F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

Sajedi, Seyedeh Nafiseh; Maadani, Mohsen; Moghadam, Meisam Nesari

doi:10.1007/s11227-021-03890-6

Cited by 34 publications

(13 citation statements)

References 25 publications

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“…To solve these problems, data aggregation techniques are used, which are addressed by Madureira et al, 19 Sajedi et al, 20 and Sawar et al 21 Madureira et al 19 introduced an Internet of Things Protocol (IoTP) that supports the implementation of data aggregation algorithms within network‐level hardware switches, which can work according to network status and based on different utilized communication technologies by IoT devices. A model called F‐LEACH that uses Leach data aggregation and fuzzy logic is covered by Sajedi et al 20 The proposal aims to optimize processes such as node selection and data aggregation, and network life, maximizing energy efficiency. Sawar et al 21 proposes a division and achievement strategy based on privacy level to reduce computational load on a fog node during the data aggregation process.…”

Section: Related Workmentioning

confidence: 99%

Data interplay: A model to optimize data usage in the Internet of Things

et al. 2023

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The Internet of Things (IoT) has been playing an important role in the technology scenario due to its high potential and impact on different society segments. Estimates suggest a trend for an increase in the number of IoT devices connected to the Internet for the next few years. Hence, the volume of data produced by IoT devices will follow this growth perspective, and there will be a demand for systems that can process, store, and promote access to large amounts of data. The data collected from sensors in typical IoT systems is stored and processed in cloud servers. However, some IoT solutions use edge devices to perform specific actions, such as processing, storage, and access, using only local infrastructure for low latency requirements. Fog computing has been used to improve IoT solutions and to transfer some of the complexity from the cloud to the edge of the network, that is, closer to devices, applications and users, working as a kind of “local and private cloud.” The cooperation of devices and applications between edge and cloud creates a need for an interplay to enable data flow among the layers of IoT systems deployed on edge and in the cloud. Thus, IoT data systems should support the data life cycle through its collection, analysis, and use. Performance efficiency is a quality factor of systems and software engineering, which measures “performance relative to the number of resources used under stated conditions.” In particular, in IoT systems that involve a large volume of data, the performance efficiency of data interplay is a relevant requirement. This work proposes a data interplay model of IoT to define and deploy the IoT data life cycle in the collection, analytics, and data use stages. This data interplay proposal aims to improve performance efficiency in IoT data life cycle operations: collection, analytics, and use among devices and applications in edge and cloud infrastructures.

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Section: Related Workmentioning

confidence: 99%

Data interplay: A model to optimize data usage in the Internet of Things

et al. 2023

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“…In FEELS, 85 they designed an efficient and low SAR algorithm, it is a cluster‐based protocol but it does not support emergency assistance. In F‐LEACH, 86 the authors proposed an algorithm to increase the network lifetime and it outperforms, but it does not consider security, and emergency assistance is not present. In EB‐FV‐MADM, 87 the authors designed an energy‐efficient, low‐energy‐cost protocol but did not consider cross‐channel interference and emergency assistance is absent.…”

Section: Classification Of Wban Routing Protocolsmentioning

confidence: 99%

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Narwal,

Malik,

Mohapatra

et al. 2023

Int J Communication

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SummaryIn the present scenario, wireless body area network (WBAN) use case has gone beyond medical monitoring (e.g., E‐Healthcare) and has become a vital part of our daily lives. WBANs, applied in E‐Healthcare systems, extract the physiological parameters through the implanted (in‐body) and/or wearable (on‐body) sensors applied to the human body and route them to the remote server in a systematized and well‐grounded manner. Thus, routing is the most primitive and non‐trivial task in WBANs for prompt and proper delivery of sensed data. The design of routing protocol is crucial for efficacious communication and high network performance. Therefore, the target of this paper is to present an all‐encompassing taxonomy of the state‐of‐the‐art routing protocols in WBANs. Firstly, this paper provides an insight into WBANs and their related projects. Next, WBAN routing challenges are explored, and after clustering these challenges based on their operating mechanism, the routing protocols are categorized into eight categories, which correspond to cluster‐ and tree‐based, cross‐layer‐based, fuzzy‐based, interference‐based, posture‐based, QoS‐based, SDN‐based, and temperature‐based routing protocols. Furthermore, comparison tables highlighting the goals, strengths, limitations, and novelty of each protocol, along with a qualitative comparison table of the routing protocols for each category based on their features are furnished. This exhaustive survey can serve as a starting point for WBAN researchers, as open research issues have been highlighted in the discussion section.

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“…Therefore, the node with the minimum sum of all weights is considered as the parent node. A Fuzzy-based data aggregation scheme called F-LEACH is proposed in [92] with the aim to maximize the network lifetime by optimizing the aggregating node selection. Besides, in F-LEACH, the FIS membership functions are optimized, and the average of several executions are selected as the optimal parameters by modifying the network scenario.…”

Section: Ai Based Solutions To Data Collection Aggregation and Dissem...mentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

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F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

Cited by 34 publications

References 25 publications

Data interplay: A model to optimize data usage in the Internet of Things

Data interplay: A model to optimize data usage in the Internet of Things

Dissecting wireless body area networks routing protocols: Classification, comparative analysis, and research challenges

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

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