In recent years, smart factory in the context of Industry 4.0 and industrial Internet of Things (IIoT) has become a hot topic for both academia and industry. In IIoT system, there is an increasing requirement for exchange of data with different delay flows among different smart devices. However, there are few studies on this topic. To overcome the limitations of traditional methods and address the problem, we seriously consider the incorporation of global centralized software defined network (SDN) and edge computing (EC) in IIoT with EC. We propose the adaptive transmission architecture with SDN and EC for IIoT. Then, according to data streams with different latency constrains, the requirements can be divided into two groups: 1) ordinary and 2) emergent stream. In the low-deadline situation, a coarse-grained transmission path algorithm provided by finding all paths that meet the time constrains in hierarchical Internet of Things (IoT). After that, by employing the path difference degree (PDD), an optimum routing path is selected considering the aggregation of time deadline, traffic load balances, and energy consumption. In the high-deadline situation, if the coarse-grained strategy is beyond the situation, a fine-grained scheme is adopted to establish an effective transmission path by an adaptive power method for getting low latency. Finally, the performance of proposed strategy is evaluated by simulation. The results demonstrate that the proposed scheme outperforms the related methods in terms of average time delay, goodput, throughput, PDD, and download time. Thus, the proposed method provides better solution for IIoT data transmission.

Adaptive Transmission Optimization in SDN-Based Industrial Internet of Things With Edge Computing

Personal Health Records (PHRs) should remain the lifelong property of patients, who should be able to show them conveniently and securely to selected caregivers and institutions. In this paper, we present My PHR Machines, a cloud-based PHR system taking a radically new architectural solution to health record portability. In My PHR Machines, health-related data and the application software to view and/or analyze it are separately deployed in the PHR system. After uploading their medical data to My PHR Machines, patients can access them again from remote virtual machines that contain the right software to visualize and analyze them without any need for conversion. Patients can share their remote virtual machine session with selected caregivers, who will need only a Web browser to access the pre-loaded fragments of their lifelong PHR. We discuss a prototype of My PHR Machines applied to two use cases, i.e., radiology image sharing and personalized medicine.

Scalable and Secure Sharing of Personal Health Records in Cloud Computing using Attribute–Based Encryption

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges

Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker-based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker-based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi-square max conditional priority features approach. In the later step, selected features are fused using a serial-based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.

Advancement and emergence of newer technologies such as analytics, artificial intelligence, machine learning have impacted many sectors such as health care, automotive etc. In the healthcare sector, these technologies resulted in various benefits such as clinical decision support, better care coordination, improving patient wellness etc. The World over, Coronary Heart Disease (CHD) is affecting millions of people. Various machine learning techniques include ensemble classifiers can be used in healthcare for improving prediction accuracy. This paper analyses various ensemble methods (Bagged Tree, Random Forest, and AdaBoost) along with Feature subset selection method — Particle Swarm Optimization (PSO), to accurately predict the occurrence of heart disease for a particular patient. Experimental results show that Bagged Tree and PSO achieved the highest accuracy.

Prediction of heart disease using ensemble learning and Particle Swarm Optimization

Data is generated by the medical industry. Often this data is of very complex nature-electronic records, handwritten scripts, etc.-since it is generated from multiple sources. Due to the Complexity...

Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection

Connecting physical objects-devices, vehicles, buildings and other items to the internet with the help of embedded electronics, softwares, sensors and network operations in order to collect and analyze data and for data exchange among these devices is called as IOT. In general connecting all the devices to the internet and trying to operate them remotely. In this paper a review on the data collection in IOT and decision making principles are presented.

Industrial WSN using IoT: A survey

There are many governmental, cultural, commercial and educational organizations that manage large number of manuscript textual information. Kannada being one of the official languages of South India, such organizations include Kannada handwritten documents. Text line segmentation in such documents remains an open document analysis problem. Detection and correction of skew angle of the segmented text lines become another important step in document analysis. Most of the segmentation algorithms, for skewed text lines, present in the literature today are sensitive to the degree of skew, direction of skew, and spacing between adjacent lines. In this paper, proposed method for the text line extraction and skew correction of the extracted text lines uses a new cost function, which considers the spacing between text lines and the skew of each text line is used. Precisely, the problem is formulated as an energy minimization problem so that the minimization of the cost function yields a set of text lines. Further it is required to efficiently correct baseline skew and fluctuations of these text lines. This proposed method also uses an efficient algorithm for baseline correction. It consists of normalizing the lower baseline to a horizontal line using a skating window approaches, thus, avoiding the segmentation of text lines into subparts. This approach copes with baselines which are skewed, fluctuating, or both. It differs from machine learning approaches which need manual pixel assignments to baselines. Experimental results show that this baseline correction approach highly improves performance.

http://ieeexplore.ieee.org/iel7/6767436/6779283/06779295.pdf

Kannada text line extraction based on energy minimization and skew correction

MRI is one of the tool for detecting the tumor in any part of the body. But precise tumor segmentation from such Magnetic resonance imaging (MRI) is difficult and also time consuming technique. To overcome such difficulty, this work proposes a very simple, efficient and automatic segmentation and classification of brain tumor. The proposed system is composed of four stages to segment, detect and classified tumor as benign and malignant. Pre-processing is carried out in the first stage after which watershed segmentation technique is applied for segmenting the image which is the second stage. The segmented image undergo for post processing to remove the unwanted segmented image so as to obtain only the tumor image. In the last stage, gray-level co-occurrence matrix (GLCM) is used to extract the feature. This feature is given as input to Support Vector Machine (SVM) to classify the brain tumor. Results and experiment shows that the proposed method accurately segments and classified the brain tumor in MR images.

Sunanda Dixit

Papers

Prediction of heart disease using ensemble learning and Particle Swarm Optimization

Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection

Industrial WSN using IoT: A survey

Kannada text line extraction based on energy minimization and skew correction

Gradient Magnitude Based Watershed Segmentation for Brain Tumor Segmentation and Classification