Agriculture Sensor-Cloud Infrastructure and Routing Protocol in the Physical Sensor Network Layer

Kim, Kyu Hyung; Lee, Sungwon; Yoo, Hyung-Joun; Kim, Dongkyun

doi:10.1155/2014/437535

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Kim et al. ( 2014 ) proposed Agriculture Sensor-Cloud Infrastructure (ASCI) that composed of three layers: Physical sensor layer, Virtual Sensor Layer, Service Cloud Layer. The goal of this framework is to process massive data, provide various types of agricultural services, and allow rapid and reliable packet delivery in WSNs.…”

Section: State-of-the-artsmentioning

confidence: 99%

Cloud-based virtualization environment for IoT-based WSN: solutions, approaches and challenges

Almurisi

Tadisetty

2022

J Ambient Intell Human Comput

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Internet of Things (IoT) is an ever-growing technology that enables advanced communication among millions of various devices to provide ubiquitous services without human intervention. The potential growth of electronic devices in sensing systems has led to the realization of IoT paradigm where applications depend on sensors to interact with the environment and collect data in a real-time scenario. Nowadays, smart applications require fast data acquisition, parallel processing, and dynamic resource sharing. Unfortunately, these requirements can not be supported efficiently with traditional Wireless Sensor Networks (WSN) due to the deficiency of computing resources and the lack of resource-sharing. Therefore, it is not recommended to develop innovative applications based on these constrained devices without further enhancement and improvement. Hence, this article explores a coeffective solution based on Cloud Computing and Virtualization Techniques to address these challenges. Cloud computing provides efficient computing resources and huge storage space, while the virtualization technique allows resources to be virtualized and shared between various applications. Integrating IoT-WSN with the Cloud-based Virtualization Environment will eliminate the drawbacks and limitations of conventional networks and facilitate the development of novel applications in a more flexible way. Furthermore, this article reviews the recent trends in IoT-WSN, virtualization techniques, and cloud computing. Also, we present the integration process of sensor networks with Cloud-based Virtualization and propose a new general architecture view for the Sensor-Cloud paradigm, and discuss its key elements, basic principles, lifecycle operation, and outline its advantages and disadvantages. Finally, we review the state-of-the-art, present the major challenges, and suggest future work directions.

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Section: State-of-the-artsmentioning

confidence: 99%

Cloud-based virtualization environment for IoT-based WSN: solutions, approaches and challenges

Almurisi

Tadisetty

2022

J Ambient Intell Human Comput

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“…The authors in [21] designed an agricultural sensor-cloud framework to provide multiple services for farmers. In order to ensure that the event data packets can be forwarded to the sink fast and reliably, the authors focused on the routing protocol of the physical layer and presented a priority-based data transmission technique.…”

Section: Related Workmentioning

confidence: 99%

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud

Zhang

Wang

Xiong

2020

Sensors

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The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users’ requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.

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“…SCs have various applications such as Nimbits [2], Pachube platform [3], Ubiquition Healthcare Monitoring [4], disaster detection [5], Google Health [6], Microsoft Health Vault [7], agriculture [8], traffic monitoring, and wildlife monitoring [9,10]. Privacy and costeffectiveness are primary concerns in data collection and sensing.…”

Section: Introductionmentioning

confidence: 99%

Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

et al. 2021

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Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.

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Agriculture Sensor-Cloud Infrastructure and Routing Protocol in the Physical Sensor Network Layer

Cited by 10 publications

References 14 publications

Cloud-based virtualization environment for IoT-based WSN: solutions, approaches and challenges

Cloud-based virtualization environment for IoT-based WSN: solutions, approaches and challenges

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud

Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

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