Supporting IoT applications deployment on edge-based infrastructures using multi-layer feature models

Cañete, Ángel; Amor, Mercedes; Fuentes, Lidia

doi:10.1016/j.jss.2021.111086

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…The use of photovoltaics can also generate energy efficiency each year, with photovoltaics producing 3,705 kWh of energy for greenhouse needs [41]. Using a multi-layer Feature Model can also reduce energy consumption [42]. Desalination systems and greenhouses for air, soil, plants, and land can generate around 85% of the water needed for tomato growth while also reducing cooling loads by more than 25% [43].…”

Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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The integration of IoT systems in agriculture has become a very important need amid the high population and increasingly limited farmland, which demands researchers to be more innovative in addressing these issues. Using IoT systems for automatic irrigation, fertilization, and cooling based on sensor values through internet networks. Poor internet connection leads to the failure of automation and sustainability in online conditions, which can be very dangerous for plants. This paper presents a new IoT-based control system divided into two parts: an automation system and an IoT system, which can maintain sustainability in online conditions to ensure that plants in the planting area are always controlled. In addition, the sensors used have undergone calibration processes to determine the increase in precision of the sensor values produced. The research results show that the system can maintain sustainability under online conditions. Mobile apps are available for control when the system is online, but if it goes offline and is unable to reconnect, the Arduino Mega will fully manage control using soil moisture sensor values for irrigation processes if the values fall below a certain threshold. This demonstrates the sustainability of the system in online conditions, allowing continuous control and reducing the risk of plant death in the planting area. The calibration result shows an increase in precision for the air temperature and humidity (DHT 11 sensor) by 7.14 and 6.15, respectively. Additionally, the precision improvement for the soil pH sensor is 1.81, while for the soil moisture sensor and the water flow sensor, it is 0.13 and 0.008, respectively.

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Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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“…Machine learning-based task allocation [28] DQN-D EC [54] IoT [38] MARL IoT [42] ACO IoT, 5G [51] FLOM-Opt [53] Q-Learning IoT, IoV [57] MA IoT [58] Heuristic, RL EC Quality of service task allocation [26] EC [32] MMAS EC [48] QT EC, IoT Resource-aware task allocation [76] MAPPO MEC [62] MDP EC [14] MAP VEC [75] JTORA VEC [72] JTORA MEC [74] JTORA MEC [15] DNF [16] MARL MEC [21] JTORA NOMA-MEC [37] EC [39] ELB [40] Knapsack MCS [70] EC, 5G…”

Section: Proposed Task Allocation Optimization (Rq2) Applied Network ...mentioning

confidence: 99%

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Patsias,

Amanatidis,

Karampatzakis

et al. 2023

Future Internet

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Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.

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“…Also, vEXgine provides excellent support for resolving the variability of architectural models, but in this case the downside is that users need to We illustrate the usage of our road map with the following interoperability scenario. Let us suppose that a user Joseph needs to model the variability of an edge computing application [115], analyze its variability, and sample some valid configurations that optimize the system's performance to generate the final product. He decides to use the S.P.L.O.T.…”

Section: Spl Tools Road Mapmentioning

confidence: 99%

Empirical analysis of the tool support for software product lines

2022

Self Cite

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For the last ten years, software product line (SPL) tool developers have been facing the implementation of different variability requirements and the support of SPL engineering activities demanded by emergent domains. Despite systematic literature reviews identifying the main characteristics of existing tools and the SPL activities they support, these reviews do not always help to understand if such tools provide what complex variability projects demand. This paper presents an empirical research in which we evaluate the degree of maturity of existing SPL tools focusing on their support of variability modeling characteristics and SPL engineering activities required by current application domains. We first identify the characteristics and activities that are essential for the development of SPLs by analyzing a selected sample of case studies chosen from application domains with high variability. Second, we conduct an exploratory study to analyze whether the existing tools support those characteristics and activities. We conclude that, with the current tool support, it is possible to develop a basic SPL approach. But we have also found out that these tools present several limitations when dealing with complex variability requirements demanded by emergent application domains, such as non-Boolean features or large configuration spaces. Additionally, we identify the necessity for an integrated approach with appropriate tool support to completely cover all the activities and phases of SPL engineering. To mitigate this problem, we propose different road map using the existing tools to partially or entirely support SPL engineering activities, from variability modeling to product derivation.

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Supporting IoT applications deployment on edge-based infrastructures using multi-layer feature models

Cited by 14 publications

References 28 publications

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

Empirical analysis of the tool support for software product lines

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