Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

Mohamed, Nader; Al-Jaroodi, Jameela; Lazarova-Molnar, Sanja; Jawhar, Imad

doi:10.3390/electronics10232918

Cited by 21 publications

(10 citation statements)

References 168 publications

(184 reference statements)

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“…The concept of smart cities has emerged with many urban services and initiatives (e.g., smart environment, smart economy, smart living, smart governance, etc.) that efficiently manage and optimize resources to achieve sustainability [12]. Therefore, providing innovative IoT services and solutions to enhance people's quality of life is one way to achieve this aim [7].…”

Section: The Proposed Systemmentioning

confidence: 99%

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Aldossary¹

2023

Computers, Materials &Amp; Continua

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In the smart city paradigm, the deployment of Internet of Things (IoT) services and solutions requires extensive communication and computing resources to place and process IoT applications in real time, which consumes a lot of energy and increases operational costs. Usually, IoT applications are placed in the cloud to provide high-quality services and scalable resources. However, the existing cloud-based approach should consider the above constraints to efficiently place and process IoT applications. In this paper, an efficient optimization approach for placing IoT applications in a multi-layer fog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into account IoT application requirements, available resource capacities, and geographical locations of servers, which would help optimize IoT application placement decisions, considering multiple objectives such as data transmission, power consumption, and cost. Simulation experiments were conducted with various IoT applications (e.g., augmented reality, infotainment, healthcare, and compute-intensive) to simulate realistic scenarios. The results showed that the proposed approach outperformed the existing cloud-based approach in terms of reducing data transmission by 64% and the associated processing and networking power consumption costs by up to 78%. Finally, a heuristic approach was developed to validate and imitate the presented approach. It showed comparable outcomes to the proposed model, with the gap between them reach to a maximum of 5.4% of the total power consumption.

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Section: The Proposed Systemmentioning

confidence: 99%

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Aldossary¹

2023

Computers, Materials &Amp; Continua

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“…. , N} pursuing vehicles capturing the same evading vehicle, the reward of each corresponding pursuing vehicle is 1 n . The global reward is the sum of all pursuing vehicles rewards, and an evading vehicle is captured with the global reward of +1.…”

Section: Problem Statementmentioning

confidence: 99%

“…Meanwhile, a partially observable scenario is considered in which each agent draws individual observation o ∈ O according to observation function Z(s, k) : S × K → O. Each agent has an action-observation history 1) is the discount factor.…”

Section: Dec-pomdpmentioning

confidence: 99%

“…The Internet of Vehicles (IoVs) is a typical application of Internet of Things (IoT) technologies in the Intelligent Transportation System (ITS) [1][2][3]. With the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, IoVs combined with Artificial Intelligence (AI) can improve the decision-making of vehicles and the efficiency of ITS [4,5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

T3OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area

Zheng¹,

Wu²,

Wang³

et al. 2022

Preprint

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Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

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“…As generalized in [ 3 ], the IoT layer is closest to end-users and contains various IoT devices. These devices provide various IoT applications for the end-users, including, but not limited to, agriculture [ 4 ], structural health monitoring [ 5 ], healthcare [ 6 ], vehicles [ 7 ], smart cities [ 8 ], and smart homes [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Agent-Based Orchestrator Model for Fog Computing

Liutkevičius

Morkevičius

Venčkauskas

et al. 2022

Sensors

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Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed.

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Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

Cited by 21 publications

References 168 publications

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

T3OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area

Distributed Agent-Based Orchestrator Model for Fog Computing

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