Integrating cloud-WSN to analyze weather data and notify SaaS user alerts during weather disasters

Arjun, D; Bala, A. Umesh; Dwarakanath,; Sampada, K S; Rao, B. Basaveswara; Pasupuleti, Haribabu

doi:10.1109/iadcc.2015.7154835

Cited by 18 publications

(9 citation statements)

References 3 publications

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“…Arjun et al [ 11 ] set out a mixed architecture that combines WSN with multiple types of sensors and Cloud computing for the forecasting of natural disasters. In their study Arjun et al establish an SaaS (Software as a Service) framework that is integrated with Cloud -WSN; this is able to provide virtualization both at the level of the nodes and at the level of the network.…”

Section: Related Workmentioning

confidence: 99%

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

Furquim

Filho

Jalali

et al. 2018

Sensors

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The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of São Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN.

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Section: Related Workmentioning

confidence: 99%

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

Furquim

Filho

Jalali

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Here, in real time manner, data is processed and managed. One proposed framework [2] conquers the above restriction by organization of WSN base for different climate advance utilizing virtual sensor and overlay idea. Checking climate information and giving SaaS and interpersonal organization cataclysm cautions in light of choice ID3 system and give cloud validation utilizing secure shell.…”

Section: 1wireless Sensor Network Based Systemmentioning

confidence: 99%

Study of Recent Literature on Weather Monitoring Systems

Patil¹,

Pachpande²,

Chaudhari³

et al. 2016

IJCA

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Climate observing has critical influence on mankind. Gathering of the various data of fleeting elements of the climate variations is extremely noteworthy. The essential point of this paper is to build up an installed framework to outline a climate observing framework which empowers the checking of climate parameters. This type of frame work includes various sensors involving temperature, Humidity, wind speed, wind bearing information can be signed into cloud so that any one (validated individual) from wherever can watch the particular information. If there should be an occurrence of any catastrophes like flame, substantial downpour, overwhelming wind, temperature or moistness might be wild, in these cases the moment data can be passed on all through the world utilizing cloud to the verified people, regardless of the fact that his equipment is wrecked in crisis.

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“…Wireless networks of such circuitry, namely Wireless Sensor Networks (WSNs), can be deployed in a wide variety of applications such as healthcare [1], smart industries [2,3], environmental sensing [4], smart farming [5], and military defense [6]. In addition, WSNs play a significant role in emerging technologies, e.g., big data [7], cloud [8], and the Internet of Things (IoT) [9]. However, they have unique design challenges due to their severe computation and energy constraints [10,11].…”

Section: Introductionmentioning

confidence: 99%

Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)

Islam

Dey

Sampalli

2018

Sensors

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Wireless Sensor Networks (WSNs) have become a significant part of surveillance techniques. With unequal clustering approaches and multi-hop communication, WSNs can balance energy among the clusters and serve a wide monitoring area. Recent research has shown significant improvements in unequal clustering approaches by forming clusters prior to the selection of cluster heads. These improvements adopt different geometric fractals, such as the Sierpinski triangle, to divide the monitoring area into multiple clusters. However, performance of such approaches can be improved further by cognitive partitioning of the monitoring area instead of adopting random fractals. This paper proposes a novel clustering approach that partitions the monitoring area in a cognitive way for balancing the energy consumption. In addition, the proposed approach adopts a two-layered scrutinization process for the selection of cluster heads that ensures minimum energy consumption from the network. Furthermore, it reduces the blind spot problem that escalates once the nodes start dying. The proposed approach has been tested in terms of number of alive nodes per round, energy consumption of nodes and clusters, and distribution of alive nodes in the network. Results show a significant improvement in balancing the energy consumption among clusters and a reduction in the blind spot problem.

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Integrating cloud-WSN to analyze weather data and notify SaaS user alerts during weather disasters

Cited by 18 publications

References 3 publications

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

Study of Recent Literature on Weather Monitoring Systems

Energy-Balancing Unequal Clustering Approach to Reduce the Blind Spot Problem in Wireless Sensor Networks (WSNs)

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