Customer Edge Switching

Kabir, Hammad; Kantola, Raimo; Santos, Jesús Llorente

doi:10.1002/9781119293071.ch9

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Collaborative edge-fog architectures leverage the combined computational resources of edge devices and fog nodes to overcome resource constraints and improve scalability. By distributing computing tasks across multiple edge and fog nodes, organizations can achieve higher performance, fault tolerance, and scalability compared to standalone edge devices (Kalil, 2018).…”

Section: Addressing Challenges and Limitationsmentioning

confidence: 99%

Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

Enoch Oluwademilade Sodiya,

Uchenna Joseph Umoga,

Alexander Obaigbena

et al. 2024

Int. J. Sci. Res. Arch.

View full text Add to dashboard Cite

The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in computing architectures, leading to the emergence and rapid evolution of edge computing. This review delves into the current state and prospects of edge computing within the IoT ecosystem, exploring its significance, challenges, and future potential. Edge computing, characterized by decentralized data processing at or near the source of data generation, has gained substantial traction owing to its ability to address critical concerns such as latency, bandwidth consumption, and privacy issues inherent in centralized cloud-based systems. By enabling data processing closer to the point of collection, edge computing minimizes latency and ensures real-time responses, making it indispensable for latency-sensitive applications like industrial automation, autonomous vehicles, and healthcare. The integration of edge computing with IoT devices has facilitated the creation of distributed computing architectures capable of handling massive data volumes generated by interconnected devices. This convergence enables efficient data aggregation, analysis, and decision-making at the network's edge, reducing the burden on centralized cloud infrastructure and optimizing resource utilization. Despite its numerous advantages, edge computing faces several challenges, including resource constraints, security vulnerabilities, and interoperability issues. Resource-constrained edge devices often lack the computational power and storage capacity required for complex analytics, necessitating innovative approaches to resource management and workload distribution. Moreover, the distributed nature of edge environments introduces new security risks, necessitating robust security measures to safeguard sensitive data and mitigate cyber threats. Looking ahead, the prospects of edge computing within the IoT ecosystem are promising, with advancements in edge hardware, software, and networking technologies driving innovation and adoption. Edge computing is poised to play a pivotal role in enabling the next wave of IoT applications, ranging from smart cities and autonomous systems to immersive experiences and personalized services. However, realizing the full potential of edge computing requires concerted efforts from industry stakeholders to address existing challenges and foster ecosystem-wide collaboration. The convergence of edge computing and IoT holds immense potential to revolutionize industries, reshape computing architectures, and empower a new era of intelligent, responsive, and decentralized systems.

show abstract

Section: Addressing Challenges and Limitationsmentioning

confidence: 99%