Securing Medical Sensor Network with HIP

Kuptsov, Dmitriy; Nechaev, Boris; Gurtov, Andrei

doi:10.1007/978-3-642-29734-2_21

Cited by 8 publications

(9 citation statements)

References 9 publications

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“…HIP has become an increasingly popular approach to offer secure communication among the Internet of Things (Kuptsov et al, 2012). In addition, we developed a multi-domain attribute-enriched role-based access control architecture (Zao et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

Zao

Gan

You

et al. 2014

Front. Hum. Neurosci.

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EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

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Section: Discussionmentioning

confidence: 99%

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

Zao

Gan

You

et al. 2014

Front. Hum. Neurosci.

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“…The connection of WSNs to the Internet enables the ex- 29 ploitation of sensing reports (that are often massive) in web 30 applications and services, which is possible with the elastic 31 storage and processing services brought by cloud computing, 32 and which leads to a ubiquitous access to WSN's data. The 33 integration of WSNs in the Internet is often realized across 34 the adoption of adapted extensions of the standard commu- net of things seems to be more difficult, due to the domi- 85 nant heterogeneity with its various forms especially, those 86 which are related to resources availability in the involved 87 entities. 88 In classical applications of WSNs, where the Internet were 89 used only as a transporting medium of sensing reports to the 90 task manager, adversaries were obliged to access the network 91 physically, so that they could successfully attack it.…”

Section: Article In Pressmentioning

confidence: 99%

“…The adoption of HIP to secure WSNs applications is re-507 cently gaining attention[34,35]. Enabling end-to-end data security in network layer by 509 means of IPsec protocol (whether it is coupled with HIP 510 or IKE) allows securing all types of transported traffic.…”

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confidence: 99%

Efficient HIP-based approach to ensure lightweight end-to-end security in the internet of things

Sahraoui

Bilami

2015

Computer Networks

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“…Second, the open environment, in which the medical sensor networks will typically operate, is uncontrollable and not trustworthy. For example, the sensor nodes are usually placed in hostile environments, which make them vulnerable to compromise attacks .…”

Section: System Architecturementioning

confidence: 99%

Lightweight and confidential data aggregation in healthcare wireless sensor networks

Soufiene

Bahattab

Trad

et al. 2015

Trans. Emerging Tel. Tech.

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Medical wireless sensor networks (MWSNs) provide efficient solutions to the ubiquitous healthcare systems. Deployment of MWSNs for healthcare monitoring minimizes the need for healthcare professionals and helps the patients and elderly people to safely maintain an independent life. In hospitals, medical data sensors on patients produce an enormous volume of increasingly diverse real-time data. However, it is still critical to efficiently aggregate the different types of MWSNs data to the central servers. The security of collected and transmitted data from medical sensors is critical, whether inside the network or when stored at central servers. Efficient and secure aggregation of data is thus very essential to ensure integrity of data delivery, as well as the privacy of these data. In this research, we propose a priority-based compressed data aggregation scheme with integrity preservation to improve the aggregation efficiency of different types of health data. We use compressed sensing as a sampling procedure to reduce the communication overhead and minimize power consumption. Then, the compressed data are encrypted, and integrity is protected by a cryptographic hash algorithm to preserve data integrity. Finally, according to different data priorities, we apply an aggregation function and then send the data for diagnosis. The security analysis focuses on security properties assured by our scheme. Then, we will present experimental results for the evaluation of the proposed system on e-health sensor platform.

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Securing Medical Sensor Network with HIP

Cited by 8 publications

References 9 publications

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

Efficient HIP-based approach to ensure lightweight end-to-end security in the internet of things

Lightweight and confidential data aggregation in healthcare wireless sensor networks

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