A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems

Clavero, Sara; Montón, José Luis Bayo; Palanca-Barrio, Senén; Alvarado, Nestor Arreaga

doi:10.3390/s21020672

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…This helps to simplify the process of integrating these descriptions into the scanning tool ecosystem. A service discovery solution specifically designed for distributed embedded systems based on edge choreography has been proposed [66]. This solution utilizes Raspberry Pi machines at the IoT edge layer.…”

Section: Service Discovery In Mobile Iot Networkmentioning

confidence: 99%

A Review of Service Selection Strategies in Mobile IoT Networks

Mohamed Hadjkouider,

Kerrache,

Korichi

et al. 2024

IEEE Open J. Commun. Soc.

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With the proliferation of service-oriented architectures, the ubiquity of mobile networks, and an expanding spectrum of services available to users, the service selection process has become paramount in designing and implementing contemporary applications. Service selection strategies in mobile networks involve choosing the optimal services from many available options based on QoS (quality of service), bandwidth, and mobility, ultimately augmenting network efficiency and user experience. Nonetheless, particular challenges can impact the efficacy of these strategies. This paper presents a comprehensive survey of service selection strategies in mobile networks, transitioning from traditional approaches to emerging techniques, while also introducing a taxonomy classifying service selection methods based on criteria including mobile devices as service providers, architectural perspectives, computing devices allocation, evaluation metrics, and service selection methods. Additionally, We offer insights into the latest QoS-aware and energy-centric service selection methodologies, while addressing challenges related to battery life and energy efficiency, Quality of Service (QoS), security and privacy, as well as scalability and resource management. Additionally, we discuss prospective trajectories and future direction.

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Section: Service Discovery In Mobile Iot Networkmentioning

confidence: 99%

A Review of Service Selection Strategies in Mobile IoT Networks

Mohamed Hadjkouider,

Kerrache,

Korichi

et al. 2024

IEEE Open J. Commun. Soc.

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“…2. In the Edge layer are located the sensors and actuators of the system, sometimes this layer can also process data, but at a very low scale [23].…”

Section: A Implementation Designmentioning

confidence: 99%

PENTEST in IoT Devices for a Precision agriculture Environment

Arreaga,

Blanc,

Encalada

et al. 2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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Agriculture is constantly evolving, and many processes in the countryside have been automated owing to technology, giving rise to precision agriculture, which has helped to optimize the use of natural and agricultural inputs through the data sensed in the crops. Security in precision agriculture systems is vital to protect data; therefore, it has been proposed to perform penetration testing (PENTEST) or attacks on precision agriculture systems. In the experiment, open-source tools were used to determine vulnerabilities that may occur in real environments. Penetration tests were performed with Denial of Service, Man-inthe-Middle, and DNS spoofing attacks for devices on the Edge and in the Fog. These tests made it possible to affect the integrity, availability, and authenticity of the system. It was reported that service drops of more than 80% within the first minute, sensitive information theft, and page duplication allowed us to determine the vulnerability of this technology.

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“…While the KubeEdge approach is mostly centralized, some elements were related to a decentralized architecture, see discussion in Section IV-E 2. Distributed orchestgration is sometimes also referred to as choreography, see e.g [45,46,47]…”

mentioning

confidence: 99%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Kokkonen¹,

Lovén²,

Motlagh³

et al. 2022

Preprint

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Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.

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A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems

Cited by 5 publications

References 47 publications

A Review of Service Selection Strategies in Mobile IoT Networks

A Review of Service Selection Strategies in Mobile IoT Networks

PENTEST in IoT Devices for a Precision agriculture Environment

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

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