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Nowadays, the Internet of Things (IoT) is reshaping numerous application domains. Amidst the different communication protocols currently available (MQTT, CoAP, AMQP, DPWS, etc.) and the abundance of management platforms, the IoT domain has stumbled into vertical silos of proprietary systems hindering interoperability. Within this intricate and fragmented ecosystem, the need for an intelligent and scalable architecture promoting interoperability becomes imperative to maximize the potential of IoT. This paper introduces a system designed to enhance the convergence of IoT protocols (e.g., MQTT, CoAP, AMQP, etc.) with the Web of Things (WoT) paradigm. Our proposed gateway-based solution integrates two systems: Stack4Things (S4T) and Data eXchange Mediator Synthesizer (DeXMS). The role of the latter is to adapt, at the gateway level, the IoT protocols and expose the resources/functionalities of the IoT devices as RESTful APIs using HTTP. Meanwhile, the former (i.e., S4T), leveraging its Dynamic DNS system, ensures that these RESTful resources (exposed at the gateway) are accessible over the Web using publicly routable Uniform Resource Locators (URLs) even when the gateway is deployed behind networking middle boxes (e.g., NATs and firewalls).

This paper introduces an efficient and scalable cloud-based privacy preserving model using a new optimal cryptography scheme for anomaly detection in large-scale sensor data. Our proposed privacy preserving model has maintained a better tradeoff between reliability and scalability of the cloud computing resources by means of detecting anomalies from the encrypted data. Conventional data analysis methods have used complex and large numerical computations for the anomaly detection. Also, a single key used by the symmetric key cryptographic scheme to encrypt and decrypt the data has faced large computational complexity because the multiple users can access the original data simultaneously using this single shared secret key. Hence, a classical public key encryption technique called RSA is adopted to perform encryption and decryption of secure data using different key pairs. Furthermore, the random generation of public keys in RSA is controlled in the proposed cloud-based privacy preserving model through optimizing a public key using a new hybrid local pollination-based grey wolf optimizer (LPGWO) algorithm. For ease of convenience, a single private server handling the organization data within a collaborative public cloud data center when requiring the decryption of secure sensor data are allowed to decrypt the optimal secure data using LPGWO-based RSA optimal cryptographic scheme. The data encrypted using an optimal cryptographic scheme are then encouraged to perform data clustering computations in collaborative public servers of cloud platform using Neutrosophic c-Means Clustering (NCM) algorithm. Hence, this NCM algorithm mainly focuses for data partitioning and classification of anomalies. Experimental validation was conducted using four datasets obtained from Intel laboratory having publicly available sensor data. The experimental outcomes have proved the efficiency of the proposed framework in providing data privacy with high anomaly detection accuracy.

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques.

The future holds the possibility of hospitals sharing medical images obtained through non-invasive systems to patients remotely. The advent of cloud and the storage and deployment of medical healthcare images in the cloud has resulted in the increased need for application of Cryptographic techniques to protect them from unauthorized access and malicious attacks. The Digital Imaging and Communication in Medicine (DICOM) standard is more compatible across medical imaging instruments globally. The pixel data of DICOM images requires more privacy and security. A novel ECDS based cryptographic approach is suggested to encrypt the original DICOM image as well as the ROI pixel data extracted from DICOM images. Results computed experimentally have proved that medical image encryption via ECDH is more robust, efficient and faster than existing medical image encryption schemes.

Amplifying Spatial Awareness via GIS — Tech which brings Healthcare Management, Preventative & Predictive Measures under the same Cloud

When it is not just about size, you gotta' be Smart, too!

Chew on It! How Singapore-based health informatics company MHC Asia Group crunches big-data to uncover your company's health

Digital tool when well-used, it is Passion

Carving the Digital Route to Wellness

Big Data, Bigger Disease Management and Current preparations to manage the Future Health of Singaporeans

A Conversation with Mr Arun Puri

Extreme Networks: Health Solutions

Big Data in Clinical Research Sector

SINGAPORE – A*STAR Scientists Reveal How Stem Cells Defend Against Viruses: New Insights to the Mechanisms bring Broad Implications to Stem Cell Therapy and Disease Diagnosis

SINGAPORE – Singtel - Singapore Cancer Society Race against Cancer 2015

SINGAPORE – NCCS and IMCB to Collaborate on Research for New Treatments to Benefit Cancer Patients

SINGAPORE – MerLion’s Finafloxacin Shown to be More Efficacious than Ciprofloxacin in the Treatment of Complicated Urinary Tract Infections

TAIWAN AND UNITED STATES – Professor Yuk-ling Yung Receives Gerard P. Kuiper Prize

SWEDEN – New Data Confirm Tresiba^® U200 Delivers Significantly Lower Rates of Confirmed Hypoglycaemia versus Insulin Glargine U100

UNITED KINGDOM – Using Ultrasound to Clean Medical Instruments

THE NETHERLANDS & UNITED STATES – Philips and Dutch Radboud University Medical Centre Introduce First Diabetes Prototype App with Integrated Online Community to Empower Patients and Enhance Continuity of Care

UNITED STATES – New Clinical Architecture Content Cloud Establishes Reliable Single Source for Terminology Updates, Makes It Easier to Keep Healthcare Systems Up-to-Date

UNITED STATES – Genomic Analysis for All Cancer Patients

UNITED STATES – Birds That Eat at Feeders Are More Likely to Get Sick, Spread Disease, International Research Team Says

From Home to Hospital: Digitisation of Healthcare.

Microsoft with RingMD, Oneview Healthcare, Vital Images, Aruba, and Clinic to Cloud: The Ecosystem of Healthcare Solutions Providers in Asia.

Data Helps in Improving Nursing Practice, Making Better Decisions.

Launch of Asian Branch for QuintilesIMS Institute.

In recent years, there have been many studies utilizing DNA methylome data to answer fundamental biological questions. Bisulfite sequencing (BS-seq) has enabled measurement of a genome-wide absolute level of DNA methylation at single-nucleotide resolution. However, due to the ambiguity introduced by bisulfite-treatment, the aligning process especially in large-scale epigenetic research is still considered a huge burden. We present Cloud-BS, an efficient BS-seq aligner designed for parallel execution on a distributed environment. Utilizing Apache Hadoop framework, Cloud-BS splits sequencing reads into multiple blocks and transfers them to distributed nodes. By designing each aligning procedure into separate map and reducing tasks while an internal key-value structure is optimized based on the MapReduce programming model, the algorithm significantly improves alignment performance without sacrificing mapping accuracy. In addition, Cloud-BS minimizes the innate burden of configuring a distributed environment by providing a pre-configured cloud image. Cloud-BS shows significantly improved bisulfite alignment performance compared to other existing BS-seq aligners. We believe our algorithm facilitates large-scale methylome data analysis. The algorithm is freely available at https://paryoja.github.io/Cloud-BS/.

Intelligent electronic components’ management systems (IECMS), which can automatically inquire about components’ stock and make inventories, were often proposed in recent years to improve the efficiency of material management. So far, research focused on system architecture and information management software. The design of components’ monitoring subsystems was neglected in practical implementations of IECMS for industrial environments. The existing schemes share the disadvantages of high complexity and cost. As IECMS are not widely applied, yet, to implementations of IECMS features of hardware customization, maintainability, real-time components’ monitoring and cloudbased stock tracking are added to provide an intelligent and economic solution for advanced material management applications. Preliminary test results prove its effectiveness in intelligent components management.

Cloud computing is internet based network diagram that represent internet, or various parts of it, as schematic clouds. The term, characteristics and services associated with internet based computing is called cloud computing. Characteristics means infrastructures, provisioning, network access and managed metering. The primary business associated model which is employed in software, platform and infrastructure as a service and common deployment model which is deployed by service providers and users to use and maintain the cloud services like private, public, community and hybrid clouds are discussed in this papers. In this paper cloud computing refers to different types of services and applications being delivered in the internet cloud but the fact is, in many cases the devices use to access such services and applications do not require any special applications. That is cloud computing is everywhere. Cloud computing also promise to cut operational and capital costs.