29,720 research outputs found
How will the Internet of Things enable Augmented Personalized Health?
Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed
IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN
Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of
deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low
computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are
often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security
flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks
(LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was
designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting
the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will
aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of
IoT networks in general
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