The relentless proliferation of Internet of Things maneuvers generates colossal volumes of data at the network edge, straining traditional cloud-centric paradigms with inherent limitations in latency, bandwidth, resilience, and privacy. This necessitates a paradigm shift towards distributed intelligence. Fog Computing arises as a pivotal architectural paradigm, extending computational, storage, and networking capabilities closer to the data source, thereby mitigating latency, reducing bandwidth consumption, and enhancing real-time processing capabilities for critical IoT applications. This paper delves into the foundational architectureof Fog Computing, dissecting its intricate layering from diverse edge devices (sensors, actuators, and gateways) through intermediate Fog nodes (routers, switches, and micro-data centers) to the overarching cloud hierarchy. We analyze key design principles, including geographical distribution, heterogeneity, virtualization, and the interplay between local and global intelligence. Furthermore, we examine critical considerations for its effective deployment in diverse IoT verticals, addressing challenges such as dynamic resource orchestration, energy efficiency, interoperability, robust security protocols across heterogeneous environments, and the need for standardized management frameworks. By exploring both the theoretical underpinnings and practical implications, this work underscores Fog Computing’s transformative potential to unlock new frontiers in intelligent, responsive, and resilient IoT ecosystems, paving the way for truly autonomous and data-driven decision-making at the edge.

Fog computing, Internet of Things (IoT), architecture and deployment, management, geographical distribution, intrusion detection systems

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1. Mulani AO, Bang AV, Birajadar GB, Deshmukh AB, Jadhav HM, Liyakat KK. IoT based air, water, and soil monitoring system for pomegranate farming. Ann Agri-Bio Res. 2024;29(2):71–86.
2. Parihar B, Kiran A, Valaboju S, Rashid SZ, Liyakat KK, DR AS. Enhancing data security in distributed systems using homomorphic encryption and secure computation techniques. In: ITM Web Conf. 2025;76:02010. EDP Sciences.
3. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N. HEECCNB: An efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In: 2023 7th Int Conf Image Inf Process (ICIIP). 2023. p. 407–10. IEEE.
4. Majeed R, Abdullah NA, Faheem Mushtaq M, Umer M, Nappi M. Intelligent cyber-security system for IoT-aided drones using voting classifier. Electronics. 2021;10(23):2926.
5. Chaari MZ, Al-Maadeed S. Increase the efficiency of IoT devices by using the wireless power transmission in the industrial revolution 4.0. Int J Online Biomed Eng. 2021;17(7).
6. Kanday R, Pimpale Y, Gupta S. Smart biomedical devices: smart alcohol detection system with engine cutoff and notifier. AIP Conf Proc. 2023;2800(1):020226.
7. Malek MS, Gundaliya PJ. Negative factors in implementing public–private partnership in Indian road projects. Int J Constr Manag. 2023;23(2):234–42.
8. Khatun MA, Chowdhury N, Uddin MN. Malicious nodes detection based on artificial neural network in IoT environments. In: 2019 22nd Int Conf Comput Inf Technol (ICCIT). IEEE; 2019. p. 1–6.
9. Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, et al. Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 2016 Int Symp Netw Comput Commun (ISNCC). IEEE; 2016. p. 1–6.
10. Lokuliyana S, Jayakody A, Dabarera GS, Ranaweera RK, Perera PG, Panangala PA. Location-based garbage management system with IoT for smart city. In: 2018 13th Int Conf Comput Sci Educ (ICCSE). IEEE; 2018. p. 1–5.
11. Sarma H, Huzuri D, Deka MK. A real-time implementation of an IoT based vehicle health monitoring system. Int J Recent Technol Eng. 2021;10:2277–3878.
12. Dinesh M, Arvind C, Sreeja Mole SS, Subash Kumar CS, Chandra Sekar P, Somasundaram K, et al. An energy efficient architecture for furnace monitor and control in foundry based on Industry 4.0 using IoT. Sci Program. 2022;2022:1128717.

The relentless proliferation of Internet of Things maneuvers generates colossal volumes of data at the network edge, straining traditional cloud-centric paradigms with inherent limitations in latency, bandwidth, resilience, and privacy. This necessitates a paradigm shift towards distributed intelligence. Fog Computing arises as a pivotal architectural paradigm, extending computational, storage, and networking capabilities closer to the data source, thereby mitigating latency, reducing bandwidth consumption, and enhancing real-time processing capabilities for critical IoT applications. This paper delves into the foundational architectureof Fog Computing, dissecting its intricate layering from diverse edge devices (sensors, actuators, and gateways) through intermediate Fog nodes (routers, switches, and micro-data centers) to the overarching cloud hierarchy. We analyze key design principles, including geographical distribution, heterogeneity, virtualization, and the interplay between local and global intelligence. Furthermore, we examine critical considerations for its effective deployment in diverse IoT verticals, addressing challenges such as dynamic resource orchestration, energy efficiency, interoperability, robust security protocols across heterogeneous environments, and the need for standardized management frameworks. By exploring both the theoretical underpinnings and practical implications, this work underscores Fog Computing’s transformative potential to unlock new frontiers in intelligent, responsive, and resilient IoT ecosystems, paving the way for truly autonomous and data-driven decision-making at the edge.

Fog computing, Internet of Things (IoT), architecture and deployment, management, geographical distribution, intrusion detection systems

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Mulani AO, Bang AV, Birajadar GB, Deshmukh AB, Jadhav HM, Liyakat KK. IoT based air, water, and soil monitoring system for pomegranate farming. Ann Agri-Bio Res. 2024;29(2):71–86.
2. Parihar B, Kiran A, Valaboju S, Rashid SZ, Liyakat KK, DR AS. Enhancing data security in distributed systems using homomorphic encryption and secure computation techniques. In: ITM Web Conf. 2025;76:02010. EDP Sciences.
3. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N. HEECCNB: An efficient IoT-cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In: 2023 7th Int Conf Image Inf Process (ICIIP). 2023. p. 407–10. IEEE.
4. Majeed R, Abdullah NA, Faheem Mushtaq M, Umer M, Nappi M. Intelligent cyber-security system for IoT-aided drones using voting classifier. Electronics. 2021;10(23):2926.
5. Chaari MZ, Al-Maadeed S. Increase the efficiency of IoT devices by using the wireless power transmission in the industrial revolution 4.0. Int J Online Biomed Eng. 2021;17(7).
6. Kanday R, Pimpale Y, Gupta S. Smart biomedical devices: smart alcohol detection system with engine cutoff and notifier. AIP Conf Proc. 2023;2800(1):020226.
7. Malek MS, Gundaliya PJ. Negative factors in implementing public–private partnership in Indian road projects. Int J Constr Manag. 2023;23(2):234–42.
8. Khatun MA, Chowdhury N, Uddin MN. Malicious nodes detection based on artificial neural network in IoT environments. In: 2019 22nd Int Conf Comput Inf Technol (ICCIT). IEEE; 2019. p. 1–6.
9. Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, et al. Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 2016 Int Symp Netw Comput Commun (ISNCC). IEEE; 2016. p. 1–6.
10. Lokuliyana S, Jayakody A, Dabarera GS, Ranaweera RK, Perera PG, Panangala PA. Location-based garbage management system with IoT for smart city. In: 2018 13th Int Conf Comput Sci Educ (ICCSE). IEEE; 2018. p. 1–5.
11. Sarma H, Huzuri D, Deka MK. A real-time implementation of an IoT based vehicle health monitoring system. Int J Recent Technol Eng. 2021;10:2277–3878.
12. Dinesh M, Arvind C, Sreeja Mole SS, Subash Kumar CS, Chandra Sekar P, Somasundaram K, et al. An energy efficient architecture for furnace monitor and control in foundry based on Industry 4.0 using IoT. Sci Program. 2022;2022:1128717.