# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 1 Introduction  
The rapid expansion of data-intensive applications such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) has significantly increased demands on Cloud and Fog computing infrastructures. Traditional electronic interconnects are encountering fundamental limitations, including bandwidth bottlenecks, increased latency, and inefficient energy utilization. Integrated photonics, which involves integrating multiple photonic components--lasers, modulators, detectors, and waveguides--onto a single chip, has emerged as a promising approach to overcome these challenges by enabling high-speed, low-energy optical data transmission [1, 2]. In Cloud computing networks, integrated photonics is transforming data center interconnectivity by achieving ultra-high bandwidth and greatly reduced energy consumption. Recent advances in photonic integrated circuits (PICs) demonstrate the potential toperform high-speed analog signal processing tasks with higher efficiency than conventional electronic solutions, significantly enhancing data handling capabilities in modern data centers [1]. Companies such as Intel and Nvidia are actively integrating optical input/output technologies directly onto processors and network switches, pushing interconnect speeds to terabit-per-second scales, which are essential for AI-driven workloads [3; 4]. Similarly, Fog computing networks, which extend Cloud functionality toward the network edge, stand to benefit substantially from integrated photonics. The compact size, energy-efficiency, and low latency characteristics of PICs make them ideally suited for deployment in edge devices, where real-time data analysis and rapid decision-making are crucial. Recent developments have illustrated the viability of photonic processors for executing complex edge computing tasks, such as real-time digital image convolution, achieving substantial gains in processing speed and energy-efficiency compared to traditional electronic processors [5]. The migration of telecommunication companies to photonic integrated CMOS-based technologies for components utilized in Fog and Cloud computing is driven by critical advantages such as low power consumption, high integration density, scalability, and cost efficiency. CMOS technology offers unparalleled miniaturization and energy-efficiency, which are essential for Fog computing nodes that require processing capabilities at the edge with minimal power budgets and compact footprints [6]. Additionally, the scalability of CMOS fabrication processes aligns perfectly with the growing demand for Cloud-based data centers where high-density integration significantly reduces operational costs and enhances system reliability [7]. The compatibility of CMOS with advanced semiconductor processes also ensures continuous performance improvements through technology scaling, making it the ideal choice for future-proofing the computing infrastructure required for Fog and Cloud paradigms [8]. PICs have become key enablers in modern Radio-over-Fiber (RoF) systems, offering compact, energy-efficient, and scalable solutions for high-frequency signal transport over optical fiber. PIC-based RoF systems integrate essential components such as modulators, photodetectors, multiplexers, and amplifiers onto a single chip, significantly reducing footprint, power consumption, and complexity compared to traditional discrete implementations [9; 10]. These integrated platforms support wide bandwidths and low-loss signal propagation, which are critical for transporting millimeter-wave and 5G signals in dense IoT environments [10]. In addition, RoF systems enabled by PICs facilitate seamless interconnection between IoT devices, Fog nodes, and Cloud data centers by centralizing signal processing functions while ensuring low-latency, high-fidelity transmission [11]. The work of [PERSON] et al.