Wireless sensor network can be used to construct a telemedicine scheme to bring together the patient data and expansion of medical conveniences when disaster occurs. The Remote Medical Monitoring (RMM) scheme of the disaster period can be constructed using the Health care center (CC), Wireless sensor nodes and a few Primary health care centers (PHC). The sensor nodes possess the capacity of making communication between patients and PHCs. This type of WSN experiences limited lifetime problem due to the limited battery energy and transmission of medical data in large quantity. This paper proposes a new and novel WSN based Disaster Rescue Telemedicine Scheme to minimize energy consumption and to maximize network lifetime. The proposed method reaches this milestone using three novel algorithms namely ‘Network clustering using Non-border CH oriented Genetic algorithm, Fuzzy rules and Kernel FCM (NCNBGF)’, ‘High gain MDC algorithm (HGMDC)’ and ‘Critical node handling using job limiting and job shifting (CJLS)’. The principal technologies used in this paper are Network node clustering, Medical image compression and Critical state node energy management to elongate the life period of WSN. The Simulation results prove that the proposed method amplifies the WSN topology lifetime to a significant level than the earlier versions. The Existing methods compared in this paper holds only 20% energy at the round 80,the proposed method stays with 43% of energy.

Soft fuzzy computing to medical image compression in wireless sensor network-based tele medicine system

This study evaluated the application of a wireless sensor network (WSN) on a web-based vital signs monitoring system to nursing homes in Taiwan. The applicability assessment focused on the timely provision of information, information accuracy, system usability, and system accessibility of healthcare systems using a wireless sensor network. Experiments were performed under Internet-based network conditions to verify the timely information provision, especially for a web-based system, including Ajax technology. The accuracy of the information was verified from statistical analyses of the residents’ daily vital sign measurements. A comparison was performed between having and not having a healthcare monitoring system in nursing homes for system usability, system accessibility, and system efficacy. The results indicate that the successful application of a WSN healthcare monitoring system is feasible for use in nursing homes in Taiwan.

Wireless Sensor Networks for Vital Signs Monitoring: Application in a Nursing Home

With the emergence of wireless sensor networks and its usage in sensitive monitoring and tracking applications, the need of ensuring complete security is gaining more importance than ever before. Complete security can only be ensured by adding privacy, cryptographic-based security and trust management aspects in a security solution. However, integration of all these three aspects in a single solution for resource constraints wireless sensor networks is not trivial. Current research intensively focuses on all these three aspects in an isolated manner. To the best of our knowledge, we have not found any work in the literature that comprehensively discusses: how these various privacy, security and trust solutions work together? In this work, we have made the first step towards this direction and to show how integration of various privacy, security and trust solutions can be performed in a single solution in step-by-step manner.

Security Completeness Problem in Wireless Sensor Networks

An innovative practice of green information systems was implemented to increase the efficiency of energy usage and reduce the energy consumption in plant factories according to the concept of energy informatics. A plant factory is the extreme development of the greenhouse culturing technology. Plants are produced in a similar manner as the mass production in industries. The plants grow in a controlled environment, including light, humidity, biogas, nutrition, and culturing liquid. In addition, the plants are isolated from the outside environment, and climatic conditions during the growing period do not affect the quality and quantity of the plants, raising public food safety. However, the energy consumption of plant factories is a primary concern. The objectives of this study are to increase the efficiency of energy usage and reduce the energy consumption in plant factories. The use of sustainable energy, particularly the solar-driven ejector refrigeration system, to provide cooling capacity can reduce the water temperature in hydroponic beds from 33 °C to 25 °C. Computational fluid dynamics (CFD) simulation is also applied to the indoor climate data collected by Internet of Things sensors in this study to provide the temperature and flow fields of plant factories.

A study on energy strategy of a plant factory using sustainable energy combined with computational fluid dynamics simulation: An innovative practice of green information systems

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.

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Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

Telemedicine aided by wireless sensor networks (WSN) has recently become a healthcare trend. Many previous studies have adopted the ZigBee-based WSN to implement a platform and/or a telemedicine system. However, the low data rate and bandwidth have limited the maximum number of nodes in a WSN during continuous and simultaneous transmission. The issue of low data throughput has not been addressed in previous reseazch. In this study, we propose an arrhythmia-aware system, and a new DSPbased WSN platform is developed for the high compression performance of physiological data in a ZigBee-based WSN. Proven by simulations and several real tests, the combination of the proposed platform and the sensor nodes can lead to more bandwidth conservation and extend the WSN scale for the next generation of telemedicine system.

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Show Hong Chen

Papers

Conserving bandwidth in a wireless sensor network for telemedicine application