Cluster Computing, Год журнала: 2024, Номер 27(8), С. 10299 - 10324
Опубликована: Май 17, 2024
Язык: Английский
Internet of Things and Cyber-Physical Systems, Год журнала: 2023, Номер 3, С. 71 - 92
Опубликована: Янв. 1, 2023
Artificial Intelligence (AI) at the edge is utilization of AI in real-world devices. Edge refers to practice doing computations near users network's edge, instead centralised location like a cloud service provider's data centre. With latest innovations efficiency, proliferation Internet Things (IoT) devices, and rise computing, potential has now been unlocked. This study provides thorough analysis approaches capabilities as they pertain or AI. Further, detailed survey computing its paradigms including transition presented explore background each variant proposed for implementing Computing. Furthermore, we discussed approach deploying algorithms models on which are typically resource-constrained devices located network. We also technology used various modern IoT applications, autonomous vehicles, smart homes, industrial automation, healthcare, surveillance. Moreover, discussion leveraging machine learning optimized environments presented. Finally, important open challenges research directions field have identified investigated. hope that this article will serve common goal future blueprint unite stakeholders facilitates accelerate development
Язык: Английский
Процитировано
165
Sensors, Год журнала: 2023, Номер 23(3), С. 1639 - 1639
Опубликована: Фев. 2, 2023
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 5G/6G Internet of things (IoT) networks. This technology extends cloud by providing intermediate services at network improving quality service latency-sensitive applications. Many AI-based solutions with machine learning, deep swarm intelligence have exhibited high potential to perform cognitive sensing, management, big data analytics, security enhancement edge-based smart Despite many benefits, there are still concerns about required capabilities deal computational complexity learning techniques IoT analytics. Resource constraints computing, distributed efficient orchestration, synchronization resources all factors that require attention improvement cost-effective development In this context, paper aims explore confluence AI application domains order leverage existing research around these identify new perspectives. The improves user experience emergency situations, such vehicles, where critical inaccuracies or delays can lead damage accidents. These same most studies used evaluate success an application. review, we first provide in-depth analysis state art a focus on eight areas: agriculture, environment, grid, healthcare, industry, education, transportation, privacy. Then, present qualitative comparison emphasizes main objective confluence, roles use artificial edge, enabling technologies open challenges, future directions, perspectives identified discussed. Finally, some conclusions drawn.
Язык: Английский
Процитировано
116
Journal of Sensor and Actuator Networks, Год журнала: 2023, Номер 12(3), С. 41 - 41
Опубликована: Май 16, 2023
The advancement in technology has led to the integration of internet-connected devices and systems into emergency management response, known as Internet Emergency Services (IoES). This potential revolutionize way which services are provided, by allowing for real-time data collection analysis, improving coordination among various agencies involved response. paper aims explore use IoES response disaster management, with an emphasis on role sensors IoT providing information responders. We will also examine challenges opportunities associated implementation IoES, discuss impact this public safety crisis management. holds great promise speed efficiency well enhancing overall well-being citizens situations. However, it is important understand possible limitations risks technology, order ensure its effective responsible use. provide a comprehensive understanding implications
Язык: Английский
Процитировано
103
IEEE Communications Surveys & Tutorials, Год журнала: 2023, Номер 26(1), С. 619 - 669
Опубликована: Ноя. 30, 2023
The proliferation of ubiquitous Internet Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation agriculture are giving rise to a prodigious amount data requiring everincreasing computations services from cloud the edge network.Fog/Edge computing is promising distributed paradigm that has drawn extensive attention both industry academia.The infrastructural efficiency these paradigms necessitates adaptive resource management mechanisms for offloading decisions efficient scheduling.Resource Management (RM) non-trivial issue whose complexity result heterogeneous resources, incoming transactional workload, node discovery, Quality Service (QoS) parameters at same time, which makes efficacy resources even more challenging.Hence, researchers have adopted Artificial Intelligence (AI)-based techniques resolve abovementioned issues.This paper offers comprehensive review issues challenges Fog/Edge by categorizing them into provisioning task offloading, scheduling, service placement, load balancing.In addition, existing AI non-AI based state-of-the-art solutions been discussed, along with their QoS metrics, datasets analysed, limitations challenges.The survey provides mathematical formulation corresponding each categorized issue.Our work sheds light on research directions cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum Software-Defined Networking (SDN), can be integrated frameworks fog/edge-of-things improve business intelligence analytics amongst IoT-based applications.
Язык: Английский
Процитировано
97
Telematics and Informatics Reports, Год журнала: 2024, Номер 13, С. 100116 - 100116
Опубликована: Янв. 8, 2024
Язык: Английский
Процитировано
74
Energies, Год журнала: 2023, Номер 16(8), С. 3465 - 3465
Опубликована: Апрель 14, 2023
The Internet of Things (IoT) is a global network interconnected computing, sensing, and networking devices that can exchange data information via various protocols. It connect numerous smart thanks to recent advances in wired, wireless, hybrid technologies. Lightweight IoT protocols compensate for with restricted hardware characteristics terms storage, Central Processing Unit (CPU), energy, etc. Hence, it critical identify the optimal communication protocol system architects. This necessitates an evaluation next-generation networks improved connectivity. paper highlights significant wireless wired technologies their applications, offering new categorization conventional provides in-depth analysis detailed technical about stacks, limitations, applications. study further compares industrial IoT-compliant software simulation tools. Finally, summary current challenges, along broad overview future directions tackle next generation. aims provide comprehensive primer on concepts, protocols, insights academics professionals use contexts.
Язык: Английский
Процитировано
62
Internet of Things, Год журнала: 2023, Номер 23, С. 100866 - 100866
Опубликована: Июль 1, 2023
Язык: Английский
Процитировано
61
Multimedia Tools and Applications, Год журнала: 2024, Номер 83(28), С. 70961 - 71000
Опубликована: Фев. 6, 2024
Язык: Английский
Процитировано
22
Alexandria Engineering Journal, Год журнала: 2024, Номер 91, С. 12 - 29
Опубликована: Фев. 6, 2024
Energy efficiency is a key area of research aimed at achieving sustainable and environmentally friendly networks. With the rise in data traffic network congestion, IoT devices with limited computational power energy resources face challenges analyzing, processing, storing data. To address this issue, computing technology has emerged as an effective means conserving for by providing high-performance capabilities efficient storage to support collection processing. As such, energy-efficient computing, or "green computing," become focal point researchers seeking deploy large-scale This study provides comprehensive Survey recent efforts green best our knowledge, none studies literature have discussed all types (edge, fog, cloud) their role enabling massive networks terms efficiency. The article starts overview technologies then goes discussion empowering energy-saving techniques environments including, energy-aware architecture, aggregation compression, low-power hardware, scheduling, task offloading, switching on/off unused resources, virtualization, harvesting, cooling optimization. outline roadmap toward realizing vision environment networks; addition, open door interested follow continue Energy-Efficient Computing.
Язык: Английский
Процитировано
21
Cluster Computing, Год журнала: 2024, Номер 28(1)
Опубликована: Окт. 18, 2024
Язык: Английский
Процитировано
19