{"title":"Enhancing IoT performance in wireless and mobile networks through named data networking (NDN) and edge computing integration","authors":"Ahmed M. Alwakeel","doi":"10.1016/j.comnet.2025.111267","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"264 ","pages":"Article 111267"},"PeriodicalIF":4.4000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S138912862500235X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
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.
期刊介绍:
Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.