Fog Computing based Automated Accident Detection and Emergency Response System using Android Smartphone

Dar, Bilal Khalid; Shah, Munam Ali; Shahid, Huniya; Naseem, Adnan

doi:10.1109/icet.2018.8603557

Cited by 19 publications

(17 citation statements)

References 23 publications

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“…iFogSim has been used to simulate the proposed algorithms. This simulator has been used by several researchers to model real‐life applications . Simulators, such as iFogSim are quite useful for modeling real‐life applications.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Load balancing aware scheduling algorithms for fog networks

Singh

Auluck

2019

Softw Pract Exp

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Fog networks have attracted the attention of researchers recently. The idea is that a part of the computation of a job/application can be performed by fog devices that are located at the network edge, close to the users. Executing latency sensitive applications on the cloud may not be feasible, owing to the significant communication delay involved between the user and the cloud data center (cdc). By the time the application traverses the network and reaches the cloud data center, it might already be too late. However, fog devices, also known as mobile data centers (mdcs), are capable of executing such latency sensitive applications. In this paper, we study the problem of balancing the application load while taking account of security constraints of jobs, across various mdcs in a fog network. In case a particular mdc does not have sufficient capacity to execute a job, the job needs to be migrated to some other mdc. To this end, we propose three heuristic algorithms: minimum distance, minimum load, and minimum hop distance and load (MHDL). In addition, we also propose an ILP-based algorithm called load balancing aware scheduling ILP (LASILP) for solving the task mapping and scheduling problem. The performance of the proposed algorithms have been compared with the cloud only algorithm and another heuristic algorithm called fog-cloud-placement (FCP). Simulation results performed on real-life workload traces reveal that the MHDL heuristic performs better as compared to other scheduling policies in the fog computing environment while meeting application privacy requirements.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Load balancing aware scheduling algorithms for fog networks

Singh

Auluck

2019

Softw Pract Exp

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“…Dar et al (2018a) has presented an automated accident detection and response system which makes use of fog computing to decrease response time. System evaluation by iFogSim toolkit is introduced in (Dar et al, 2019;2018b). Sarkar and Kumar (2019) have developed and simulated a fog based intelligent security system using the PTZ controller.…”

Section: A Fog Computingmentioning

confidence: 99%

IoT-Fog based Smart-Building Security System Design and Performance Evaluation

Medhat

Elshafey

Ali

2020

Journal of Computer Science

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This paper aims to develop a smart building-security system utilizing fog technology. Fog computing is an innovative solution to reduce latency and network congestion, in which the cloud is extended to the edge of the network. The proposed system can detect an intruder, fire and water leakage. It is implemented using Raspberry Pi, Arduino, Pi-camera and different sensors. The sensors are installed on Arduino and Pi-camera is mounted on Raspberry Pi. The Raspberry Pi is used as a fog node to fit the time-sensitive nature of the system. The performance was evaluated through the iFogSim toolkit. Fog reduces 33.26 and 46.8% average latency and sensor delay as compared to the cloud. Also, the value of the network usage decreases by 71% in the case of fog-based even with increased network load. Finally, the iFogSim was evaluated by measuring the simulation time, which gave acceptable results of about 8.5 sec.

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“…Designing applications in the fog computing paradigm reduces frequent data transmissions between cloud and edge devices, assuring low latency and minimum network utilization. Research performed in [ 28 , 29 , 30 ] concludes that reduced latency is offered by fog computing models as compared to the cloud. Fog nodes are distributed throughout the network, providing scalability and mobility [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

Hassan

Ahmad

et al. 2020

Sensors

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The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

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Fog Computing based Automated Accident Detection and Emergency Response System using Android Smartphone

Cited by 19 publications

References 23 publications

Load balancing aware scheduling algorithms for fog networks

Load balancing aware scheduling algorithms for fog networks

IoT-Fog based Smart-Building Security System Design and Performance Evaluation

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

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