Why Software-Defined Radio (SDR) Matters in Healthcare?

Background: Wireless Body Area Networks (WBANs) have been drawing noteworthy academic and industrial attention. A WBAN states a network dedicated to acquire personal biomedical data via cutting-edge sensors and to transmit healthcare-related commands to particular types of actuators intended for health purposes. Still, different proprietary designs exist, which may lead to biased assessments. This paper studies the role of Software-Defined Radio (SDR) in a WBAN system for inpatient and outpatient monitoring and explains to health professionals the importance of the SDR within WBANs. Methods: A concern related to all wireless networks is their dependence on hardware, which limits reprogramming or reconfiguration alternatives. If an error happens in the equipment, firmware, or software, then, typically, there will be no way to fix system vulnerabilities. SDR solves many fixed-hardware problems with other benefits. Results: SDR entails more healthcare domain dynamics with more network convergence in agreement with the stakeholders involved. Then the SDR perspective can bring in innovation to the healthcare subsystems’ interoperability with recombination/reprogramming of their parts, updating, and malleability. Conclusion: SDR technology has many utilizations in radio environments and is becoming progressively more widespread among all kinds of users. Nowadays, there are many frameworks to manipulate radio signals only with a computer and an inexpensive SDR arrangement. Moreover, providing a very cheap radio receiver/transmitter equipment, SDR devices can be merged with free software to simplify the spectrum analyses, provide interferences detection, deliver efficient frequency distribution assignments, test repeaters' operation while measuring their parameters, identify spectrum intruders and characterize noise according to frequency bands

QoS in Body Area Networks: A survey

Body Area Networks (BANs) are becoming increasingly popular and have shown great potential in real-time monitoring of the human body. With the promise of being cost-effective and unobtrusive and facilitating continuous monitoring, BANs have attracted a wide range of monitoring applications, including medical and healthcare, sports, and rehabilitation systems. Most of these applications are real time and life critical and require a strict guarantee of Quality of Service (QoS) in terms of timeliness, reliability, and so on. Recently, there has been a number of proposals describing diverse approaches or frameworks to achieve QoS in BANs (i.e., for different layers or tiers and different protocols). This survey put these individual efforts into perspective and presents a more holistic view of the area. In this regard, this article identifies a set of QoS requirements for BAN applications and shows how these requirements are linked in a three-tier BAN system and presents a comprehensive review of the existing proposals against those requirements. In addition, open research issues, challenges, and future research directions in achieving these QoS in BANs are highlighted.</jats:p

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Deep Reinforcement Learning in Health care systems

In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. We classify, and compare the advantages and disadvantages of various routing proto-cols. We also address Emergency health issues and suggest how it can be improved

Era of Deep Learning in Wireless Networking

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Wireless Communications and Mobile Computing using Machine learning

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not re-place, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Achieving Longevity in Wireless Body Area Network by Efficient Transmission Power Control for IoMT Applications

The application of tiny body sensors to collect, process, store, analyze, and retrieve medical information from a human body is a part of the Internet of Medical Things (IoMT).&nbsp; IoMT helps to monitor and track human vital health parameters, predict disease, notify the patients and the health care professionals with relevant data for analyzing the problems before they become severe and for earlier invention. By 2022, more than 60 % of IoT applications will be health-related. The convergence of biomedical sensors, wireless body area networks (WBAN), Information technology, and bioinformatics will help improve the efficiency of saving human lives. In a WBAN, network longevity is challenging because of the limited supply of low power battery energy in tiny body sensor nodes. Here, we proposed an energy-efficient transmission power control (TPC) algorithm to extend the network lifetime in IoMT networks for healthcare applications by eliminating the transceiver overhearing problem. In TPC, human tissue resistivity properties are considered to adjust the transmission power, which reduces the communication power and extends the network lifetime. The simulation results show that network power consumption is reduced by 35%

Artificial iIntelligence for Big Data: issues and challenges

Artificial intelligence (AI) concerns the study and development of intelligent ma-chines and software. The associated ICT research is highly technical and specialized, and its focal problems include the developments of software that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely la-bour intensive and time consuming. Thus, unleashing AI on big data can have a sig-nificant impact on the role data plays in deciding how we work, how we travel and how we conduct business. This paper explores how Artificial Intelligence, in conjunc-tion with Big Data technologies, can help organizations to bring about operational and business transformation.Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches