Enhancing IoT performance in wireless and mobile networks through named data networking (NDN) and edge computing integration

Available online The rapid expansion of the Internet of Things (IoT) in wireless and mobile networks demands novel approaches for efficient data transmission and management. Traditional IP-based networking architectures struggle to meet the high-speed, low-latency, and scalable requirements of IoT. Named Data Networking (NDN), a content-centric networking paradigm, provides an alternative by focusing on data retrieval based on content names rather than device addresses. However, while NDN offers significant advantages in reducing latency and improving data dissemination, its integration with edge computing for real-time IoT applications remains suboptimal due to challenges in dynamic resource allocation, routing efficiency, and robustness under uncertain network conditions. This paper proposes a novel adaptive NDN-Edge Computing framework that dynamically optimizes data retrieval, caching, and computational resource allocation. Unlike prior studies that focus solely on theoretical models or static configurations, our framework introduces a multi-objective optimization model for balancing latency, reliability, and energy efficiency in IoT environments. Additionally, we formulate a robust optimization approach to ensure network resilience against unpredictable traffic surges, topology changes, and edge node failures. Through extensive simulations and real-world case studies, we demonstrate that the proposed integration significantly improves latency (up to 25 % reduction), energy efficiency (15 % improvement), and cache hit ratio (20 % increase) compared to conventional NDN and edge computing approaches. This work contributes to the ongoing research by providing a scalable, adaptive, and resilient NDN-edge computing framework that enhances IoT data processing while addressing critical limitations of existing solutions. Future work will focus on security enhancements and the integration of blockchain for decentralized trust management in IoT ecosystems.

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Publication Details

Journal
Computer Networks
Published
2025-03-30
DOI
https://doi.org/10.1016/j.comnet.2025.111267
Citations
8
Primary Topic
Caching and Content Delivery
Type
article
Field-Weighted Citation Impact
11.90
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article

Enhancing IoT performance in wireless and mobile networks through named data networking (NDN) and edge computing integration

Ahmed M. Alwakeel
8 citations
Computer Networks
Caching and Content Delivery
11.90
article

Enhancing IoT performance in wireless and mobile networks through named data networking (NDN) and edge computing integration

Ahmed M. Alwakeel
article en
8 citations

Abstract

Available online The rapid expansion of the Internet of Things (IoT) in wireless and mobile networks demands novel approaches for efficient data transmission and management. Traditional IP-based networking architectures struggle to meet the high-speed, low-latency, and scalable requirements of IoT. Named Data Networking (NDN), a content-centric networking paradigm, provides an alternative by focusing on data retrieval based on content names rather than device addresses. However, while NDN offers significant advantages in reducing latency and improving data dissemination, its integration with edge computing for real-time IoT applications remains suboptimal due to challenges in dynamic resource allocation, routing efficiency, and robustness under uncertain network conditions. This paper proposes a novel adaptive NDN-Edge Computing framework that dynamically optimizes data retrieval, caching, and computational resource allocation. Unlike prior studies that focus solely on theoretical models or static configurations, our framework introduces a multi-objective optimization model for balancing latency, reliability, and energy efficiency in IoT environments. Additionally, we formulate a robust optimization approach to ensure network resilience against unpredictable traffic surges, topology changes, and edge node failures. Through extensive simulations and real-world case studies, we demonstrate that the proposed integration significantly improves latency (up to 25 % reduction), energy efficiency (15 % improvement), and cache hit ratio (20 % increase) compared to conventional NDN and edge computing approaches. This work contributes to the ongoing research by providing a scalable, adaptive, and resilient NDN-edge computing framework that enhances IoT data processing while addressing critical limitations of existing solutions. Future work will focus on security enhancements and the integration of blockchain for decentralized trust management in IoT ecosystems.

Computer NetworksVol. 264
University of Tabuk (SA)
Affordable and clean energy
Openalex Percentile: Top 2%
Caching and Content Delivery
11.90
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Enhancing IoT performance in wireless and mobile networks through named data networking (NDN) and edge computing integration — Ahmed M. Alwakeel · Computer Networks (2025) | TGRS Research Map | TGRS