Introduction
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An efficient simulation model is used to demonstrate how mobile cloud computing might be used to handle various emergency situations.
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The cloud nodes in the proposed approach can extend their assistance in times of need at an affordable operating and maintenance cost.
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The mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately.
Literature survey
S/N | Authors | Contribution |
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[1] | Garetto M, Leonardi | Using differential equations, the computational procedure proposed analyses the random nodes, leading to the formation of numerous straightforward expressions |
[2] | Rathee G, Garg S, Kaddoum G, et al | A hypothetical framework was proposed for making judgments using both an ideal and an adversarial mode of operation in order to provide a multidimensional model |
[3] | Hatami-Marbini A, Varzgani N, Sajadi SM, Kamali A | This research uses simulation-based and mathematical modelling optimization approaches to detect the best and closest location of medical centers in case of emergencies |
[4] | Bruno R, Conti M, Gregori E | A system architecture was designed to use sensing techniques to make mobile computing tasks less complicated using a code to link source mobile networks with destination mobile networks |
[5] | Ramasamy V, Gomathy B, Sarkar JL, et al | An emergency management system was proposed which uses Bluetooth technology to follow peer-to-peer communication to reduce the workload of mobile devices |
[6] | Kim C, Dudin A, Dudin S, Dudina O | A multi-server queueing system model was introduced for long-term storage of new users in a communication network |
[7] | Lamb ZW, Agrawal DP | An architecture is proposed which analyzes available real-time resources and allocates to the most feasible and logical resource in order to reduce networking overhead |
[8] | Dou Y, Ho YH, Deng Y, Chan HCB | An inter-cloud system was designed for providing transfer functions during offloading calculations to address these issues |
[9] | Gandotra P, Member S, Jha RK D2D | A data survey was conducted in computing networks, and the permission of device-to-device communication to provide details on discovery and route processing strategies for various cloud computing nodes |
[10] | Mitropoulos S, Mitsis C, Valacheas P, Douligeris C | A novel medical information system for emergencies was presented which simulates the services the Greek National Instant Aid Centre provides |
[11] | Krishna Keerthi Chennam, Rajanikanth Aluvalu and S.Shitharth,‘ | This work designed an integrated framework of an attribute-based multistage encryption standard for providing security and data confidentiality |
[12] | Han S, Ma D, Kang C, et al | An offloading model that uses mobile edge computing was proposed to solve the issue of high mobile cloud computing technology |
[13] | Nanda S, Panigrahi CR, Pati B | A detailed survey on mobile cloud computing and emergency medical system applications was conducted, and possible solutions to the design and development challenges were proposed |
[14] | Poulymenopoulou M, Malamateniou F, Vassilacopoulos G ( | An integrated EMS framework based on cloud computing was proposed which provides authorized users with access to emergency case information for exchanging data with hospitals |
[15] | Rajanikanth Aluvalu, V.Uma Maheswari, Krishna Keerthi Chennam and S.Shitharth,‘ | A dynamic access control model is proposed for the security of cloud-stored data, and for providing users with access to the data |
[16] | B. Thirumaleshwari Devi, S.Shitharth | A study was conducted on the most common security attacks and breaches, especially honey pot, in cloud computing |
[17] | Shabbir, Maryam & Shabbir, Ayesha & Iwendi, Celestine & Javed, Abdul Rehman & Rizwan, Muhammad & Herencsar, Norbert & Lin, Chun-Wei | The authors proposed an algorithm for the security and privacy of health information making use of the Modular Encryption Standard (MES) |
[18] | Anajemba, Joseph & Yue, Tang & Iwendi, Celestine & Alenezi, Mamdouh & Mittal, Mohit | The authors presented an offloading technique for multi-access edge computation in a high demand Internet of Things network for energy efficiency and consumption reduction |
[19] | Sirajuddin, Mohammad & Rupa, Ch & Iwendi, Celestine & Biamba, Cresantus | The authors proposed a trust-based routing protocol to enhance the performance and quality of service of the Mobile Ad-hoc Network (MANET) |
[20] | Priya,, Swarna & Bhattacharya, Sweta & Reddy, Praveen & Somayaji, Siva & Lakshman, Kuruva & Kaluri, Rajesh & Hussien, Aseel & Gadekallu, Thippa | The authors introduced an energy efficient architecture based on the cloud and the Internet of Everything (IoE) to optimize the consumption of energy and reduce data traffic |
[21] | Wang, Tian & Quan, Yang & Shen, Xuewei & Gadekallu, Thippa & Wang, Weizheng & Dev, Kapal | The authors designed technology for data privacy in the IoT based on the cloud technology, which hides the data transmission information between the edge servers and the cloud |
[22] | T. Gadekallu, Quoc-Viet Pham, Dinh C. Nguyen, P. Maddikunta, N. Deepa, B. Prabadevi, P. Pathirana, Jun Zhao, W. Hwang | A comprehensive review of developments in and applications of blockchain, edge computing, and Internet of Things (IoT) was presented |
Mobile computing: system model
Optimization algorithm
Experimental verification
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Scenario 1: Energy of computing nodes
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Scenario 2: Signal to Noise Ratio
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Scenario 3: External weighting factors
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Scenario 4: Activation periods of mobile nodes
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Scenario 5: Gain of decision tree
Scenario 1: energy of computing nodes
Power | Time period | Energy [5] | Energy (Proposed) |
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5.69 | 1.6 | 0.9 | 0.3 |
7.23 | 2.3 | 0.8 | 0.1 |
9.02 | 2.9 | 0.6 | 0.05 |
11.56 | 3.4 | 0.4 | 0.04 |
13.28 | 3.8 | 0.2 | 0.02 |
Scenario 2: Signal to Noise Ratio
Threshold levels | Number of observation periods | SNR [5] | SNR (Proposed) |
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7.24 | 10 | 2 | 0.8 |
9.23 | 14 | 1.8 | 0.6 |
12.45 | 16 | 1.4 | 0.3 |
13.92 | 19 | 1.3 | 0.1 |
14.36 | 24 | 1 | 0 |
Scenario 3: external weighting factors
Weight of packets | Number of restrictions | Load [5] | Load (Proposed) |
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4.16 | 2 | 1.41 | 0.9 |
6.94 | 3 | 1.22 | 0.86 |
9.01 | 4 | 1.19 | 0.82 |
13.57 | 5 | 1.05 | 0.74 |
14.21 | 6 | 1.03 | 0.71 |
Scenario 4: activation periods of mobile nodes
Frequency | Number of sensing units | Activation period (Existing) | Activation period (Proposed) |
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8.12 | 4 | 0.2 | 1 |
12.25 | 8 | 0.3 | 1.4 |
16.79 | 12 | 0.6 | 1.9 |
21.34 | 16 | 0.9 | 2.3 |
25.5 | 20 | 1 | 2.6 |
Scenario 5: gain of decision tree
Number of mobile nodes | Entropy | Gain [5] | Gain (Proposed) |
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1000 | 230 | 72 | 83 |
5000 | 375 | 76 | 86 |
10000 | 425 | 81 | 93 |
20000 | 565 | 84 | 95 |
40000 | 635 | 87 | 98 |
Performance evaluation
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Case study 1: Conjunction characteristics
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Case study 2: Robustness characteristics
Case study 1: conjunction characteristics
Best epoch | Convergence [5] | Convergence (Proposed) |
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20 | 32 | 30 |
40 | 39 | 35 |
60 | 50 | 50 |
80 | 53 | 50 |
100 | 53 | 50 |
Case study 2: robustness characteristics
Best epoch | Robustness [5] | Robustness (Proposed) |
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20 | 75 | 97 |
40 | 72 | 92 |
60 | 77 | 96 |
80 | 73 | 93 |
100 | 78 | 95 |