Security in Low Powered Wide Area Networks: Opportunities for Software Defined Network-Supported Solutions

Pathak, Gaurav; Gutiérrez, Jairo; Rehman, Saeed Ur

doi:10.3390/electronics9081195

Cited by 23 publications

(13 citation statements)

References 105 publications

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“…Various studies use flexible time division multiple access (TDMA) as the most suitable that decreases interference and decreases power consuming. In [ 25 ], the author details more interference techniques in WBAN. Table 3 includes a summarization of some selected studies according to the used sensors, collected data, and the transmission protocols.…”

Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

“…Table 3 showed a description of the collected articles according to various factors include (Diseases, collected data, the sensor used and the transmission protocol). The authors of [25], provide a comprehensive survey about security and privacy among WS devices. Interoperability and integration: Health data are usually big and heterogeneous; therefore, interoperability is a critical issue.…”

Section: Wban Challenges In Rmsmentioning

confidence: 99%

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Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

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Section: Main Components Of the Rpm Systemmentioning

confidence: 99%

Section: Wban Challenges In Rmsmentioning

confidence: 99%

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

et al. 2021

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“…Intrainterference is the most common in a single WBAN. Numerous studies have shown that flexibility time division multiple access (TDMA) is the best option for reducing interference and power consumption [19].…”

Section: Notification Componentsmentioning

confidence: 99%

A System of Remote Patients’ Monitoring and Alerting Using the Machine Learning Technique

Dhinakaran¹,

Phasinam

Alanya-Beltrán

et al. 2022

Journal of Food Quality

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Machine learning has become an essential tool in daily life, or we can say it is a powerful tool in the majority of areas that we wish to optimize. Machine learning is being used to create techniques that can learn from labelled or unlabeled information, as well as learn from their surroundings. Machine learning is utilized in various areas, but mainly in the healthcare industry, where it provides significant advantages via appropriate decision and prediction methods. The proposed work introduces a remote system that can continuously monitor the patient and can produce an alert whenever necessary. The proposed methodology makes use of different machine learning algorithms along with cloud computing for continuous data storage. Over the years, these technologies have resulted in significant advancements in the healthcare industry. Medical professionals utilize machine learning tools and methods to analyse medical data in order to detect hazards and offer appropriate diagnosis and treatment. The scope of remote healthcare includes anything from tracking chronically sick patients, elderly people, preterm children, and accident victims. The current study explores the machine learning technologies’ capability of monitoring remote patients and alerts their current condition through the remote system. New advances in contactless observation demonstrate that it is only necessary for the patient to be present within a few meters of the sensors for them to work. Sensors connected to the body and environmental sensors connected to the surroundings are examples of the technology available.

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“…Overall, various survey-based studies highlighting the vulnerabilities in LPWAN communication technologies have identified an urgent need for secure and uninterrupted communication between an end-device and the gateway for secure and effective IoT networks for large-scale IoT deployments. While there is greater potential in the emergence of software-defined network (SDN) architecture for security in IoT, the protocols in SDN are still under development [37,38].…”

Section: Related Work and Research Contributionmentioning

confidence: 99%

Towards a Secure and Scalable IoT Infrastructure: A Pilot Deployment for a Smart Water Monitoring System

Overmars¹,

Venkatraman²

2020

Technologies

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Recent growth in the Internet of Things (IoT) looks promising for realizing a smart environment of the future. However, concerns about the security of IoT devices are escalating as they are inherently constrained by limited resources, heterogeneity, and lack of standard security controls or protocols. Due to their inability to support state-of-the-art secure network protocols and defense mechanisms, standard security solutions are unsuitable for dynamic IoT environments that require large and smart IoT infrastructure deployments. At present, the IoT based smart environment deployments predominantly use cloud-centric approaches to enable continuous and on-demand data exchange that leads to further security and privacy risks. While standard security protocols, such as Virtual Private Networks (VPNs), have been explored for certain IoT environments recently, the implementation models reported have several variations and are not practically scalable for any dynamically scalable IoT deployment. This paper addresses current drawbacks in providing the required flexibility, interoperability, scalability, and low-cost practical viability of a secure IoT infrastructure. We propose an adaptive end-to-end security model that supports the defense requirements for a scalable IoT infrastructure. With low-cost embedded controllers, such as the Raspberry Pi, allowing for the convergence of more sophisticated networking protocols to be embedded at the IoT monitoring interface, we propose a scalable IoT security model integrating both the IoT devices and the controller as one embedded device. Our approach is unique, with a focus on the integration of a security protocol at the embedded interface. In addition, we demonstrate a prototype implementation of our IoT security model for a smart water monitoring system. We believe that our modest first step would instill future research interests in this direction.

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Security in Low Powered Wide Area Networks: Opportunities for Software Defined Network-Supported Solutions

Cited by 23 publications

References 105 publications

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

A System of Remote Patients’ Monitoring and Alerting Using the Machine Learning Technique

Towards a Secure and Scalable IoT Infrastructure: A Pilot Deployment for a Smart Water Monitoring System

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