An IoT Platform for Civil Infrastructure Monitoring

Jeong, Seongwoon; Law, Kincho H.

doi:10.1109/compsac.2018.00111

Cited by 21 publications

(11 citation statements)

References 14 publications

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“…ZigBee is another short-range wireless technology in the IEEE 802.15.4 family [24][25]. This IoT technology has been widely applied for home automation, industrial monitoring, and healthcare applications.Zigbee is low power consumption, operates in the unlicensed bands, primarily 2.4 GHz and 868 MHz or 915 MHz, and its default operating mode at 2.4 GHz uses 16 channels of 2 MHz bandwidth, its range is a few meters up to 100 meters.Table 2 shows the comparison of the standards.…”

Section: Zigbeementioning

confidence: 99%

A Study for Remote Monitoring of Water Points in Mauritania Based on IoT (LoRa) Technology

Abeidi

Kerim

Nanne

2022

IJEEI

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Wetlands in Mauritania contain the most important water sources necessary for the survival of rural communities in the country. In these areas, the main rural activities such as animal husbandry, agriculture, and fishing take place. Lack of water or flooding must be monitored to plan solutions in advance. After a comparative study of IoT wireless technologies, we proposed that LoRa technology is the most suitable for our field of application. However, in certain areas where access to the cellular network is difficult, we propose the addition of satellite communication in the LoRamonitoring system to achieve information collected at any point in the world via the cloud and the Internet. We carried out a practical case for the areas covered by the UMTS (3G) cellular network using devices integrating LoRaWAN to evaluate the performance of this technology. The results show the success of the communication over a distance of 14 km.

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Section: Zigbeementioning

confidence: 99%

A Study for Remote Monitoring of Water Points in Mauritania Based on IoT (LoRa) Technology

Abeidi

Kerim

Nanne

2022

IJEEI

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“…The researchers in [20] designed an IoT framework named IoT as a service (iTaaS) in which mobile handheld gadgets at the front end work as a gateway to collect and transmit to the cloud for data processing and storage at the backend. The authors in [21] proposed a scalable approach of hybrid cloud computing for data management with the three-layered model. The layers are the communication layer, storage layer, and mapping layer.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Cost is not estimated in [4], [19], [20], [21], [22], [23], [24], and [25] The cost estimator at z-axis estimates the cost.…”

Section: Cost Estimationmentioning

confidence: 99%

The 3-Axis Scalable Service-Cloud Resource Modeling for Burst Prediction Under Smart Campus Scenario

et al. 2021

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Internet of Things (IoT) enables smart campuses more convenient for cloud services. The availability of cloud resources to its users appears as a fundamental challenge. The existing research presents several auto-scaling techniques to scale the resources with the increase in users' demands. However, still, the cloud users of auto-scaled servers experience service disruption, delayed responses, and the occurrence of service bursts. The prevailing burst management framework exhibits limitations in the context of burdening the existing auto-scaled machines for cost estimation and resource allocation. This research presents a 3axis auto-scaling framework for load balancing and resource allocation by incorporating a dedicated cost estimator and allocator (on the z-axis). The cost estimation server develops a log of existing load estimates of vertical and horizontal servers and scales the new users' requests in case the vertical threshold is breached with new requests. The cost estimator, in its data structure, keeps track of the current resources available at both vertical and horizontal servers. The historical information of available resources and the new resources' requests is decided by the cost estimator as per demand and supply scenario. The general characteristics of servers are resources pooling, requests queue development, burst identification, automatic scaling, and load balancing. The cost estimator also prioritizes vertical servers for resource allocations, and switches to the horizontal server when the vertical server reaches its 75% quota of resources. The study simulates 1000 users' requests of smart campus, adopts state-of-the-art ensemble with bagging strategy and handles an effective class imbalance situation.

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“…The container technology was used in a distributed manner like clusters. Jeong et al in (9) proposed a decentralized approach for scalability in which environment of hybrid (private for sensitive data and public for huge data) cloud computing was used for data management to share data among different parties.…”

Section: Figmentioning

confidence: 99%

Smart campus system using internet of things: simulation and assessment of vertical scalability

Razzaq¹

2020

IJST

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Background/Objectives: Scalability in the Internet of Things is meant for future extension in Internet connected devices. The scalability can be classified into two broad sense in terms of future flexibility i.e., vertical and horizontal scalability and in terms of infrastructure of the Internet of things i.e., device, communication, gateway, and server levels. Novelty: In this article, a novel taxonomy of scalability is proposed and presented according to infrastructure and it is categorized into four levels i.e., device, gateway, communication, and server levels. Moreover, a performance analysis of vertical scalability is conducted at the server level for a smart campus transportation system on a custom-based emulator developed in Visual Studio. Findings: From the results, it is observed that by increasing the load the response time of the server increases. And by scaling up, response time decreases. By scaling up at a certain limit and increasing the load, response time again increases. It is strongly believed that this research will help in assessing the capacity of present and future smart systems.

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An IoT Platform for Civil Infrastructure Monitoring

Cited by 21 publications

References 14 publications

A Study for Remote Monitoring of Water Points in Mauritania Based on IoT (LoRa) Technology

A Study for Remote Monitoring of Water Points in Mauritania Based on IoT (LoRa) Technology

The 3-Axis Scalable Service-Cloud Resource Modeling for Burst Prediction Under Smart Campus Scenario

Smart campus system using internet of things: simulation and assessment of vertical scalability

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