Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as "The Internet of Things". WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network lifetime , but also affects the availability of these resources for other tasks. To increase lifetime and provide an energy-efficient WSN, here we have proposed a new scheme called "A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs". CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network lifetime by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

The digital transformations and use of healthcare information system, electronic medical records, wearable technology, and smart devices are increasing with the passage of time. A variety of sources of big data in healthcare are available, such as biometric data, registration data, electronic health record, medical imaging, patient reported data, biomarker data, clinical data, and administrative data. Visualization of data is a key tool for producing images, diagrams, or animations to convey messages from the viewed insight. The role of cardiology in healthcare is obvious for living and life. The function of heart is the control of blood supply to the entire parts of the body. Recent speedy growth in healthcare and the development of computation in the field of cardiology enable researchers and practitioners to mine and visualize new insights from patient data. The role of visualization is to capture the important information from the data and to visualize it for the easiness of doctors and practitioners. To help the doctors and practitioners, the proposed study presents a detailed report of the existing literature on visualization of data in the field of cardiology. This report will support the doctors and practitioners in decision-making process and to make it easier. This detailed study will eventually summarize the results of the existing literature published related to visualization of data in the cardiology. This research uses the systematic literature protocol and the data was collected from the studies published during the year 2009 to 2018 (10 years). The proposed study selected 53 primary studies from different repositories according to the defined exclusion, inclusion, and quality criteria. The proposed study focused mainly on the research work been done on visualization of big data in the field of cardiology, presented a summary of the techniques used for visualization of data in cardiology, and highlight the benefits of visualizations in cardiology. The current research summarizes and organizes the available literature in the form of published materials related to big data visualization in cardiology. The proposed research will help the researchers to view the available research studies on the subject of medical big data in cardiology and then can ultimately be used as evidence in future research. The results of the proposed research show that there is an increase in articles published yearly wise and several studies exist related to medical big data in cardiology. The derivations from the studies are presented in the paper.

The Internet of Things (IoT) is a new paradigm that connects objects to provide seamless communication and contextual information to anyone, anywhere, at any time (AAA). These Internet-of-Things-enabled automated objects interact with visitors to present a variety of information during museum navigation and exploration. In this article, a smart navigation and information system (SNIS) prototype for museum navigation and exploration is developed, which delivers an interactive and more exciting museum exploration experience based on the visitor’s personal presence. The objects inside a museum share the information that assist and navigate the visitors about the different sections and objects of the museum. The system was deployed inside Chakdara Museum and experimented with 381 users to achieve the results. For results, different users marked the proposed system in terms of parameters such as interesting, reality, ease of use, satisfaction, usefulness, and user friendly. Of these 381 users, 201 marked the system as most interesting, 138 marked most realistic, 121 marked it as easy-in-use, 219 marked it useful, and 210 marked it as user friendly. These statistics prove the efficiency of SNIS and its usefulness in smart cultural heritage, including smart museums, exhibitions and cultural sites.

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