MAAS: A mobile cloud assisted architecture for handling emergency situations

Sarkar, Joy Lal; Panigrahi, Chhabi Rani; Pati, Bibudhendu; Saha, Apu Kumar; Majumder, Abhishek

doi:10.1002/dac.3950

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…They can also be used to monitor infrastructure, such as bridges, roads, and buildings, and to track the movement of people and goods. The development of IoT technology has been driven by the increasing availability of low-cost sensors, as well as advances in wireless communication [16][17][18] and cloud computing [19][20][21][22]. These developments have made it possible to collect and process large amounts of data in real-time, enabling new possibilities for monitoring and analysis.…”

Section: Iot Technology and Generic Iot Architecturementioning

confidence: 99%

“…Sarkar et al [20] Emergency Situations Proposed a mobile cloud-assisted architecture that supports multicloud and hybrid-cloud environments for handling emergency situations such as natural calamities. The algorithm offloads data to the most suitable cloud and yields better performance compared to baseline algorithms.…”

Section: Reference Context Of Use Description Of Usementioning

confidence: 99%

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From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

Damaševičius

Bačanin

Misra

2023

JSAN

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The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

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Section: Iot Technology and Generic Iot Architecturementioning

confidence: 99%

Section: Reference Context Of Use Description Of Usementioning

confidence: 99%

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

Damaševičius

Bačanin

Misra

2023

JSAN

View full text Add to dashboard Cite

show abstract

“…The best DC selection is to oblige solicitations of a specific client. Furthermore, adjusting the heaps among the DCs ignoring the expense is also a monotonous issue [5].…”

Section: Introductionmentioning

confidence: 99%

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Parida

Pati

Chandan³

et al. 2022

COMSIS J

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In Cloud computing the user requests are passaged to data centers (DCs) to accommodate resources. It is essential to select the suitable DCs as per the user requests so that other requests should not be penalized in terms of time and cost. The searching strategies consider the execution time rather than the related penalties while searching DCs. In this work, we discuss Penalty Elimination-based DC Allocation (PE-DCA) using Guided Local Search (GLS) mechanism to locate suitable DCs with reduced cost, response time, and processing time. The PE-DCA addresses, computes, and eliminates the penalties involved in the cost and time through iterative technique using the defined objective and guide functions. The PE-DCA is implemented using CloudAnalyst with various configurations of user requests and DCs. We examine the PE-DCA and the execution after-effects of various costs and time parameters to eliminate the penalties and observe that the proposed mechanism performs best.

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Data analytics in wireless systems and IoT issues and challenges

Krishna

Yenduri

Ariwa

2020

Int J Communication

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SummaryThe emergence of data engineering along with the support of Online Social Networks is growing by millions every day due to the introduction of wireless systems and Internet of Things. The rapid growth of usage of smart devices helps to create new generation knowledge sharing platforms. Data Analytics has a major role to play in the growth and success of wireless and IoT applications. The growth of data has become exponential and is difficult to analyze. Many researchers depend on the data available on Wireless Systems and IoT for developing new generation services and applications. With the opportunity of information and communication technologies like heterogeneous networking, cloud computing, web services, crowd sensing and data mining, ubiquitous and asynchronous information sharing is feasible. But this also brings out a lot of provocations with respect to conflicting standards, data portability, data aggregation, data distribution, differential context and communication overhead. The smart information and communication technology has changed many features of human lifestyle: personal and work places.

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MAAS: A mobile cloud assisted architecture for handling emergency situations

Cited by 8 publications

References 23 publications

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

PE-DCA: Penalty elimination based data center allocation technique using guided local search for IaaS cloud

Data analytics in wireless systems and IoT issues and challenges

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