Cross layered cryptography based secure routing for IoT-enabled smart healthcare system

Kore, Ashwini; Patil, Sushil D.

doi:10.1007/s11276-021-02850-5

Cited by 23 publications

(9 citation statements)

References 41 publications

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“…In this segment, we will speak about the most recently used cross-layer authentication and data preservation countermeasure schemes of SWSN. To tackle cross-layer security threats in SWSN, WSN, and IoT applications, Kore, et al [99] proposed a novel prototype known as Cross-Layer and Cryptography-based Secure Routing (CLCSR) scheme. This model is consist of two phases such as a cross-layer mechanism to secure sensor clusters and a lightweight cryptography scheme to ensure the privacy of the user's data in the network.…”

Section: Cross Layer Counteraction Schemes Of Swsnmentioning

confidence: 99%

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Adil

Menon²,

Balasubramanian

et al. 2023

IEEE Sensors J.

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Section: Cross Layer Counteraction Schemes Of Swsnmentioning

confidence: 99%

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Adil

Menon²,

Balasubramanian

et al. 2023

IEEE Sensors J.

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“…Researchers have proposed different protocols under this category and some recent protocols are briefly described as follows: The OBSFR 94 routing protocol was designed with the aim of reducing cop count and delay in the partitioned networks and utilizes multi‐point‐to‐point routing. Kore and Patil 78 presented the on‐body PRPLC protocol developed in 2009 to address frequent postural partitioning problems. A prototype was built to get the effects of on‐body topology disconnections contemplating postural mobility, ultra‐short wireless range, and RF attenuation.…”

Section: Classification Of Wban Routing Protocolsmentioning

confidence: 99%

“…In CLCSR, 78 the authors aim to build a protocol that preserves privacy and transmits data very securely, but they have not checked CLCSR with different attackers and need to check the performance.…”

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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“…The association of data concurrently secured by many sensors may be employed by DSC approaches, which are driven by the Slepian–Wolf assumption [ 25 ]. The DSC systems state that each sensor center point transmits its compressed results to the sink for collective interpretation.…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

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Cross layered cryptography based secure routing for IoT-enabled smart healthcare system

Cited by 23 publications

References 41 publications

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

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

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

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