Communication protocols evaluation for a wireless rainfall monitoring network in an urban area

Ortega-Gonzalez, Lilia; Acosta-Coll, Melisa; Piñeres-Espitia, Gabriel; Butt, Shariq Aziz

doi:10.1016/j.heliyon.2021.e07353

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…The second is its built-in ability to fit into a star-based topology. The third is that, as argued by both Tadrist et al [ 62 ] and Ortega-Gonzalez et al [ 63 ], a LoRaWAN has demonstrated its robustness in adverse weather conditions, which usually go hand-in-hand with river flooding.…”

Section: Methodsmentioning

confidence: 92%

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Boulouard

Ouaissa

et al. 2022

Sensors

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River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

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Section: Methodsmentioning

confidence: 92%

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Boulouard

Ouaissa

et al. 2022

Sensors

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“…An important reason for its use is to mitigate the impact on the population of natural hazards in vulnerable areas. EWS is an important tool for hazard risk management, and many applications require this solution to cope with phenomena such as tornadoes [17,18], floods [19,20], landslides [21,22], flash floods [23,24], tsunamis [25], and the like. Therefore, the use of emerging technologies in EWS is relevant and is a challenge for researchers.…”

Section: Emerging Technologies For Early Warning Systemmentioning

confidence: 99%

Performance analysis of 6LoWPAN protocol for a flood monitoring system

Piñeres-Espitia

Butt

Francisco

et al. 2022

J Wireless Com Network

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The internet of things is a disruptive technology that has been applied as a solution to problems in many fields of monitoring environmental variables. It is supported by technologies such as wireless sensor networks, which offer many protocols and hardware platforms in the market today. Protocols such as 6LoWPAN are novel, so this work focuses on determining whether its implementation on TelosB mote is feasible; these would be placed on an experimental deployment for a particular scenario of flash floods in a sector known as “La Brigada”, in the city of Barranquilla. This proposal has not been evaluated in Colombia for this type of application, and no similar work has been done for this type of scenario. For the evaluation of 6LoWPAN, a deployment with two end nodes and a sink node has been designed, due to the monitoring section under study; 5-min tests are proposed where through round trip time traffic PINGv6 packets are generated back and forth (Echo) between a sink node and two end nodes. The results are based on the evaluation of metrics such as delay and ping packet request/response rate. The performance of these metrics is subject to test scenarios that vary according to distance, packet size, and channel scan time. Two routing options, static or dynamic, are also proposed for this application case. The tests performed yielded results in terms of better performance in the test scenarios for packets with an average size of 120 B and channel monitoring times of 1024 ms. Likewise, the use of the TelosB platform was validated as a viable and innovative option for a monitoring scenario to flash floods in short stretches of the city of Barranquilla—Colombia. This study is important because it can provide information on the use of the TelosB platform as a valid solution for similar application scenarios; furthermore, the tests performed can be replicated in similar studies to evaluate congestion, power consumption, routing, topologies, and other metrics. This study is providing a road map for the research community to follow the simulation scenario to apply the test to their own studies. This work also provides the guidelines for similar researchers to monitor the flood in their own regions and then compare their results with this study.

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“…The technique was demonstrated using a simulation based on real data obtained from a hospital geriatric department in London, UK. The paper also discusses the possibility of applying the methodology to various medical departments (surgery, stroke, and mental illness) [38]. The study also proposed mathematical modeling to manage the bed occupancy in all departments of the hospital.…”

Section: Bed Occupancymentioning

confidence: 99%

A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector

Gochhait

Butt

De-la-Hoz-Franco

et al. 2021

Aut. Control Comp. Sci.

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The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and Tele-Medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services system; (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a Machine Learning solution for patient admission scheduling autonomously. The propose framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.

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Communication protocols evaluation for a wireless rainfall monitoring network in an urban area

Cited by 10 publications

References 20 publications

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Performance analysis of 6LoWPAN protocol for a flood monitoring system

A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector

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