Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN

Ullah, Ata; Said, Ghawar; Sher, Muhammad

doi:10.1007/s12083-019-00745-z

Cited by 69 publications

(35 citation statements)

References 20 publications

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“…Aggregator nodes compress data size to reduce energy consumption and communication cost while transmitting data to fog node. At the edge node, the message extraction algorithm is utilized to extract device-level data [68].…”

Section: Secure Data Aggregation Using Fog Computing In Healthcarementioning

confidence: 99%

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

et al. 2021

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Internet of medical things (IoMT) is getting researchers' attention due to its wide applicability in healthcare. Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes. Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers. We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature. This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered. A taxonomy is presented to categorize the schemes. Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio. We present a summary of these scheme along with analytical discussion. Finally, a number of open research challenges are identified. Moreover, the schemes are explored to identify the challenges that are addressed in each scheme.

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Section: Secure Data Aggregation Using Fog Computing In Healthcarementioning

confidence: 99%

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

et al. 2021

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“…It can, nevertheless, share the encrypted data with an adjacent aggregator in order to send data to the fog server by adding its current collected data to the encrypted data. The relevant data values may be extracted by the fog server and saved in a local repository, which may then be updated in cloud repositories [29]. Storage, communication, transmission ratio, energy consumption, and resilience are all improved by the fog system [29].…”

Section: Related Workmentioning

confidence: 99%

“…The relevant data values may be extracted by the fog server and saved in a local repository, which may then be updated in cloud repositories [29]. Storage, communication, transmission ratio, energy consumption, and resilience are all improved by the fog system [29]. As a result, job allocation and secure deduplication are two of the system's tasks.…”

Section: Related Workmentioning

confidence: 99%

DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

Varmaghani

Nazar

Ahmadi

et al. 2021

Wireless Communications and Mobile Computing

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Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog-based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution-Map-Transfer-Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision-Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.

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“…Given the difficulty to collect medical data safely and efficiently owing to the resource limitations of IoT devices, various studies on providing medical services by combining IoT and edge clouds have been conducted [128,129]. In addition, to collect data, aggregate them safely and efficiently, and transmit them to a server, a study has been conducted on a system leveraging fog computing [130,131]. ere is also a growing interest in introducing unmanned aerial vehicles (UAVs) as smart objects for collecting health data.…”

Section: Keyword Clustering and Evolution Of Research On Iotmentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

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Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN

Cited by 69 publications

References 20 publications

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

Current Research Trends in IoT Security: A Systematic Mapping Study

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