Secure Health Monitoring Research Articles

An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning.

Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck of Φ-OTDR in field application. An event recognition method based on deep learning is proposed in this paper. This method directly uses the temporal-spatial data matrix from Φ-OTDR as the input of a convolutional neural network (CNN). Only a simple bandpass filtering and a gray scale transformation are needed as the pre-processing, which achieves real-time. Besides, an optimized network structure with small size, high training speed and high classification accuracy is built. Experiment results based on 5644 events samples show that this network can achieve 96.67% classification accuracy in recognition of 5 kinds of events and the retraining time is only 7 min for a new sensing setup.

Secure Health Monitoring of Soldiers with Tracking System using IoT: A Survey

Secure Health Monitoring of Soldiers with Tracking System using IoT: A Survey

Security Health monitoring and Attestation of Virtual Machines in Cloud computing

Security Health monitoring and Attestation of Virtual Machines in Cloud computing

Optimal Resource Allocation for Green and Clustered Video Sensor Networks

Wireless video sensor networks (WVSNs) are opening the door for many applications, such as industrial surveillance, environmental tracking, border security, and infrastructure health monitoring. In WVSN, energy conservation is very essential because: 1) sensors are usually battery-operated and 2) each sensor node needs to compress the video prior to transmission, which consumes more power than conventional wireless sensor networks. In this paper, we study the problem of minimizing the total power consumption in a cluster-based WVSN, leveraging cross-layer design to optimize the encoding power, the transmission power, and the source rate at each sensor node. To realize this problem, we devise a resource optimization framework, which takes into account the video signal distortion due to compression, in addition to packet loss in the wireless channel while trying to allocate network resources among multiple video sensors. Leveraging duality theory, we design a proximal minimization algorithm that is capable of achieving the optimal allocation of network resources, source rates, and encoding and communication powers, while providing application-level quality of service represented by video distortion. The algorithm is extended for sensor nodes with hybrid energy sources, leveraging energy harvesting to minimize the aggregate power, while addressing the tradeoff between renewable versus grid energy sources.

The mechanism and suppression methods of optical background noise in phase-sensitive optical time domain reflectometry

Phase-sensitive optical time domain reflectometry (-OTDR) has the advantages of fast response and high sensitivity. Therefore, it can realize fully distributed monitoring of weak vibrations along an optical fiber, which is of great value in many applications such as perimeter security and structural health monitoring. However, the optical background noise in the -OTDR will disturb the extraction of effective signals and limit the performance of this system. The optical background noise mainly includes the laser center frequency drift, the polarization-relevance noise and the distortion measurement due to the nonlinear relationship between optical fiber strain and interference intensity. In this paper, the generating mechanism of these optical background noise was analyzed and the corresponding noise suppression methods were proposed. The experiment results showed that the proposed methods could suppress the optical background noise effectively and improve the sensing performance significantly.

An Intelligent and Secure Health Monitoring Scheme Using IoT Sensor Based on Cloud Computing

Internet of Things (IoT) is the network of physical objects where information and communication technology connect multiple embedded devices to the Internet for collecting and exchanging data. An important advancement is the ability to connect such devices to large resource pools such as cloud. The integration of embedded devices and cloud servers offers wide applicability of IoT to many areas of our life. With the aging population increasing every day, embedded devices with cloud server can provide the elderly with more flexible service without the need to visit hospitals. Despite the advantages of the sensor-cloud model, it still has various security threats. Therefore, the design and integration of security issues, like authentication and data confidentiality for ensuring the elderly’s privacy, need to be taken into consideration. In this paper, an intelligent and secure health monitoring scheme using IoT sensor based on cloud computing and cryptography is proposed. The proposed scheme achieves authentication and provides essential security requirements.

Toward energy-efficient and trustworthy eHealth monitoring system

The rapid technological convergence between Internet of Things (IoT), Wireless Body Area Networks (WBANs) and cloud computing has made e-healthcare emerge as a promising application domain, which has significant potential to improve the quality of medical care. In particular, patient-centric health monitoring plays a vital role in e-healthcare service, involving a set of important operations ranging from medical data collection and aggregation, data transmission and segregation, to data analytics. This survey paper firstly presents an architectural framework to describe the entire monitoring life cycle and highlight the essential service components. More detailed discussions are then devoted to {\em data collection} at patient side, which we argue that it serves as fundamental basis in achieving robust, efficient, and secure health monitoring. Subsequently, a profound discussion of the security threats targeting eHealth monitoring systems is presented, and the major limitations of the existing solutions are analyzed and extensively discussed. Finally, a set of design challenges is identified in order to achieve high quality and secure patient-centric monitoring schemes, along with some potential solutions.

Application of Smart Materials in SmartStructures

Smart materials are expected to be an important ingredient of third-generation structures. Candidate smart materials for structural applications include optical fiber-based sensors, Ferro-magnetic sensors, shape memory alloys and piezoelectric sensors. As sensor technologies advance, periodical evaluations of their performance should be conducted to identify the best-performing sensors available for the measurement of structural responses (e.g. displacement, velocity, acceleration, strain, and stress) and detecting structural damage (e.g. cracking, fatigue and corrosion). Such evaluations should consider their performance (e.g. reliability, sensitivity, integrity, and robustness) not only as stand-alone sensors but more importantly when externally attached to structural members as well as internally embedded in concrete and FRP materials. In construction, smart materials and systems could be used in „smart‟ buildings, for environmental control, security and structural health monitoring e.g. strain measurement in bridges using embedded fiber optic sensors. Magnetorheological fluids have been used to damp cable-stayed bridges and reduce the effects of earthquakes. In marine and rail transport, possibilities include strain monitoring using embedded fiber optic sensors. The paper discuss about types of smart materials, smart sensing Technology, components of smart structures, various sensors i.e. Fiber optic sensor, smart concrete, smart structure for seismic protection, health monitoring of smart structure.

WSN Performance Issues and Various Clustering Methods

There are several application have been developed with need of self organization for network. To fulfill this requirement need of wireless sensor network in such applications. To manage network efficiently clustering is used. Lots of works have been done in field of wireless sensor networks (WSNs) in last few years. These researches have boost potential of WSNs in applications such as security monitoring, disaster management, military area, border protection and health monitoring systems. Such applications are required to be remotely deployed sensor nodes in huge numbers and to operate autonomously. So there need to scalability, nodes are often collected into disjoint clusters. This paper, presents a categorization and common organization of available clustering proposal. This work analysis various clustering algorithms used for WSNs and give a review with focusing on their objectives features, etc. and proposed efficient clustering method for stable cluster formation and maintenance.

Viewpoint on “Dissolvable fluidic time delays for programming multi-step assays in instrument-free paper diagnostics”

Medical diagnostics must function in a wide range of environments: from urban hospitals in developed countries (where cost and simplicity are presently not crucial objectives) to remote settings in developing economies, where little is available in terms of infrastructure (and where low cost and great simplicity are everything). The ‘‘developing economy’’ challenge is one that is now being embraced by the microfluidics community: exactly how does one provide biomedical information when there is no electricity, refrigeration, clean water, or trained medical personnel? Although the challenges of inexpensive, self-contained medical diagnostics are often phrased in terms of medical needs encountered in, say, the rural villages and large urban slums of Africa, practical solutions to these challenges would also be widely useful in many other circumstances: examples of other types of applications that share at least some technical requirements include medical diagnosis in military field operations, detection of diseases of animals and plants, verification of food and water safety, environmental monitoring, analyses for homeland security, and chronic and point-ofcare health monitoring. In each area, specific applications require some combination of simplicity, robustness, and low cost. New methods applicable to one have the potential to be applicable to others. Making diagnostics reliable without the infrastructure usually available in a well-supported academic laboratory (where much of the LoC technology has originated) is more difficult than it seems. The characteristics that must be built into a successful system make a long list, and sometimes seem incompatible; for example: how can unskilled personnel accurately measure the intensity of color in a colorimetric assay? How can one use the reliable, workhorse reagents (e.g., common enzymes and antibodies) of bioanalysis if they are unstable in the environments in which they must be used? Because the list is so long, there will probably be no single, best solution, but rather ingenious solutions for each problem that will then be combined in packages tailored to specific applications.

A comparative analysis of video codecs for multihop wireless video sensor networks

Wireless video sensor networks (WVSNs) have drawn significant attention in recent years due to the advent of low-cost miniaturized cameras, which makes it feasible to realize large-scale WVSNs for a variety of applications including security surveillance, environmental tracking, and health monitoring. However, the conventional video coding paradigms are not suitable for WVSNs due to resource constraints such as limited computation power, battery energy, and network bandwidth. In this paper, we evaluated and analyzed the performance of video codecs based on emerging video coding paradigms such as distributed video coding and distributed compressive video sensing for multihop WVSNs. The main objective of this work was to provide an insight into the computational (encoding/decoding) complexity, energy consumption, node and network lifetime, processing and memory requirements, and the quality of reconstruction of these video codecs. Based on the findings, this paper also provides some guidelines for the selection of appropriate video codecs for a given WVSN application.

Secure Remote Health Monitoring with Unreliable Mobile Devices

As the nation's healthcare information infrastructure continues to evolve, new technologies promise to provide readily accessible health information that can help people address personal and community health concerns. In particular, wearable and implantable medical sensors and portable computing devices present many opportunities for providing timely health information to health providers, public health professionals, and consumers. Concerns about privacy and information quality, however, may impede the development and deployment of these technologies for remote health monitoring. Patients may fail to apply sensors correctly, device can be stolen or compromised (exposing the medical data therein to a malicious party), low-cost sensors controlled by a capable attacker might generate falsified data, and sensor data sent to the server can be captured in the air by an eavesdropper; there are many opportunities for sensitive health data to be lost, forged, or exposed. In this paper, we design a framework for secure remote health-monitoring systems; we build a realistic risk model for sensor-data quality and propose a new health-monitoring architecture that is secure despite the weaknesses of common personal devices. For evaluation, we plan to implement a proof of concept for secure health monitoring.

Pervasive Health Care Services and Technologies

Pervasive Health Care Services and Technologies