scholarly journals Leveraging Fog Computing for a Secure and Smart Healthcare

2019 ◽  
Vol 8 (2) ◽  
pp. 6117-6122
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Healthcare Services ◽  
Decision Fusion ◽  
Sensor Data ◽  
Smart Sensors ◽  
Healthcare Applications ◽  
Smart Healthcare ◽  
Iot Devices

From hairbrushes to scales, all devices have sensors embedded in them to collect and communicate data. Smart Healthcare is proving to be an exciting and dynamic area with lots of room for new innovations and the increasing consumer demand for proactive health monitoring devices. Having India poised to spend a lot on healthcare, recent innovations using IoT devices and big data analytics can propel the healthcare industry into the future. Smart healthcare providers are leveraging cloud computing with fog computing to optimize their healthcare services. These smart healthcare applications depend mainly on the raw sensor data collected, aggregated, and analyzed by the smart sensors. Smart sensors these days generate myriad amount of data like text, image, audio, and video that require real-time or batch processing. Aggregating these diverse data from various types of resources remains a dispute till date. To resolve this issue, we have proposed a softwarized infrastructure that integrates cloud computing and fog computing, message brokers, and Tor for supple, safe, viable, and a concealed IoT exploitation for smart healthcare applications and services. Our proposed platform employs machine-to-machine (M2M) messaging, data fusion and decision fusion, and uses rule-based beacons for seamless data management. Our proposed flexBeacon system provides an IoT infrastructure that is nimble, secure, flexible, private, and reasonable. We have also proposed an M2M transceiver and microcontroller for flawless data incorporation of smart healthcare applications and services. Based on the IoT devices’ technical capabilities and resource availability, some systems are capable of making use of homomorphic encryption and zero knowledge proofs. The proposed flexBeacon platform offers seamless management and data aggregation without loss of accuracy. The cost of implementing a softwarized IoT for smart healthcare is also greatly reduced.

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

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6574
Author(s):  
Syed Rizwan Hassan ◽  
Ishtiaq Ahmad ◽  
Shafiq Ahmad ◽  
Abdullah Alfaify ◽  
Muhammad Shafiq
Keyword(s):  
Cloud Computing ◽  
Monitoring System ◽  
Fog Computing ◽  
Self Report ◽  
Healthcare Applications ◽  
Computing Architecture ◽  
Computing Paradigm ◽  
Smart Healthcare ◽  
Number Of Patients ◽  
Iot Devices

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.

scholarly journals Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Location Awareness ◽  
False Data Injection ◽  
Injection Attacks ◽  
Iot Devices

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

Internet of Things and Fog Computing Applications in Intelligent Transportation Systems

2020 ◽  
pp. 131-150 ◽  
Author(s):  
Korupalli V. Rajesh Kumar ◽  
K. Dinesh Kumar ◽  
Ravi Kumar Poluru ◽  
Syed Muzamil Basha ◽  
M Praveen Kumar Reddy
Keyword(s):  
Intelligent Transportation Systems ◽  
Fog Computing ◽  
Analytical Techniques ◽  
Transportation Systems ◽  
Sensor Data ◽  
Smart Sensors ◽  
Processing Unit ◽  
Stream Data ◽  
Autonomous Cars ◽  
Iot Devices

Self-driving vehicles such as autonomous cars are manufactured mostly with smart sensors and IoT devices with artificial intelligence (AI) techniques. In most of the cases, smart sensors are networked with IoT devices to transmit the data in real-time. IoT devices transmit the sensor data to the processing unit to do necessary actions based on sensor output data. The processing unit executes the tasks based on pre-defined instructions given to the processor with embedded and AI coding techniques. Continuous streaming of sensors raw data to the processing unit and for cloud storage are creating a huge load on cloud devices or on servers. In order to reduce the amount of stream data load on the cloud, fog computing, or fogging technology, helps a lot. Fogging is nothing but the pre-processing of the data before deploying it into the cloud. In fog environment, data optimization and analytical techniques take place as a part of data processing in a data hub on IoT devices or in a gateway.

scholarly journals Privacy-Preserving Internet of Things: Techniques and Applications

2019 ◽  
Vol 8 (6) ◽  
pp. 3229-3234 ◽  
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Multiparty Computation ◽  
Iot Applications ◽  
Attribute Based Encryption ◽  
Daily Behavior ◽  
Iot Devices

Privacy has become an imperative term in the recent technology developments. Lots of data are being collected through every digital activity of users. The expeditious development of IoT applications have raised the concern about the privacy of the IoT systems. The data collected via IoT sensors can reveal the daily behavior of the users, location, and other sensitive information. Hence, it is necessary to preserve the privacy of data collected by IoT devices. A large number of techniques and approaches have been implemented and used in different IoT based applications such as cloud computing based IoT, fog computing based IoT, blockchain based IoT and trajectory applications. In this paper, we present a detailed investigation of the existing approaches to preserve the privacy of data in IoT applications. The techniques like k-anonymity, secure multiparty computation, attribute based encryption and homomorphic encryption are analyzed. Finally, a comparative analysis of privacy preserving techniques with its applications are presented.

scholarly journals Fog Computing Application of Cyber-Physical Models of IoT Devices with Symbolic Approximation Algorithms

2021 ◽  
Author(s):  
Deok-Kee Choi
Keyword(s):  
Cloud Computing ◽  
Manufacturing Systems ◽  
Fog Computing ◽  
Streaming Data ◽  
Physical Models ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Symbolic Approximation ◽  
Iot Devices ◽  
Smart Manufacturing Systems

Abstract Smart manufacturing systems transmit out streaming data from IoT devices to cloud computing; however, this could bring about several disadvantages such as high latency, immobility, and high bandwidth usage, etc. As for streaming data generated in many IoT devices, to avoid a long path from the devices to cloud computing, Fog computing has drawn in manufacturing recently much attention. This may allow IoT devices to utilize the closer resource without heavily depending on cloud computing. In this research, we set up a three-blade fan as IoT device used in manufacturing system with an accelerometer installed and analyzed the sensor data through cyber-physical models based on machine learning and streaming data analytics at Fog computing. Most of the previous studies on the similar subject are of pre-processed data open to public on the Internet, not with real-world data. Thus, studies using real-world sensor data are rarely found. A symbolic approximation algorithm is a combination of the dictionary-based algorithm of symbolic approximation algorithms and term-frequency inverse document frequency algorithm to approximate the time-series signal of sensors. We closely followed the Bayesian approach to clarify the whole procedure in a logical order. In order to monitor a fan's state in real time, we employed five different cyber-physical models, among which the symbolic approximation algorithm resulted in about 98% accuracy at a 95% confidence level with correctly classifying the current state of the fan. Furthermore, we have run statistical rigor tests on both experimental data and the simulation results through executing the post-hoc analysis. By implementing micro-intelligence with a trained cyber-physical model unto an individual IoT device through Fog computing we may alienate significant amount of load on cloud computing; thus, with saving cost on managing cloud computing facility. We would expect that this framework to be utilized for various IoT devices of smart manufacturing systems.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

Cloud Based Wireless Infrastructure for Health Monitoring

2020 ◽  
pp. 34-55 ◽  
Author(s):  
Ajay Chaudhary ◽  
Sateesh Kumar Peddoju ◽  
Suresh Kumar Peddoju
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Healthcare Services ◽  
Power Source ◽  
Vital Role ◽  
Healthcare Applications ◽  
Related Data ◽  
Health Related ◽  
Healthcare Monitoring ◽  
Wireless Infrastructure

The wireless infrastructure based devices can collect data for long period of time even with a tiny power source as they perform specific function of collection of health related data and sending to gateways. The sensing data of healthcare monitoring consumes low power but they had limited computation power to process this data, where the cloud computing plays a vital role and compliment the loophole of wireless infrastructure based systems. In cloud computing with its immense computation power for easily deployment of healthcare monitoring algorithms and helps to process sensed data. As these two technologies did great jobs in their respective fields a conflate framework of these two technologies may lead to a great architecture for healthcare applications. This chapter reviews complete state-of-the-art and several use cases related to healthcare monitoring using different wireless infrastructure and adapting cloud based technologies in providing the healthcare services.

From Cloud Computing to Fog Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Sanjay P. Ahuja ◽  
Niharika Deval
Keyword(s):  
Cloud Computing ◽  
Fog Computing ◽  
Cost Savings ◽  
End Users ◽  
Internet Technology ◽  
Future Research ◽  
Computing Paradigm ◽  
Close Proximity ◽  
Iot Devices ◽  
Communication Control

This article describes how in recent years, Cloud Computing has emerged as a fundamental computing paradigm that has significantly changed the approach of enterprises as well as end users towards implementation of Internet technology. The key characteristics such as on-demand resource provision, scalability, rapid elasticity, higher flexibility, and significant cost savings have influenced enterprises of all sizes in the wide and successful adoption of Cloud Computing. Despite numerous advantages, Cloud Computing has its fair share of downsides as well. One of those major concerns is latency issues which has relevance to the Internet of Things (IoT). A new computing paradigm has been proposed by Cisco in early 2014 and termed 'Fog Computing'. Fog Computing otherwise known as Edge Computing is the integration of Cloud Computing and IoT. Being located in close proximity to the IoT devices, the Fog assists with latency requirements of IoT related applications. It also meets the data processing needs of IoT devices which are resource constrained by bringing computation, communication, control and storage closer to the end users. Clouds continue to offer support for data analytics. One can think of the IoT-Fog-Cloud as being part of a continuum. This article surveys the current literature on Fog Computing and provides a discussion on the background, details and architecture of Fog Computing, as well as the application areas of Fog Computing. The article concludes with some recommendations in the areas of future research.

scholarly journals A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jangwon Gim ◽  
Sukhoon Lee ◽  
Wonkyun Joo
Keyword(s):  
Healthcare Services ◽  
Current Situation ◽  
Sensor Data ◽  
Technological Advancement ◽  
Analysis Framework ◽  
Data Interoperability ◽  
Care Services ◽  
Smart Healthcare ◽  
Future Events ◽  
Prescriptive Analysis

A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.

scholarly journals A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

2021 ◽  
Vol 18 (22) ◽  
pp. 413
Author(s):  
Ismail Zaharaddeen Yakubu ◽  
Lele Muhammed ◽  
Zainab Aliyu Musa ◽  
Zakari Idris Matinja ◽  
Ilya Musa Adamu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Fog Computing ◽  
Dynamic Resource Allocation ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Agent Based ◽  
Edge Network ◽  
Dynamic Resource ◽  
Iot Devices

Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery. HIGHLIGHTS The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one GRAPHICAL ABSTRACT

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