ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

Babar, Mohammad; Khan, Muhammad Sohail

doi:10.1177/15501477211035332

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…(4) Path planning algorithm based on information age: the UAV uses a path planning algorithm based on information age according to the map information to calculate the optimal path from the starting point to the end point, and then flies along that path until it completes the delivery task or runs out of power. (5) The model in this paper: the UAV uses the intelligent delivery UAV path planning and control model based on IoT and edge computing proposed in this paper based on the map information, utilizes the powerful arithmetic power of the edge servers to make up for the lack of the on-board platforms, carries out the cluster's information processing and fusion on the side of the base station, and assists the cluster in real-time task trajectory planning, so as to achieve a more stable connection, a more secure flight, and a more efficient The mission is more stable connection, safer flight, and more efficient.…”

Section: Resultsmentioning

confidence: 99%

“…Current research has mainly focused on the enhancement of quality of service and user experience of IoT applications by edge computing, while less consideration has been given to the enhancement of intelligence and efficiency of IoT devices by edge computing. As a typical IoT device, the improvement of intelligence and efficiency of UAVs can not only enhance the function and performance of UAVs, but also reduce the operation cost and risk of UAVs [5]. Therefore, it is a meaningful work to study how to optimize the path planning and control of UAVs using edge computing.…”

Section: Introductionmentioning

confidence: 99%

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Path Planning and Control of Intelligent Delivery UAV Based on Internet of Things and Edge Computing

Zhang

2024

IJACSA

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This paper investigates the intelligent delivery UAV path planning and control problem based on the Internet of Things and edge computing, and proposes a novel model and algorithm to realize the collaborative optimization of the path planning and control of the UAV, which improves the intelligence level and flight efficiency of the UAV. In this paper, the mathematical model of UAV path planning and control is firstly established, the relationship and influencing factors among the elements of UAV, edge server, delivery task, path planning and control are analyzed, and the optimization objectives and constraints are proposed. Then, this paper designs an algorithmic framework for UAV path planning and control, using the support and guidance of edge computing to achieve the cooperative optimization of path planning and control of UAVs, taking into account the constraints and objectives of the UAVs themselves, as well as the synergy and competition between UAVs. Then, this paper proposes specific algorithms for UAV path planning and control, adopting methods such as metaheuristics, to solve the optimization problem of UAV path planning and control, and improve the intelligent level and flight performance of UAVs.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path Planning and Control of Intelligent Delivery UAV Based on Internet of Things and Edge Computing

Zhang

2024

IJACSA

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“…For example, in Samy et al [ 126 ], clustering of fog nodes to balance the network load is used to increase scalability. Autoscaling is a solution that aims to optimize the use of resources [ 149 ]. However, the edge-computing environment is very dynamic which impacts the availability of nodes in a distributed edge-based infrastructure, so the load on each node may change continuously.…”

Section: Open Issues and Future Directionsmentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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“…and general applications in cloud-enabled fog computing environments [ 2 , 3 ]. Note that in fog computing environments, Internet of Things device tasks are offloaded to nearby edge servers [ 4 , 5 ]. Due to their lightweight properties, Internet of Things applications do not require supercomputer-class computing resources.…”

Section: Introductionmentioning

confidence: 99%

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

Lim

2022

Sensors

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The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption.

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ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems

Cited by 10 publications

References 37 publications

Path Planning and Control of Intelligent Delivery UAV Based on Internet of Things and Edge Computing

Path Planning and Control of Intelligent Delivery UAV Based on Internet of Things and Edge Computing

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

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