The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

Optimum design of hybrid renewable energy systems: Overview of different approaches

As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.

Vehicular Edge Computing and Networking: A Survey

Methodological review: Computerized analysis of pigmented skin lesions: A review

pdf/vehicular-edge-computing-and-networking-a-survey-3lxe6z04l0.pdf

Vehicular Edge Computing and Networking: A Survey.

Recently, internet of vehicles (IoVs) is captivating a lot of interest due to a wide range of applications in various domains. These applications rely on up-to-date information of vehicles for provisioning various services. However, frequent message transmissions by a sheer number of vehicles may not only engulf a centralized server but may also cause a severe congestion which is not suitable for ongoing services specially in emergency situations. The aim of this study is to reduce congestion and messaging overhead. This paper presents a fog-assisted congestion avoidance scheme for IoV named energy efficient message dissemination (E2MD). Unlike most of the existing schemes, E2MD capitalizes the merits of fog computing to minimize communication cost and manage services. Each vehicle is required to update their status to a fog server frequently, either directly or through intermediate nodes. In case of an emergency, the fog server will timely inform upcoming traffic to slow down, dispatch rescue teams to provide necessary services, and coordinate patrolling missions to clear the road. Moreover, failure detection and recovery mechanisms are also presented to ensure availability of the fog server. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance reign of E2MD compared to contemporary schemes in terms of latency and communication cost.

Congestion avoidance through fog computing in internet of vehicles

Air borne sensed data is gained by satellite/aerial images and other remote sensing or scanning platforms. Each grid-cell, or pixel, has a certain value depending on how the image was captured and what it represents. Reprojection and resampling of raster image data should be handled with caution. Issues of geometric distortions and errors due to resampling need to be considered carefully. Interpretation of such data requires correction of distortions and resampling of recorded data. There are different methods for this purpose but they have their inbuilt problems as well. This paper presents a bilinear interpolation method for resampling of air borne sensed data. The method is developed in Matlab and results are obtained using different interpolation rates. A review of the used method is also given at the end.

Resampling air borne sensed data using bilinear interpolation algorithm

Data in medical images is very large and therefore for storage and/or transmission of these images, compression is essential. A method is proposed which provides high compression ratios for radiographic images with no loss of diagnostic quality. In the approach an image is first compressed at a high compression ratio but with loss, and the error image is then compressed losslessly. The resulting compression is not only strictly lossless, but also expected to yield a high compression ratio, especially if the lossy compression technique is good. A neural network vector quantizer (NNVQ) is used as a lossy compressor, while for lossless compression Huffman coding is used. Quality of images is evaluated by comparing with standard compression techniques available

Absolutely Lossless Compression Of Medical Images

Recently, Internet of Vehicles (IoVs) is getting growing interest because of their suitability for a wide range of emerging applications. Most of these applications require vehicles to continuously update their information to a centralized location in order to gain various services. However, frequent transmission of messages by an abundance number of vehicles may not only overwhelm a centralized server but also causes a huge congestion which might disrupt various services including emergency situations. The aim of this research is to minimize congestion and messaging delay. This paper presents a fog-assisted congestion avoidance scheme for IoV named Energy Efficient Message Dissemination (E2MD). To capitalize the merits of fog computing and minimize latency, E2MD opts a distributed approach by employing a fog server to complement services in IoVs. In E2MD, vehicles continuously update their status to a fog server either directly or through intermediate nodes. The performance of the proposed scheme is validated through NS 2.35 simulations. Simulation results confirm the performance supremacy of E2MD compared to contemporary schemes in terms of end-to-end delay and messaging cost.

Fog-assisted Congestion Avoidance Scheme for Internet of Vehicles

A method is proposed for compression of medical images. The approach is called DLC2 (Diagnostically Lossless Compression-2). To get lossless effect, we use lossy coding followed by error image coding. An NNVQ (Neural Network Vector Quantizer) is used for lossy compression and Huffman coding is used to code the difference image losslessly. It is an spatial domain technique. No frequency domain transformations are required, this makes the proposed scheme simple and computationally economical. Proposed method can achieve higher compression ratios than existing standard lossless compression techniques and also meets the legal requirement of medical image archiving

Diagnostically Lossless Compression-2 of Medical Images

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