MAACO: A Dynamic Service Placement Model for Smart Cities

Jojoa, Christian Humberto Cabrera; Svorobej, Sergej; Palade, Andrei; Kazmi, Aqeel; Clarke, Siobhán

doi:10.1109/tsc.2022.3143029

Cited by 11 publications

(9 citation statements)

References 41 publications

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“…Constraint (8) ensures that SR i,d = 1, if the processing node d ∈ N is turned on to place the IoT application i ∈ I; otherwise, SR i,d = 0.…”

Section: Data Transmission Optimizationmentioning

confidence: 99%

“…In each experiment, 10 million IoT devices were simulated: 30% were augmented reality (AR) users, 30% were infotainment users, 20% were healthcare users, and 20% were compute-intensive users as shown in Table 4. These percentages were determined based on [8,14] to simulate realistic environments, where augmented reality and infotainment applications had the largest share (i.e., IKEA Mobile App, Disney Coloring Book, Weather Channel Studio Effects) and (i.e., Netflix, Snapchat, Instagram), respectively; followed by healthcare applications (i.e., Remote Patient Monitoring, Heart-Rate Monitoring, Glucose Monitoring), and compute-intensive applications (i.e., Video Editing and Processing, Software Compiling, Machine Learning). )-upload/download data rates (UD i ).…”

Section: Design Of the Experimentsmentioning

confidence: 99%

“…Thus, there is a need for an efficient optimization approach to place and process IoT applications either in the fog, in the cloud, or both, depending on their requirements. Some of the related works in the field of smart cities have focused on the execution of IoT applications and services, as presented in [1,2,[4][5][6][7][8][9][10]. For example, Belli et al [5] have highlighted the key factors of an IoT infrastructure for smart cities along with their challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Muneeb et al [4] proposed a multi-layer architecture for analyzing IoT data in a cloud-fog computing, considering service latency, network usage, and energy consumption. Additionally, Cabrera et al [8] presented a dynamic model for application placement in smart cities based on certain Quality of Service (QoS) requirements, such as load balancing and service latency. However, their models have not addressed the execution of IoT services for smart city applications.…”

Section: Introductionmentioning

confidence: 99%

“…Input parameters and values of the model (IoT applications, cloud and fog, and networks)[8,11,13,14] …”

mentioning

confidence: 99%

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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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“…Constraint (8) ensures that SR i,d = 1, if the processing node d ∈ N is turned on to place the IoT application i ∈ I; otherwise, SR i,d = 0.…”

Section: Data Transmission Optimizationmentioning

confidence: 99%

Section: Design Of the Experimentsmentioning

confidence: 99%