SDN controller with an overall topological view and battery information of each networking node provide updated route information to the nodes in case of mobility events.…”

Section: Location-basedmentioning

confidence: 99%

“…The SDN controller exhibits similarities with the anchor node or RV in the network, however, it offers better network performance and reduced overhead. In SDN, the routing path states (control packet) and the information related to mobility events [94] are typically sent proactively and periodically to avoid any issue that may occur due to controller ignorance of recent path updates and mobility. The SDN controller on receiving the routing path and mobility events information -recognized by the packet type, checks for path changes that may occur owing to changes in mobile nodes' location.…”

Section: Software-defined Networkingmentioning

confidence: 99%

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

et al. 2023

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Information-centric networking (ICN) and its fruition, the named data networking (NDN) is a paradigm shift from host-centric address-based communication architecture to the content-centric namebased one. ICN intends to resolve various major issues faced by today's internet architecture such as privacy, security, consistent routing, and mobility, to name a few. With the massive increase of mobile data traffic in today's era, mobility is one of the major concerns in networking. On the one hand, ICN realization i.e., the NDN follows a pull-based communication model and natively supports the consumer (end-user) mobility in wired networks by maintaining the forwarding states on intermediate nodes. Nevertheless, the mobile consumer nodes confront issues in wireless networking environments such as excessive energy consumption as a result of request flooding, content retrieval delays due to intermittent connectivity, and bandwidth consumption due to the broadcasting nature of the wireless medium, among others. The producer (contentgenerator) mobility, on the other hand, was not initially supported in the original architectural design of NDN for both wired and wireless networks. Therefore, to efficiently address the degradation issues incurred by mobile consumer/producer nodes, a plethora of mobility management schemes have been proposed over the recent few years. In this paper, we provided a detailed survey on the existing research efforts-in the context of producer as well as consumer mobility, that have been proposed in the literature. Moreover, we outlined various research directions considering the role of mobility in futuristic technologies such as artificial intelligence-enabled smart networks, edge computing, software-defined networking, vehicular-fog computing, autonomous driving, and resource-constrained Internet of Things.INDEX TERMS Named data networking, mobility management, producer mobility, wireless networking, artificial intelligence, edge computing, vehicular fog computing, autonomous driving. I. INTRODUCTIONT HE 21st century is experiencing immense growth in the production of data 1 as the result of the adaptation of the Internet of Things (IoT) vision in almost every walk of life [1]- [4]. The produced data is shared by enabling the communication between two parties: 1) the producer node, which could be a resource-rich server device on the Internet or resource-constrained sensor node in wireless sensor 1 We use Data with a uppercase "D" to refer to ICN/NDN Data packet whereas data with lowercase "d" refer to general data. networks (WSNs), among others, and 2) the consumer node, which could be an end-user using a mobile application on a smartphone, browsing a website on a laptop, or a base station in the WSNs environment. Due to the heavy data production in real-time scenarios, the current internet architecture and other existing wireless networking environments such as mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), and WSNs, are facing significant challenges specifically when the ...

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CBILEM: A novel energy aware mobility handling protocol for SDN based NDN-MANETs

Cited by 9 publications

References 56 publications

Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

A Comprehensive Survey on Knowledge-Defined Networking

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

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